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Mova Z70 Ultra Roller im Test: Saugroboter kämmt Wischwalze & zieht den Boden ab

Sun, 26 Jul 2026 16:15:09 +0200

Der Mova Z70 Ultra Roller fährt fast lautlos und wischt mit einer selbstauflockernden Walze. Ob der edle Top-Saugroboter überzeugt, zeigt der Test.

Warum das klassische Kreativbriefing nicht mehr ausreicht

Sun, 26 Jul 2026 07:30:21 +0200

Das Kreativbriefing ist fast schon eine Kunstform für sich. Lange war es das zentrale Werkzeug, um Strategie, Gestaltung und Tonalität miteinander zu verbinden. Doch während sich die Art, wie wir arbeiten, grundlegend verändert, verliert das klassische Briefing zunehmend an Bedeutung.weiterlesen auf t3n.de

9 Anzeichen, dass Sie kurz vorm Burnout stehen

Sun, 26 Jul 2026 06:33:30 +0200

Trotz Überlastung immer funktionieren zu wollen, macht krank.

Mangostar – shutterstock.com

Programmierer, so lautet ein populäres Bonmot, sind Maschinen, die Koffein in Code verwandeln. Das trifft auch auf viele andere Freiberufler zu. Der hohe Koffeinbedarf hängt damit zusammen, dass Freelancer allzu oft genau dann arbeiten, wenn ihr Gehirn sich lieber auf Stand-by schalten und zur Ruhe begeben möchte, nämlich nachts.

Sogar ein Buch gibt es schon, das sich mit diesem Phänomen und seinen Ursachen beschäftigt (“Why Programmers work at Night“). Gesund ist die Nachtarbeit nicht, ebenso wenig wie das ständige Zuviel an Arbeit und ein paar andere Arbeits- und Lebensgewohnheiten, die vielen Freiberuflern zu eigen sind.

Work-Life-Balance in Schieflage geraten

Nach Ansicht von Karol Krol, einem polnischen Blogger, Programmierer und Internetunternehmer, stehen viele Freelancer kurz vor dem Burnout, ohne es zu merken. Wir sagen Freiberuflern, woran sie feststellen können, dass ihre Work-Life-Balance bedrohlich in die Schieflage geraten ist.

1. Sie arbeiten oft bis spät in die Nacht

Menschen sind keine Eulen und keine Fledermäuse, sondern biologisch eindeutig tagaktive Tiere. Sie sehen gut am Tag und schlecht in der Nacht. Sich einzureden, man sei nachts am produktivsten oder könne nachts “einfach am besten arbeiten”, ist in aller Regel Selbstbetrug. Wer nachts kein Ende findet, hat entweder insgesamt zu viel Arbeit oder schafft es nicht, sich tagsüber Ablenkungen zu entziehen.

2. Sie kommen morgens nicht in Gang

Natürlich: Kalt duschen, ein schneller Kaffee und 20 Minuten nach dem Weckerklingeln am Schreibtisch sitzen – das schaffen die wenigsten. Aber wer auch zwei oder drei Stunden nach dem Aufstehen nicht in der Lage ist, die ersten Dinge auf der To-do-Liste anzugehen, hat ein Problem. Ein Grund kann – natürlich – chronische Müdigkeit sein, ein anderer die Tatsache, dass Sie Ihren Arbeitstag nicht als begrenztes, achtstündiges Gebilde betrachten. Sie sind eigentlich immer im Arbeitsmodus – und haben deshalb auch nie Freizeit.

3. Sie haben keine Zeit für Entspannung

Nie abzuschalten, ist hochgradig gesundheitsgefährdend. Gerade Menschen, die grundsätzlich viel leisten können und wollen, brauchten unbedingt Erholungsphasen, damit ihre Kraft erhalten bleibt. Dabei genügt es nicht, auf dem Sofa zu liegen und über den nächsten Job nachzudenken. Denn auch das Gehirn braucht Entspannung. Wer es ihm nie gönnt, ist allein schon deshalb ein Burnout-Kandidat.

4. Ihre Standardantwort ist: Keine Zeit!

Wie oft haben Sie zuletzt gesagt: “Ich kann nicht, bin gerade im Stress!”, wenn ein Freund mit Ihnen ein Bier trinken gehen wollte? Wenn Sie seit drei Wochen oder mehr außer Arbeiten nichts gemacht haben, dann stimmt etwas nicht. Ein Leben, das ausschließlich aus Arbeit besteht, kann nicht Ihr Ziel sein.

5. Jobs werden nicht pünktlich fertig

Das kann natürlich ganz unterschiedliche Gründe haben: insgesamt zu viel Arbeit, schlechte Organisation, schlechtes Briefing durch den Auftraggeber etc.
Der häufigste und gefährlichste Grund hängt allerdings eng mit Punkt zwei dieser Liste zusammen: Dadurch, dass sie immer im Arbeitsmodus sind und ihr Arbeitstag gefühlt 24 Stunden hat, gaukelt das Unterbewusstsein Ihnen vor, Sie hätten für alles unendlich viel Zeit. Also gibt es auch keinen Grund, sofort mit irgendwas anzufangen.

6. Keine Zeit für eigene Projekte

Freiberufler zu sein bedeutet, Freiheiten zu haben. Zum Beispiel die, neben den Jobs für Ihre Kunden eigene Projekte anzuschieben. Doch ein solches Projekt zu starten ist eine Sache, es anschließend auch durchzuziehen, eine andere. Wenn Sie mindestens ein Projekt haben, an das Sie glauben, das aber schon seit einem halben Jahr darauf wartet, weiterverfolgt zu werden, sollten Sie sich fragen, warum Sie ursprünglich gerne Freiberufler sein wollten.

7. Sie haben keine Hobbys

Oder doch, haben Sie natürlich schon, aber Sie kommen schon ewig nicht mehr dazu. Das Klavier ist seit einem Jahr so verstimmt, dass es keinen Spaß mehr macht. Den Klavierstimmer anrufen? Keine Zeit. Siebzig Euro kostet der Fitness-Club Sie jeden Monat, aber Sie haben keine Ahnung, wann Sie zuletzt dort waren.

8. Sie lesen fast nie mehr ein Buch

Klar, auch viele Angestellte tun das nicht. Aber für fast alle fällt Lesen in die Rubrik: Dinge, die ich schon lange mal wieder tun wollte. Wer Bücher liest, beweist sich selbst, dass er zumindest gelegentlich gerne auf andere Gedanken kommen möchte. Und dass er entschlossen ist, sich auch mal zu entspannen.

9. Freundschaften schlafen ein

Mehrere Menschen, die Ihnen lieb und teuer sind, haben Sie seit Monaten nicht mehr gesprochen. Nehmen Sie sich vor, einmal pro Woche zum Hörer zu greifen und Menschen anzurufen, die ihnen wichtig sind. Oder die Ihnen mit gutem Grund einmal wichtig waren.

Stress

… und ziehen Sie Yoga und weitere Meditationsübungen in Betracht. Diese Übungen sind die besten Mittel gegen Stress und tragen dazu bei, Stressgefühle abzubauen. Ganz abgesehen vom gesundheitlichen Nutzen dienen die Trainings auch dazu, den Stress besser zu managen.

Obwohl wir natürlich seit unserer Geburt atmen, wissen die meisten von uns nicht, wie man richtig atmet. Viele atmen in einer oberflächlichen Art und Weise – besonders in stressbetonten oder unruhigen Zeiten. Tiefes Atmen durch den Bauch kann zur inneren Ruhe beitragen. Und es hilft, in unbequemen und angespannten Situationen einen kühlen Kopf zu bewahren.

Wer sich die Zeit nimmt um darüber zu sprechen, wie die vielen Veränderungen und Schwierigkeiten am Arbeitsplatz die einzelnen Mitarbeiter bewegen, kann die Arbeitsmoral heben. Es ist ein Fehler zu glauben, Menschen seien nicht verängstigt und besorgt und der Arbeitsplatz sei davon nicht betroffen.

Die Zeiten sind angespannt und schwierige Veränderungen in Organisationen sind die Regel. Daher sind Ehrlichkeit, Glaubwürdigkeit und Offenheit so wichtig. Heute ist es mehr als je zuvor entscheidend, eine positive Einstellung in der Belegschaft auszulösen. Stellen Sie Fragen, die zu Lösungen ermuntern wie “Was läuft heute gut, was sind unsere Stärken, wie möchten wir, dass dieses Unternehmen aussieht?”

Leute arbeiten intensiver für das, woran sie glauben und was sie zur Schaffung beigetragen haben. Das ist ein entscheidender Punkt, der während einer tiefgreifenden Umgestaltung am Arbeitsplatz geprüft werden muss. Was das mögliche Ausmaß des Arbeitsplatz-Wandels betrifft, sollten Mitarbeiter frühzeitig in die Entwicklung einbezogen werden.

Bücher, Gruppen, Familie und enge Freunde sowie Trainer können wichtige Quellen sein, um sich den eigenen Gefühlen bewusster zu werden. Auch kann man dadurch leichter lernen, mit diesen Gefühlen umzugehen, um sich über sein Verhalten im Klaren zu werden. Besonders sollte man darauf achten, wie man andere Menschen anspricht.

Was man tut oder lässt, hat direkten Einfluss darauf, was Mitarbeiter glauben, was akzeptabel ist. Seien Sie ein überzeugendes Beispiel dafür, dass ein ausgeglichenes Verhältnis zwischen Beruf und Privatleben von Bedeutung ist. Essen Sie mit anderen zu Mittag und motivieren Sie Kollegen dazu mitzukommen. Auch Spaß und Lachen am Arbeitsplatz sind erwünscht, da dies Stress reduzierende Faktoren sind.

Wer sich immer nur auf das Negative konzentriert, tut weder seiner Gesundheit noch seiner Denkweise einen Gefallen. Und seien wir ehrlich: Der Anteil an positiven und erbaulichen Geschichten in den Nachrichten fällt eindeutig spärlich aus. Es ist extrem wichtig, sich so gut wie möglich von jeglichem Trübsal abzukapseln und wieder mit Leuten Kontakt aufnehmen bzw. Dinge zu tun, die Spaß machen.

Konzentrieren Sie sich auf den Kern Ihrer Arbeit. Jetzt ist Zeit, mit den Mitarbeitern Prioritäten zu setzen und sich darüber Gedanken zu machen, welche Projekte einen perfekten Lösungsansatz erfordern. Nicht jedes Projekt kann an oberster Stelle stehen. Gerade in wirtschaftlich angespannten Zeiten sind Brainstorming-Sitzungen wichtiger denn je.

Spielekonsolen: Auslieferungsmengen dürften 2026 um fast 20 % einbrechen

Sun, 26 Jul 2026 06:30:14 +0200

2026 ist generell kein gutes Jahr für Hersteller von Unterhaltungselektronik. Die Speicherkrise sorgt für steigende Komponentenpreise. Folgerichtig können die Anbieter entweder die Preise deutlich erhöhen, womit...

Zum Beitrag: Spielekonsolen: Auslieferungsmengen dürften 2026 um fast 20 % einbrechen

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Top 10: Das beste Klapprad-E-Bike im Test – Urtopia vor Engwe und Brompton

Sat, 25 Jul 2026 20:15:07 +0200

Wir zeigen die besten Klapprad-E-Bikes aus fast 40 Tests. Das Urtopia Carbon Fold 2 ist unser Testsieger.

The EU’s AI transparency deadline is weeks away. Is your enterprise ready?

Sat, 25 Jul 2026 19:50:13 +0200

Providers and deployers of AI systems: You only have a couple of weeks left until you must explicitly inform users when they are interacting with AI content.

To assist in the effort, the European Commission (Commission) has published guidelines to help AI deployers get in line with the AI Act’s transparency obligations, which will begin to go into effect on August 2.

After that, companies providing AI systems must alert users when they are interacting with AI. They must also tell users when they have been exposed to deepfakes, “emotion recognition,” or biometric categorization systems, or when they are given AI-manipulated content in matters of “public interests without human review or editorial control.”

Henna Virkkunen, the Commission’s executive VP for tech sovereignty, security and democracy, said in a statement, “with today’s guidelines, the Commission supports the smooth and effective application of the AI Act to make AI systems interacting with people such as chatbots and AI agents and AI content more transparent and trustworthy. These guidelines support providers and deployers in meeting their obligations under the AI Act, while helping citizens know when they are interacting with AI.”

Systems must include machine-readable markers to reveal such content, to reduce “the risk of deception and manipulation” and build public trust in AI.

“Generative systems have collapsed the cost of producing convincing content while the cost of judging it stands where it always stood,” said Sanchit Vir Gogia, chief analyst at Greyhound Research. This requirement is “an attempt to restore friction to that imbalance.”

A company’s non-compliance could result in fines anywhere from €750K (about $856K) to €15M (about $17 million), or even up to 3% of its total worldwide annual revenue.

Transparency requirements

The EU AI Act’s transparency requirements apply to “natural or legal persons,” public authorities, agencies, or other bodies that develop AI systems, or have them developed, and place them on the EU market or into use under their name or trademark. This means all companies, regardless of whether or not they are EU-based.

“Systems placed on the European market, put into service there, or producing outputs used there are inside the field, wherever the developer sits,” Gogia noted.

Applicable systems must be intended to interact directly with “natural persons”; these systems include AI-enabled chatbots or conversational agents, AI companions, or coding agents. However, AI-enabled tools like recommender systems, spam filters, authentication, search and retrieval, transcription, text and code auto-completion, or predictive maintenance do not fall under the rule.

Specific outputs such as AI-generated text, images, video, and audio must contain a machine-readable mark. Deepfakes and public interest-related text created by AI without human review or control must be clearly labeled, however, deepfake content that is “artistic, creative, satirical, or fictional” is largely exempt.

AI content must be marked with one of three labels: “AI,” “Fully AI-generated,” or “Partially AI-modified.” For instance, “Fully AI-generated” applies when news summaries, music, art, or videos have been created without any human oversight (apart from prompting), while “partially AI-modified” could mean a person’s face is swapped into an authentic photograph to create a deepfake.

The three icons are publicly available for free use; enterprises can download zip files in PNG and SVG formats.

Most of the Act’s transparency rules begin to go into effect on August 2. But AI systems placed on the market before then will have some leeway; they must be in compliance by December 2.

However, a four-month allowance “on one obligation, for one population of systems, contingent on one procedural step, is not a strategy,” Gogia emphasized. Enterprises should plan to comply by August 2 and “treat any relief that arrives as margin.”

A consistent code of practice

Along with the transparency guidelines, the Commission has introduced a code of practice that essentially serves as a gesture of good faith. When signed, it can provide “legal certainty” and a “simple and practical” way to demonstrate compliance with the AI Act, according to the Commission. Signatories can also collaborate through the ‘Signatory Taskforce,’ which will share practices and advance technologies around marking and labeling practices.

Providers that choose not to sign must comply through other methods and demonstrate that those methods are “adequate” through assessment by surveillance authorities, according to the Commission.

Non-signatories “keep their flexibility, and will face more case-by-case scrutiny for it,” said Gogia.

Criteria for compliance

Shashi Bellamkonda, principal research director at Info-Tech Research Group, pointed out that the transparency requirements apply to content only when three criteria are met: It has been published, is informative to the public, or is on matters of public interest.

B2B business content or blogs may not need an AI disclosure if they do not meet these criteria, he noted. Also, published text that has undergone human review or is under editorial control does not need to be labeled. Editorial control means that a person must hold the ultimate legal responsibility for the publication of the content.

Many companies like Google, Adobe, and LinkedIn have already established ways to identify images marked as AI-generated. Meta has made it a requirement, but the creator has to add the AI-generated label, Bellamkonda said.

“This is a good move for guardrails around public information, and companies with good compliance and ethical oversight may not have to worry about this,” he noted. But as a general practice, companies should disclose AI-generated content and state whether it has been human reviewed.

Creating a transparency pipeline

Establishing full transparency means identifying who carries the responsibility for the content, whether the marking survives real use, not just testing, and what evidence will defend the decision, Gogia said.

Concerns cluster around responsibility, durability and evidence. Several organizations usually touch one piece of content, and none controls the whole chain, which is why contracts become the “pressure point,” he said. Most current agreements were written to deliver software and say “almost nothing” about provenance persistence, verification access, or evidence retention.

The durability concern is the most difficult, Gogia noted, because marking performs well in controlled settings but “badly in ordinary life.” Meta, for one, said its invisible watermark was designed to survive cropping; a published test, however, found the company’s preview detector missed 55% of cropped images.

“CIOs should ask which platform can actually provide evidence before believing its dashboard,” said Gogia.

Disclosure of AI use must be “clear, distinguishable and accessible,” he emphasized. “A notice buried in lengthy terms, or reachable only through determined clicking, satisfies nobody, least of all a market surveillance authority.”

Sustained compliance is a “living control” requiring a central record of systems, duties and evidence; testing taking place where the user meets the control rather than where the developer built it; and continuous supplier assurance. Enforcement will vary by country, so keep one common baseline with local overlays, Gogia said.

His advice: Inventory every system that talks to people, generates content, or gauges sentiment; classify provider and deployer roles; place disclosures at first interaction; define substantive human review; keep the evidence.

Marks and provenance signals should be tested after content undergoes cropping, compression, translation, transcription, and other editing, Gogia said. A useful audit starts from a real output and follows its “pulse” through generation, editing and publication, identifying at “each beat” the responsible party, the surviving mark, and evidence for exceptions. Missed labels should also be traced for root cause and recurrence.

To ensure compliance, before August 2, enterprises need a prioritized inventory, live disclosures on the highest-risk use cases, and a “named owner for every control,” he noted. In the first 30 days, they should stabilize and test; in the first 90 days, push requirements into procurement processes as a standing discipline. Procurement must secure commitments on marking methods, known failure modes, and evidence access, with explicit notice if/when any of them change.

“The sensible architecture is a common transparency baseline carrying traceability, responsibility, and evidence, with jurisdictional overlays for language, sector rules, and local practice,” Gogia said.

This article originally appeared on CIO.com.

OECD: Physical labor isn’t immune from AI disruptions

Sat, 25 Jul 2026 19:50:12 +0200

Jobs involving physical labor are at high risk of disruption from automation, with new technologies such as AI robots becoming more prevalent, according to a recent study by the Organization for Economic Co-operation and Development (OECD). That means workers in construction and extraction, farming, fishing, forestry, production and material transportation could be affected by fast-moving technology changes.

“Routine and low-skilled jobs are at higher risk,” the Paris-based public policy group said, adding that “overall, jobs requiring non-routine cognitive, social and creative skills are less susceptible to automation.”

The kinds of creative and cognitive jobs still thought to be less exposed to automation include social work and community service roles.

OECD also said management jobs — which often require workers to devise creative answers to solve problems — fall within the creative and cognitive category. “While AI has made some high-skill job requirements more susceptible to automation, many critical skills in these roles remain difficult to automate,” OECD said.

The same still holds true for some physical and manual labor jobs – including cleaners, agricultural workers, food-prep assistants, and laborers — which are less exposed to the affects of AI, OECD said.

But people who work in programming, translating and interpretation positions could find their work affected by the quick rise of AI tools and services. According to the organization, global AI uptake rose from 7% in 2021 to 20% in 2025.

“In these occupations, GenAI could perform a significant share of tasks at least twice as fast today or in the near future,” OECD said.

The effects of the AI boom are not always uniform across industries or regions. In a separate 2026 Employment Outlook study released by the group, exposure to disruption from generative AI (genAI) ranges from about 16% in some areas to more than 70% elsewhere, depending on industries and occupations.

Numerous research firms have said in recent years that AI is driving short-term job losses, though tech industry experts and analysts have argued AI will also create new careers and jobs as agentic AI takes over low-skilled work.

AI technology has become so ubiquitous that it’s been compared to electricity — virtually all companies will need it or at least know how to use it. But it’s adoption has been hindered at times as companies struggle to find ROI from its use, and by regulatory and ethical hurdles.

In the US, AI blamed for June job losses

According to a Challenger, Gray & Christmas study released earlier this month, AI was cited as the top reason for job cuts in June. The outplacement firm said employers cut 45,849 job cuts in June, of which 14,029 were attributed to AI, with the tech industry leading the cuts.

“Tech remains the epicenter of this year’s cuts,” Challenger said. “AI is the dominant force as companies are restructuring around it, automating roles, and reallocating budgets toward new capabilities. The sector is being reshaped in real time.”

Overall, AI has been responsible for 173,568 job cuts since 2021, the company said.

AI is hurting jobs in customer service, internal reporting, telecommunications, and hosting automation, said Victor Janulaitis, a staffing consultant who was formerly CEO at Janco Associates Inc.

“C-level executives continue to be focused on eliminating ‘non-essential’ managers, staff, and services,” he said. “Coders and developers have limited opportunities with legacy applications.”

While jobs in the IT sector overall are declining, current hiring tends to skew in the direction of people with AI skills. A report this month by CompTIA put job listings with AI skills at around 500,000, which is close to double the number in January.

“Employers in other industries are accelerating digital transformation initiatives and moving from AI experimentation to implementation,” said Seth Robinson, CompTIA’s vice president for industry research.

That view dovetails with what ManpowerGroup, the recruitment firm, is seeing; demand for AI-related skills has nearly doubled over the past year, said Ger Doyle, regional president of North America at ManpowerGroup.

That growth extends well beyond traditional technology roles as companies move from experimenting to AI deployments at scale, Doyle said. “We’re seeing it influence hiring across occupations ranging from data science and engineering to project management and operational roles,” he said.

Monday.com cuts 20% of its workforce to restructure for the AI era

Sat, 25 Jul 2026 19:50:09 +0200

Healthy software companies typically don’t suddenly eliminate one-fifth of their workforce, but monday.com is doing just that as it bets on flatter teams, AI agents, and customer implementation expertise as the winning combination in the AI era.

Monday.com co-founder and co-CEO Eran Zinman today announced the “very difficult decision” to reduce the AI work platform company’s global workforce by about 20%, or 620 people.

The move has nothing to do with increasing margins or replacing humans with AI, he insisted in his post on LinkedIn; rather, it’s a calculated decision to trim down and hone the company’s focus as AI becomes integral to day-to-day workflows.

“This is not a distress signal; it is a deliberate reset, disclosed with its price attached,” said Sanchit Vir Gogia, chief analyst at Greyhound Research. “The industry has quietly swapped the meaning of productivity, and this filing is the clearest exhibit yet.”

A ‘significant opportunity’ in technology

In a SEC filing this week, monday.com said its restructuring plan reflects the “ongoing transformation of its product, marketing, and go-to-market strategy.” The move is intended to support a “leaner, more focused operating model” as the company continues to invest in its AI-driven strategy.

Zinman noted in his post that the company has shifted to “doing the work with AI and not just managing it,” and is focused on building environments where “people and AI agents [work] together in one workspace.”

In recent months, monday.com has evolved its products, strategy, and the way it serves its customers, and Zinman contended that “the organization we built for our previous chapter is not the organization that fits the new AI era.” Monday.com needs to “execute more decisively,” take on new challenges, and quickly respond to market changes, he said.

“We have never seen such a significant opportunity in software, driven by such exciting technology,” Zinman noted. He emphasized that the reduction is not to replace people with AI, nor to improve margins; the “vast majority” of savings will be reinvested into talent, products, and AI.

The restructuring will result in a “flatter organization” with fewer management layers and smaller, more autonomous teams, and monday.com also has a new go-to-market model, Zinman explained. Customers expect “deeper implementation support” as they deploy AI, and the company will work more closely with customers, increase its on-site presence, create new roles, and “adapt many existing ones.” In its SEC filing, the company said it expects to continue hiring in “key strategic areas” throughout 2026.

Workers will be expected to work better, “not harder,” Zinman noted. He pointed to several past examples where work could have been done in a few days, but instead took many months with “multiple meetings and endless friction.”

“This wasn’t people’s fault and everyone was frustrated by this,” he said. “Our new org changes ownership to allow people to make decisions and move fast.”

A spokesperson for monday.com declined to comment further on the staff reductions.

Monday.com’s key market advantages

Monday.com certainly isn’t struggling; the company expects 19% to 20% year-over-year growth in 2026.

“Companies in that position do not restructure because they must,” Greyhound’s Gogia noted. “They restructure because they have decided to become something else.”

Melody Brue, VP and principal analyst at Moor Insights & Strategy, pointed out that organizational redesign is important for real AI transformation, but while it can signal confidence to the market, it can still be “devastating” to humans.

While the company looks as though it’s trying to do right, that ultimately remains to be seen, she said. “There are often hidden internal bruises that can surface long after layoffs.”

Monday.com’s advantage is in its “structured substrate,” Gogia noted; its boards, permissions and typed workflows give agents something firmer to act on than just documents and chat history. The company highlights its natively built agents that can be configured by any team member, as well as connectors with Claude, Microsoft Copilot, and ChatGPT, and dedicated routes for external agents to authenticate and operate.

“For some time, the sharper enterprise question has been shifting from who has an agent to who owns the governed runtime in which an agent can safely act,” he said. “Structured work is a serious claim on that runtime.”

But parts of monday.com’s agent estate remain in staged release, and its product is ultimately “mid-transition,” Gogia pointed out; its agent builder carried a beta label as recently as March,. Also, the company’s pricing model changed in May to a hybrid model charging for seats as well as mandatory AI credits. And, while its AI-powered no-code builder monday vibe passed $1 million in annual recurring revenue within two and a half months, monday.com has not released subsequent outcomes, usage volumes, or attach rates.

Further, there’s an element of “gravity” with its competitors, he observed. Asana is reorganizing teams around agents, Atlassian is wiring agents into the developer estate, and others are simply bundling them into their offerings: Microsoft is doing so across the productivity stack, and ServiceNow across enterprise operations, each with identity and procurement built in.

“Their pull is strongest exactly where monday.com wants to grow, in the largest accounts, where control-plane depth and administrative reach decide the deal,” said Gogia.

Actions for the near-term

Going forward, buyers should focus on operating risk, not headline risk, Moor’s Brue noted. In practice, that’s continuity of service, roadmap consistency, and strength of enterprise support. Productivity should be valued as better outcomes per unit of organizational effort, not mere activity.

“It should be a measure of how much smoother, faster, and more effective the operating model becomes when AI is built into the work,” said Brue.

Gogia noted that strain surfaces first in customer service, and monday.com’s attention is being redistributed. The company’s annual report disclosed that its focus is now concentrated on the largest accounts, with support for medium-sized clients moved to an AI-first and human-supported model.

During the first month of the transition, buyers should track named account continuity and escalation times, he advised. By the first quarter, keep an eye on whether credit governance and admin controls mature on schedule, and if the roadmap beyond the AI estate keeps pace. By the half-year mark, determine whether promised implementation depth is producing outcomes or “simply more billable engagement.”

Support tiers should be enumerated in writing before renewal, and buyers should contract for “side exits,” Gogia emphasized, with overage pricing fixed in advance, the right to pause consumption, and portability for workflows and agent configuration “if the relationship sours.” Finance should also insist on monthly consumption reporting by capability. Further, integration efforts, partner dependency, and change management should be considered first-class costs of the agent era, “not as afterthoughts to a license.”

“A license was a known cost,” said Gogia. “A meter is a behavior, and behavior is harder to forecast than headcount.”

This article originally appeared on CIO.com.

AMD raises the AI stakes with Helios, Venice and robotics

Sat, 25 Jul 2026 19:50:07 +0200

AMD executives took to the stage at its Advancing AI 2026 event in San Francisco today to detail the company’s next generation of AI infrastructure solutions, from Instinct MI455X AI accelerator GPUs and 6th Gen EPYC “Venice” CPUs, to Pensando networking, ROCm.AI software and its Helios rack-scale platform that ties it all together.

AMD has been working towards rack-scale AI system solutions for years. Its ZT Systems acquisition last year added valuable engineering talent and intellectual property that is now finally bearing the real fruits. Its Helios AI platform is a major platform evolution for AMD, with shipments scheduled to begin in the second half of this year (which is here and now).

The announcements at Advancing AI show how the company has engineered its AI platform solutions for large reasoning models, sustained inference and agentic workflows. These workloads pressure memory capacity, data movement, networking and CPU orchestration. AMD’s approach is to keep as much data close to the compute engines as possible and move it more efficiently throughout the system, but there’s deeper nuance here that’s obvious versus AMD’s chief rival, NVIDIA.

AMD’s MI455X targets the AI memory wall

The Instinct MI455X GPU is the compute engine that fuels the Helios rack, and the first GPU based on AMD’s new CDNA 5 architecture. Built with a modular mix of 2nm and 3nm chiplets, it carries 432GB of HBM4 and 23.3TB/s of peak memory bandwidth.

Compared to AMD’s current MI355X, the MI455X offers 1.5 times the memory capacity, up to 2.9 times the peak memory bandwidth and up to four times the peak matrix performance with MXFP4 and MXFP8 data types, which are lower-precision numerical formats designed to accelerate AI processing while reducing memory demands. With MXFP6 (6-bit floating point), performance is rated at up to twice that of MI355X.

AMD also shared some actual, measured internal results using production silicon. The company claims MI455X delivers 3.8 times higher FP8 decode performance, 3.5 times more measured FP4 compute performance and between 2.5 and 3.5 times more networking bandwidth than MI355X, depending on the transfer path tested. Those figures provide more context than just numerical specifications, though they remain AMD-provided comparisons that will need independent validation.

AMD

The architectural choices behind the numbers are important. Reasoning models and long context windows require sizeable KV caches for maintaining AI attention states, while mixture-of-experts models frequently move large amounts of data across accelerators. MI455X should let more model data, activation states and cache remain local. New dedicated IP in hardware can transfer data while the GPU continues processing, and expanded cache and multicast capabilities are designed to reduce redundant data movement to further improve efficiency.

The aforementioned lower-precision formats can also raise throughput and reduce memory use, but model developers still have to determine where they can be applied without unacceptable accuracy loss.

AMD’s Helios rack takes aim at Vera Rubin

Dave Altavilla

Helios is AMD’s primary rack-scale competitor to NVIDIA’s Vera Rubin platform. Each liquid-cooled rack combines 72 MI455X GPUs, 18 single-socket Venice host CPUs and Pensando networking technologies.

In its most complete, premium configuration, AMD rates Helios for 2.9 exaflops of low-precision AI compute, with 31TB of aggregate HBM4 capacity, 1.7PB/s of memory bandwidth, 260TB/s of bidirectional scale-up bandwidth and 43TB/s of scale-out bandwidth.

These are formidable figures, but they are technical specifications rather than actual application benchmarks. The more consequential development is AMD’s move from collections of eight-GPU servers to a 72-GPU shared-memory domain. Models too large for one node can operate across the rack without treating every exchange as a scale-out networking transaction, which benefits large-model inference as well as training.

AMD uses UALink over Ethernet, or UALoE, for an open standard scale-up fabric. Each MI455X provides 3.6TB/s of bidirectional scale-up bandwidth, while the complete rack delivers all-to-all connectivity through a single switch layer. AMD also claims six times more scale-out bandwidth per GPU than MI355X when MI455X is configured with three Pensando Vulcano 800 AI NICs.

While open standards give cloud providers more control over suppliers and system design, AMD and its partners now have to prove those components can deliver the predictable performance, reliability and deployment experience customers expect from a tightly controlled, more vertically integrated platform.

Finally, AMD designed Helios with automatic rerouting around failed links, virtual rack partitions, tray-level serviceability and rack-wide power, cooling and health monitoring. Major hyperscalers and potentially large-scale enterprise customers will likely key in on these capabilities, which can affect the availability, total cost and consistency of the AI services they consume.

Kind of like cowbell, AMD Venice gives agentic AI more CPU

AMD

AMD’s agentic CPU messaging regarding its upcoming Venice-based EPYC processors is mostly marketing speak, but the underlying requirement is very real. An AI agent can invoke retrieval, databases, security checks, code execution and other tools before a GPU generates a response. Running many agents concurrently increases the amount of conventional compute requirements surrounding the accelerators.

Venice scales to 256 Zen 6 cores with support for 512 threads, 16 memory channels, up to 1GB of L3 cache per socket, along with PCIe 6.0 and CXL 3.1 connectivity. AMD is also offering several Venice configurations for other applications, including general-purpose servers, high-frequency workloads, GPU hosts and high-density CPU sandbox systems used to execute agent tools.

Treating the CPU solely as a GPU host understates its role. Gateways, tokenization, vector search, databases and short-lived code execution stress different mixes of per-core performance, thread count, memory bandwidth and I/O. Specifically, AMD’s internal testing shows Venice significantly outperforming its current EPYC 9965 Turin CPU across five parts of the agentic AI pipeline, including gateway processing, context assembly, vector search, enterprise applications and short-lived tool execution. Individual gains vary by workload, but AMD details the overall generational improvement at up to a 1.7 times lift. As with the MI455X figures though, these comparisons come from AMD and will require independent validation.

Pensando networking and ROCm software advance

Keeping GPUs fed with data and coordinating traffic across racks directly affects utilization and operating costs. In fact, GPU utilization is a pretty sad state of affairs currently for some of the major frontier model providers.

As such, Pensando networking has become central to AMD’s roadmap. Helios can connect each MI455X to as many as three 800Gbps Vulcano AI NICs, while Salina DPUs handle front-end networking and infrastructure services.

On the software side, which is an equally critical component, AMD also introduced ROCm.AI, an AI-assisted development layer due to arrive in August. It includes reusable skills for coding agents, simplified management and Hyperloom, which can profile workloads, tune serving configurations, modify kernels and validate results.

These tools address two persistent AMD challenges: developer efficiency and ease of use, and software tuning. Automated optimization still has to produce repeatable gains without creating hard-to-maintain code, however. And while ROCm has progressed significantly over the last few years, NVIDIA’s CUDA retains an advantage in maturity, tooling and developer familiarity.

Customer commitments underscore rack-scale confidence

AMD now has commitments that give its MI450 generation and Helios considerably more weight. Meta and OpenAI have announced multi-generation agreements composed of up to 6GW of AMD compute capacity, with initial 1GW deployments planned for the second half of 2026.

Oracle plans a 50,000-GPU public cloud cluster beginning in the third quarter, while Microsoft will deploy Helios for Azure AI inference. Finally, just before the AMD event, Anthropic announced a strategic partnership for up to 2 Gigawatts of AMD-fueled AI compute, with its first gigawatt expected online in the first half of 2027.

Commitments of this scale reflect confidence in more than just MI455X performance. These customers are evaluating the complete architecture, including Venice CPUs, Pensando networking, ROCm software, rack integration, serviceability and AMD’s ability to deliver and execute across multiple product generations.

There is some financial alignment behind the agreements as well. AMD issued OpenAI performance-based warrants and committed to investing up to $5 billion in Anthropic. That context matters when evaluating these deals as market validation, but these planned deployments are substantial nonetheless and put Helios on a much stronger foundation as it begins shipping.

AMD expands its robotics and embedded foundation

AMD also expanded its physical AI portfolio, building on credible traction from its Xilinx-derived Kria adaptive system-on-modules and embedded technologies that are already powering robotics, machine vision and industrial automation applications.

The new Ryzen AI Embedded X100 combines up to 16 Zen 5 CPU cores, integrated Radeon graphics, a second-generation NPU and as much as 128GB of unified LPDDR5X memory shared across its compute engines. To me this looks a lot like a repackaging and optimization of the company’s Strix Halo platform, but with specific optimizations for the embedded space. Regardless, AMD is pairing X100 with the Kria AI Robotics Developer Platform, which includes a System Module or SOM, and a new Robotics Partner Network spanning hardware, software and platform providers.

Samples began shipping in June, with full production expected in the fourth quarter. This broader objective is to give developers a path across AMD x86 CPUs, GPUs, NPUs and FPGAs for real-time autonomous systems, rather than requiring them to assemble those hardware engines and software components independently.

Execution for AMD is now the test

AMD has assembled a credible platform for the burgeoning agentic AI market that’s blowing up currently with no signs of stopping. MI455X addresses memory and data movement, Venice handles dense agentic CPU workloads, Pensando networking connects global system resources, and ROCm.AI addresses software complexity. Finally, Helios assembles these components into a true competitive threat for NVIDIA’s latest Vera Rubin platform.

AMD’s open architecture may appeal to customers seeking supplier choice, but openness must also translate into reliable deployments, competitive total cost and software that does not require a significant rip-up. NVIDIA enters this cycle with a stronger ecosystem and far more rack-scale deployment experience. The true test will be how easily and reliably customers can integrate, operate and maintain these AMD solutions at scale.

As it stands, AMD now has major customers and a clearly defined architecture with systems engineering expertise behind it. Delivering Helios on schedule and showing that its performance claims translate into a real production workload throughput advantage and total cost of ownership gains will determine how much the competitive gap narrows. And of course, this is in a market that is clamoring for ever-more compute resources with a seemingly insatiable demand for AI services and capacity. That’s an environment for big iron success. Now AMD just has to deliver optimized, turnkey AI platforms. This is far easier said than done, but time will soon tell as deployments take shape this year.

This article is published as part of the Foundry Expert Contributor Network.
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Move fast and save things: A quick guide to recovering a hacked account

Sat, 25 Jul 2026 19:04:37 +0200

What you do – and how fast – after an account is compromised often matters more than it may seem

Announcing winapp, the Windows App Development CLI

Sat, 25 Jul 2026 19:01:37 +0200

We are excited to announce the public preview of the Windows App Development CLI (winapp), a new open-source c

The post Announcing winapp, the Windows App Development CLI appeared first on Windows Developer Blog.

GDC 2026: Announcing new tools and platform updates for Windows PC game developers

Sat, 25 Jul 2026 19:01:36 +0200

Our focus is clear: making Windows 11 the best place for game developers to create, experiment, ship and scale. Windows is an open, flexible platform that supports choice across engines, tools, hardware and distribution models. That commitment is roo

The post GDC 2026: Announcing new tools and platform updates for Windows PC game developers appeared first on Windows Developer Blog.

Enhanced developer tools on the Microsoft Store

Sat, 25 Jul 2026 19:01:36 +0200

The Microsoft Store on Windows continues to evolve, shaped by ongoing feedback from developers building and scaling apps on the platform. Over the past months, that feedback has guided several updates across onboarding, publishing, distribution and a

The post Enhanced developer tools on the Microsoft Store appeared first on Windows Developer Blog.

Accelerating Arm momentum in Japan with App Assure

Sat, 25 Jul 2026 19:01:36 +0200

Worldwide, customers are adopting Arm-powered Copilot+ PCs for the many benefits they provide over traditional PCs. These devices deliver industry-leading performance, outpacing competitors in both every day and demanding workloads, while off

The post Accelerating Arm momentum in Japan with App Assure appeared first on Windows Developer Blog.

Part 2 – Babylon.js 9.0: Tooling updates and new geospatial features

Sat, 25 Jul 2026 19:01:35 +0200

Our mission is to create one of the most powerful, beautiful and accessible web rendering engines in the world. With Babylon.js 9.0, we’re delivering a major set of enhancements to help you build stunning experiences.

Babylon.js Editor<
The post Part 2 – Babylon.js 9.0: Tooling updates and new geospatial features appeared first on Windows Developer Blog.

Announcing Babylon.js 9.0

Sat, 25 Jul 2026 19:01:35 +0200

Our mission is to build one of the most powerful, beautiful, simple and open web rendering engines in the world. Today, we are thrilled to announce that mission takes a monumental leap forward with the release of Babylon.js 9.0.

https://www.youtube.

The post Announcing Babylon.js 9.0 appeared first on Windows Developer Blog.

Part 3 – Babylon.js 9.0: OpenPBR and additional engine updates

Sat, 25 Jul 2026 19:01:34 +0200

Babylon.js 9.0 introduces several enhancements to help developers build beautiful, high-performance 3D experiences.

OpenPBR Support - Alpha

Babylon.js 9.0 begins implementation of Part 3 – Babylon.js 9.0: OpenPBR and additional engine updates appeared first on Windows Developer Blog.

Krieg der Sterne: Wie Hotels und Restaurants ihre Google-Bewertungen frisieren

Sat, 25 Jul 2026 18:59:45 +0200

Der Beitrag Krieg der Sterne: Wie Hotels und Restaurants ihre Google-Bewertungen frisieren erschien zuerst beim Online-Magazin BASIC thinking. Über unseren Newsletter UPDATE startest du jeden Morgen bestens informiert in den Tag.

Wer ein Restaurant oder Hotel auf Google Maps sucht, orientiert sich an den Sternebewertungen. Doch eine dpa-Stichprobe zeigt, dass allein in Hessen 17 Prozent der Gastbetriebe bereits Rezensionen haben löschen lassen – in Frankfurt sogar fast jeder Dritte. Der Grund: Was als Schutz vor Diffamierung gedacht war, ist längst zu einem Reputationswerkzeug verkommen. Seit April […]

Der Beitrag Krieg der Sterne: Wie Hotels und Restaurants ihre Google-Bewertungen frisieren erschien zuerst auf BASIC thinking. Folge uns auch auf Google News und Flipboard oder abonniere unseren Newsletter UPDATE.

9 Kommandozeilen-Tools, die jeder Dev braucht

Sat, 25 Jul 2026 18:59:24 +0200

Selbst wenn Sie dieser Anblick nicht in Verzückung versetzt – ein Blick auf diese obligatorischen Kommandozeilen-Tools lohnt sich.

Foto: SkillUp | shutterstock.com

Manche Devs arbeiten mit der Kommandozeile (auch Command Line Interface; CLI), weil sie sie lieben – andere, weil ihnen nichts anderes übrig bleibt. Egal zu welcher Kategorie Sie sich zählen: Diese neun CLI-Tools helfen Ihrer Produktivität und Effizienz (zusätzlich) auf die Sprünge.

tldr

Keine Angst, wir ersparen Ihnen an dieser Stelle eine langwierige, faszinative Abhandlung über die ganz eigene Magie, die die Unix-Shell entfaltet. Fakt ist: Wenn man mit ihr arbeiten will, ist es manchmal erforderlich, vorher ein Handbuch zu lesen. Unix Docs (auch man- oder manual pages) sind diesbezüglich allerdings ein zweischneidiges Schwert: Die benötigte Information ist vorhanden – es ist nur die Frage, wo. Den Teil der Dokumentation aufzuspüren, den Sie gerade benötigen, kann ein entmutigender Task sein. Zwar kann die gute alte Befehlszeile dabei helfen – um ein offizielles Handbuch aufzurufen, genügt:

$ man

Allerdings zeichnen sich man-pages vor allem durch ihre Informationsdichte aus – und die Tatsache, dass sie manchmal aktuelle Informationen für neuere Tools vermissen lassen. Das CLI-Tool tldr versetzt Sie in die Lage, zielgerichteter zu suchen:

$ tldr

tldr in Aktion.

Foto: Matthew Tyson | IDG

Falls Sie npm installiert haben, ist die tldr-Installation nur einen kurzen Befehl entfernt:

npm install -g tldr

ngrok

Sobald Sie tldr installiert haben, können Sie damit viele weitere Befehle erkunden. Zum Beispiel:

$ tldr ngrok

Reverse proxy that creates a secure tunnel from a public endpoint to a locally running web service.

Mit ngrok eröffnet sich Ihnen eine stressfreie Möglichkeit, von einem Remote-Browser auf eine Entwicklungsmaschine zuzugreifen. Aber das Tool kann noch weit mehr. Sie können damit beispielsweise in der Cloud entwickeln und die Ergebnisse im Browser in Augenschein nehmen. Zudem können Sie mit ngrok auch schnell und einfach laufende Services über HTTPS veröffentlichen – ohne sich mit der Security-Infrastruktur herumschlagen zu müssen. Angenommen, Sie bauen einen Service Worker auf, der HTTPS benötigt, dann ist alles, was Sie für einen sicheren Kontext tun müssen, ngrok zu starten.

Das CLI-Tool ngrok macht Devs das Leben auf verschiedenen Ebenen leichter.

Foto: Matthew Tyson | IDG

Ein Beispiel, bei dem der HTTP-Port 8080 freigegeben wird:

$ ngrok http 8080

Der ngrok-Output sieht wie folgt aus:

https://f951-34-67-117-59.ngrok-free.app -> http://localhost:8080

Anschließend kann jedermann die zugewiesene URL aufrufen (machen Sie sich keine Mühe).

screen

Mit diesem Befehlszeilen-Tool können Sie eine Shell-Sitzung mit oder ohne laufenden Prozess “beiseite legen” und sie anschließend zu einem beliebigen Zeitpunkt fortsetzen – auch wenn Sie die ursprüngliche Session beenden.

$ tldr screen

Hold a session open on a remote server. Manage multiple windows with a single SSH connection.

Nehmen wir an, Sie starten ngrok, um remote auf eine Webanwendung zuzugreifen: Sie starten den Prozess, lassen diesen dann in screen laufen und programmieren so lange etwas. Währenddessen läuft ngrok die ganze Zeit weiter – Sie können über screen jederzeit wieder darauf zugreifen. Veranschaulicht in Code würde das wie folgt aussehen:

$ screen

// Now we are in a new session

$ ngrok http 8080

// Now ngrok is running, exposing http port 8080

Type ctrl-a

// Now we are in screen's command mode

Type the "d" key, to "detach".

// Now you are back in the shell that you started in, while screen is running your ngrok command in the background:

$ screen -list

There is a screen on:

128861.pts-0.dev3 (04/25/24 14:36:58) (Detached)

Tipp

Wenn Sie eine laufende Sitzung, in der Sie sich gerade befinden, benennen wollen, nutzen Sie die Tastenkombination Strg + A und geben :sessionname ein. Das ist besonders nützlich, wenn Sie mit mehreren Screen-Instanzen arbeiten wollen.

Screen ist ein umfangreiches und potentes CLI-Tool.

Foto: Matthew Tyson | IDG

Wenn wie im Beispiel nur eine screen-Instanz läuft, führt der Befehl $ screen -r (für “re-attach”) Sie zurück zu Ihrer ngrok-Sitzung. Im Fall mehrerer Screens können Sie diese mit Hilfe ihrer ID wieder aufrufen:

$ screen -r

Wenn Sie Ihre Session endgültig beenden wollen, beenden Sie ngrok mit Strg + C und geben anschließend exit in die Kommandozeile ein.

sdkman & nvm

Wenn Sie Java oder JavaScript auf einem Server verwenden, sollten Sie sich mit sdkman (für Java) und nvm (für Node) vertraut machen. Beide Kommandozeilen-Tools sind nützlich, wenn es darum geht, mit mehreren Programmiersprachenversionen auf dem selben Rechner zu jonglieren – und dabei sowohl Path Adjustment als auch Umgebungsvariablen überflüssig machen.

Mit sdkman können Sie beispielsweise neuere Java-Versionen erkunden und anschließend wieder zum aktuellen LTS-Release springen. Dieser Prozess wird durch das sdk-Kommando abstrahiert.

sdkman zeigt alle verfügbaren Java-Installationen auf einem lokalen Rechner an – inklusive derjenigen, die gerade in Benutzung ist.

Foto: Matthew Tyson | IDG

Zwischen den Versionen zu wechseln, gestaltet sich denkbar einfach – $ sdk use java 19-open führt Sie direkt zu JDK Version 19.

$ tldr sdk

Manage parallel versions of multiple Software Development Kits.

Supports Java, Groovy, Scala, Kotlin, Gradle, Maven, Vert.x and many others.

Die nvm-Utility funktioniert ganz ähnlich:

$ tldr nvm

Install, uninstall or switch between Node.js versions.

Supports version numbers like "12.8" or "v16.13.1", and labels like "stable", "system", etc.

Ein Blick auf nvm.

Foto: Matthew Tyson | IDG

fzf

Sowohl grep als auch find sind Standardbestandteile der Kommandozeilen-Befehlspalette. Allerdings sind beide Tools nicht so funktional, wie sie sein sollten. Das ruft fzf auf den Plan – einen “Fuzzy File Finder”. Mit “Fuzzy” ist dabei gemeint, dass die Details zu dem, was Sie suchen, nicht unbedingt klar definiert sein müssen. Ein Beispiel:

$ tldr fzf

Command-line fuzzy finder.

Similar to sk.

Sobald Sie fzf starten, indiziert das CLI-Tool umgehend das Dateisystem, um Ergebnisvorschläge für Ihre Suchen zu unterbreiten.

In diesem Beispiel suchen wir nach einem Projekt, an dem wir zuletzt gearbeitet haben.

Foto: Matthew Tyson | IDG

Aus 878.937 Möglichkeiten hat fzf die 25 Dateien und Verzeichnisse ausgewählt, die unseren Anforderungen entsprechen könnten – und das völlig ohne Umwege.

exa

Mit exa werden langweilige alte ls-Listings schöner und nützlicher:

$ tldr

A modern replacement for ls (List directory contents).

Für eine bessere Developer Experience ohne mentalen Overhead statten Sie ls einfach mit einem exa-Alias aus. Das Tool respektiert die meisten ls-Standardoptionen – exa -l funktioniert also (beispielsweise) genau so, wie Sie es erwarten würden.

Exa ist das neue ls.

Foto: Matthew Tyson | IDG

bat

Die bat-Utility ähnelt dem cat-Tool – ist aber besser:

$ tldr bat

Print and concatenate files.

A cat clone with syntax highlighting and Git integration.

Es handelt sich hierbei im Wesentlichen um eine Komfort- beziehungsweise Developer-Experience-Optimierung – ähnlich wie im Fall von exa. Wenn Sie bat verwenden, erwartet Sie ein vollwertiger File Viewer – inklusive Title, Borders, Line Numbers und insbesondere einer hilfreichen Syntax-Highlighting-Funktion für Programmiersprachen oder Konfigurationsdateien. Dabei reagiert bat auf less/more-Befehle – und wird mit “q” beendet. Die Navigation erfolgt über die Pfeiltasten.

Bat ist ein simples Dienstprogramm, das es zu einem echten Erlebnis macht, Dateien auf der Konsole zu durchsuchen.

Foto: Matthew Tyson | IDG

nethack

Ein absoluter Kommandozeilen-Klassiker ist nethack – der ursprüngliche, Konsolen-basierte ASCII Dungeon Crawler. Das CLI-Tool wird Ihre Produktivität zwar nicht direkt ankurbeln – kann aber durchaus dabei helfen, ein paar Minuten zur Ruhe zu kommen, um komplexe Dev-Probleme zu durchdringen.

Es gibt neuere Versionen des Nethack-Konzepts – manchmal fährt man jedoch mit dem Original am besten.

Foto: Matthew Tyson | IDG

Dieser Artikel ist im Original bei unserer Schwesterpublikation Infoworld.com erschienen.

Honor Magic V6 im Test: Das fast perfekte Foldable

Sat, 25 Jul 2026 18:59:23 +0200

Luke Baker

Auf einen Blick

Pro

Wunderschönes, schlankes Design
Große, helle Displays
Hervorragende Kameras
Coole Software-Funktionen

Kontra

Hoher Preis
MagicOS kann gelegentlich frustrierend sein

Fazit

Das Honor Magic V6 ist ein Falt-Smartphone, das einfach alles kann. Es ist schlank, robust, leistungsstark und sieht gut aus. Die Kameras gehören zu den besten, die Software bietet einige raffinierte Funktionen und die Akkulaufzeit ist außergewöhnlich. Besitzer eines Magic V5 werden wahrscheinlich kaum einen Grund für ein Upgrade sehen, aber für alle anderen ist es eine gute Wahl – sofern Sie es sich leisten können.

Das Honor Magic V6 wurde ursprünglich bereits im März in China vorgestellt, doch es dauerte eine Weile, bis es den Weg nach Europa fand. Nun ist es endlich in Europa erhältlich und strebt den Titel des bislang besten Falt-Smartphones an.

Leider ist die Konkurrenz für Honor stärker denn je, und sie wird noch härter werden, sobald Apples seit Langem gemunkeltes Foldable auf den Markt kommt. Bietet dieses schlanke Kraftpaket genug, um weiterhin überzeugend zu bleiben?

Es sieht auf jeden Fall vielversprechend aus, verfügt über einen der größten Akkus aller jemals erschienenen Falt-Smartphones, ist das erste mit IP69-Zertifizierung und gehört dennoch zu den dünnsten Modellen. Das ist schon mal ein guter Anfang. Ich habe es in den vergangenen Wochen auf Herz und Nieren geprüft, und hier ist mein Fazit.

Design & Verarbeitung

Aluminiumrahmen, Rückseite aus Verbundfasermaterial
219 g, NanoCrystal Shield-Glas
IP69-zertifiziert

Das Honor Magic V6 sieht dem Magic V5 sehr ähnlich, wobei der auffälligste Unterschied in der Form des Kamerarahmens liegt. Dieser ist nun kantiger und weist eine achteckige Form auf. Auch neue Farbvarianten tragen dazu bei, es von der Vorgängergeneration abzugrenzen.

Luke Baker

Die rote Version ist wahrscheinlich die auffälligste; sie verfügt über eine tiefrote, fast blutrote Rückseite mit strukturierter Oberfläche. Ich besitze die goldene Version, die ebenfalls sehr auffällig ist. Freunde haben bemerkt, dass es wie ein Smartphone aussieht, das ein saudischer Prinz oder Kim Kardashian benutzen würde.

Die Farbe schimmert leicht, wenn Licht darauf fällt; sowohl am Scharnier als auch auf der Rückseite ist ein dezentes, sich wiederholendes Dreiecksmuster zu erkennen, und es bleibt irgendwie frei von Fingerabdrücken, obwohl es ziemlich glänzend ist. Das sieht wirklich cool aus. Auch die mitgelieferte Hülle ist von höchster Qualität, mit einem cremefarbenen Kunstlederbezug und einem roségoldenen Kamerarahmen, der gleichzeitig als ausklappbarer Ständer dient.

Wenn Sie etwas suchen, das etwas weniger Aufmerksamkeit auf sich zieht, gibt es natürlich auch traditionellere Modelle in Schwarz und Weiß. Letzteres hat zudem einen versteckten Vorteil: Mit nur 8,75 Millimetern im geschlossenen Zustand ist es das dünnste Modell der Reihe, und Honor geht davon aus, dass es derzeit das dünnste Falt-Smartphone auf dem Markt ist.

Ich habe das weiße Modell nicht ausprobiert, und vielleicht ist es tatsächlich spürbar schlanker, aber meiner Meinung nach sieht die goldene Version definitiv etwas dicker aus und fühlt sich auch so wie mein 8,9 Millimeter dickes Galaxy Z Fold 7 an.

Das ist allerdings Haarspalterei, denn dieses Smartphone ist immer noch dünner als viele Flaggschiff-Smartphones im Barrenformat. Ob es nun das dünnste ist oder nicht – es ist auf jeden Fall dünn genug, um sich in der Hosentasche angenehm anzufühlen.

Luke Baker

Erwähnenswert ist auch, dass das Cover-Display nun flach ist und eine symmetrischere Form aufweist. Dadurch sieht das Smartphone im zusammengeklappten Zustand noch mehr wie ein herkömmliches Barren-Handy aus, doch die unvermeidliche, eckige Kante am Scharnier verrät es ein wenig.

So wichtig die Abmessungen eines Klapphandys auch sind, ebenso entscheidend ist die Robustheit. Und glücklicherweise ist das Honor Magic V6 robust wie ein Panzer gebaut.

Es verfügt über die Schutzklasse IP69 – die höchste aller faltbaren Smartphones. Das bedeutet, dass es praktisch staubdicht ist und sowohl das Eintauchen in Süßwasser als auch den Strahl von heißem Wasser problemlos aushält. Die Technologie der faltbaren Smartphones hat große Fortschritte gemacht.

Es ist zudem besonders robust konstruiert, mit kratzfestem „NanoCrystal Shield“-Glas auf der Vorderseite und einem „Super Steel“-Scharnier, das die Stoßfestigkeit verbessert.

SGS-Zertifizierungen untermauern diese Angaben ebenfalls, da das Smartphone für seine Fallfestigkeit mit 5 Sternen ausgezeichnet wurde. Ich habe mein Testgerät nicht allzu grob behandelt, aber es ist beruhigend zu wissen, dass es unversehrt bleiben dürfte, sollte mir einmal ein Ausrutscher unterlaufen.

Bildschirm & Lautsprecher

Außen: 6,52 Zoll, 1.080 x 2.420, OLED, 120 Hertz
Innen: 7,95 Zoll, 2.172 × 2.352, OLED, 120 Hertz
Stereolautsprecher

Bei Falt-Smartphones dreht sich alles um den Bildschirm. Beginnen wir also mit dem Star der Show: dem inneren Display. Mit einer Diagonale von knapp unter 8 Zoll gehört es zu den größten Falt-Smartphones im Buchformat auf dem Markt.

Es ist nahezu perfekt quadratisch, was sich hervorragend für die parallele Nutzung zweier Apps eignet.

Luke Baker

Dieses flexible Display verfügt über eine Bildwiederholfrequenz von 120 Hertz und erreicht eine beeindruckende Spitzenhelligkeit von 5.000 Nits. Solche Helligkeitsangaben sollten Sie zwar stets mit einer gehörigen Portion Skepsis betrachten, doch unabhängig davon handelt es sich um ein sehr helles Display, das auch im Freien gut ablesbar ist.

Der innere Bildschirm ist mit einer glänzenden Schutzfolie versehen (wenn auch mit einer Antireflexbeschichtung), was sowohl Vor- als auch Nachteile mit sich bringt. Wie ich bereits in anderen Testberichten zu Falt-Smartphones erwähnt habe, kaschieren mattierte Schutzfolien die Falz zwar besser, ziehen aber auch Fingerabdrücke an. Diese hier verschmiert nicht so leicht, doch trotz aller Bemühungen von Honor treten entlang der Falz einige unerwünschte Reflexionen auf.

Luke Baker

Apropos: Die Falz ist bewundernswert flach, und noch vor nicht allzu langer Zeit wäre sie die beste gewesen, die mir je begegnet wäre. Allerdings nutze ich das Oppo Find N6 seit seiner Markteinführung fast täglich, und leider kann dieses Modell da nicht ganz mithalten.

Es ist dennoch äußerst beeindruckend und um Längen besser als das Pixel 10 Pro Fold. Auch im Vergleich zum Galaxy Z Fold 7 fällt die Falz etwas weniger auf.

Was das Außendisplay betrifft, so ist es fast nicht von dem Bildschirm eines Flaggschiff-Handys im Barren-Format zu unterscheiden, abgesehen davon, dass es ganz leicht schmaler ist als üblich. Es verfügt über schöne, schmale Einfassungen an allen Seiten, eine flinke Bildwiederholfrequenz von 120 Hertz und eine noch höhere Spitzenhelligkeit von 6.000 Nits.

Wie bereits bei den letzten Generationen der faltbaren Honor-Modelle gibt es keine nennenswerten Nachteile bei der Nutzung des zusammengeklappten Telefons; es fühlt sich einfach wie ein normales Smartphone an. Wenn Sie dann einen größeren Bildschirm für Ihre Inhalte, Spiele oder Multitasking benötigen, klappen Sie es einfach auf.

Luke Baker

Wie üblich hat Honor einiges in Funktionen zum Augenschutz investiert, und beide Bildschirme unterstützen eine PWM-Dimmung mit 4.320 Hertz. Das werden Sie besonders zu schätzen wissen, wenn Sie empfindlich auf Flackern reagieren. Beide Bildschirme unterstützen zudem die Eingabe per Stylus, allerdings hatte ich leider keinen Honor-Stylus zur Hand, um dies auszuprobieren.

Die luxuriösen Displays werden durch ein ordentliches Lautsprecherset ergänzt. Das Stereopaar erzeugt eine schöne, breite Klangbühne, kann mehr als laut genug werden und bietet eine ordentliche Basswiedergabe. Sie können zwar nicht mit den bassbetonten Lautsprechern des Magic 8 Pro mithalten, aber bei einem so schlanken Gehäuse sind die Möglichkeiten nun einmal begrenzt.

Technische Daten & Leistung

Qualcomm Snapdragon 8 Elite Gen 5
16 GB RAM
512 GB Speicher

Das Magic V6 verfügt über den leistungsstärksten Chip von Qualcomm, den Snapdragon 8 Elite Gen 5, und das globale Modell ist mit 16 GB RAM und 512 GB Speicher ausgestattet. Bei einem so schlanken Falt-Smartphone stellt die Wärmeableitung stets eine Herausforderung dar, doch Honor hat hier eine großzügig dimensionierte Vapor-Chamber verbaut, um eine optimale Leistung zu gewährleisten.

Luke Baker

Das V6 hat meine täglichen Leistungsanforderungen problemlos erfüllt. Bei einem Falt-Smartphone habe ich oft drei Apps gleichzeitig geöffnet, wechsle zwischen ihnen hin und her und spiele häufig gleichzeitig Musik ab oder streame Videos. Selbst dann kam das Smartphone kaum ins Schwitzen.

Man muss schon ein ziemlich anspruchsvolles Spiel starten, um dieses Smartphone ein wenig ins Schwitzen zu bringen, und wie es der Zufall so will, bin ich ziemlich süchtig nach NTE: Neverness to Everness (ein Open-World-Spiel mit atemberaubender Grafik und einer riesigen Stadtkarte, mit deren Darstellung die meisten Smartphones zu kämpfen haben). Doch das Magic V6 hatte damit kaum Probleme.

Das Spiel lief bei der Grafikvoreinstellung „Extreme“ flüssig mit 60 FPS, und obwohl es sich ziemlich stark erwärmte – insbesondere im Bereich der Kamera –, schien die Leistung darunter nicht zu leiden. Die beste Erfahrung machte ich mit einem GameSir-Clamp-Controller, der die Wärme von meinen Handflächen fernhielt.

Luke Baker

Honor Magic V6 – Benchmark-Ergebnisse

Was die Benchmark-Ergebnisse angeht, wird es recht interessant. Während die meisten Ergebnisse hervorragend waren, hatte das Smartphone erhebliche Schwierigkeiten, den 3DMark Wildlife Extreme Stress Test abzuschließen. Normalerweise führe ich diesen Test bei aufgeklapptem Smartphone durch, doch es überhitzte sich und brach den Benchmark jedes Mal ab, wenn ich es versuchte.

Zugegebenermaßen war es in letzter Zeit recht warm, doch so etwas ist mir bisher noch nie passiert. Bei der Durchführung des Tests auf dem Cover-Display konnte es den Test mit einem ordentlichen Ergebnis abschließen, auf dem klappbaren Bildschirm war dies jedoch nicht möglich. Im alltäglichen Gebrauch hatte ich jedoch nie derartige Probleme.

Kameras

50-MP-Hauptkamera mit f/1,6
64 MP, f/2,5, 3-fach-Tele
50 MP, f/2,2 Ultraweitwinkel
20 MP, f/2,2 Selfie-Kamera

Das Honor Magic V6 verfügt über dieselbe Kamera-Hardware wie die Vorgängergeneration, was bedeutet, dass es keine nennenswerten Neuerungen gibt.

Allerdings verfügte das V5 bereits über eine der beeindruckendsten Kameraausstattungen unter allen faltbaren Smartphones, und die Konkurrenz hat sich in dieser Hinsicht nicht sonderlich ins Zeug gelegt – daher gehört es nach wie vor zu den Besten.

Luke Baker

Auf der Rückseite befindet sich eine 50-MP-Hauptkamera mit einem Sensor von ansehnlicher Größe (1/1,56 Zoll) und einem lichtstarken f/1,6-Objektiv. Hinzu kommen eine 50-MP-Ultraweitwinkelkamera mit einem extrem weiten Sichtfeld (entspricht 13 mm) sowie ein ausgezeichnetes 64-MP-Teleobjektiv. Außerdem gibt es zwei 20-MP-Selfie-Kameras, von denen jeweils eine durch das jeweilige Display ragt.

Dies ist eine äußerst vielseitige Objektivausstattung, mit der Sie alles aufnehmen können – von weitläufigen Landschaften bis zu intimen Porträts. Wie üblich hat mir die Verwendung des Teleobjektivs am meisten Spaß gemacht, und dies könnte die beste Tele-Kamera sein, die jemals in einem faltbaren Smartphone verbaut wurde.

Es verfügt über einen recht großen 1/2-Zoll-Sensor und kann aus sehr kurzer Entfernung fokussieren. Ihr Motiv muss sich lediglich etwa 20 Zentimeter vom Objektiv entfernt befinden, und in Kombination mit der Brennweite von 70 mm (äquivalent) entsteht so eine schöne perspektivische Kompression und ein natürliches Bokeh.

Natürlich können Sie mit einem digitalen Ausschnitt noch weiter zoomen, und die Kamera-App ermöglicht Ihnen auf Wunsch eine bis zu 100-fache Vergrößerung. Bei einer Vergrößerung von mehr als 10-fach können Sie generative KI nutzen, um die Bilder zu bereinigen, doch auch ohne diese Funktion erhalten Sie bereits bei etwa 20-facher Vergrößerung brauchbare Bilder.

Ich habe bereits erwähnt, wie weitwinklig das Ultraweitwinkelobjektiv ist, und das gefällt mir besonders gut daran; ansonsten ist es jedoch im Vergleich zu den anderen Objektiven definitiv ein Qualitätsverlust.

Nachts ist es zudem am wenigsten überzeugend, da Bewegungsunschärfe dort nur schwer zu bewältigen ist. Bei den richtigen Lichtverhältnissen ist es jedoch zu wirklich beeindruckenden Aufnahmen fähig.

Luke Baker

Die Hauptkamera ist wie immer die zuverlässigste des Trios. Dank des größeren Sensors schneidet sie bei schlechten Lichtverhältnissen am besten ab und liefert tagsüber etwas schärfere Bilder als die anderen Kameras.

Die Selfie-Kameras sind nichts Besonderes. Sie erfüllen ihren Zweck und eignen sich für Zoom-Anrufe, doch wenn man auf der Rückseite so beeindruckende Kameras hat, lohnt es sich wirklich, das Smartphone umzudrehen und den Bildschirm auf der Rückseite zu nutzen, um wirklich beeindruckende Fotos zu machen.

Die Bildverarbeitung von Honor kann etwas unbeständig sein, aber ich habe den Eindruck, dass sie immer konsistenter wird, und wenn sie funktioniert, lassen sich wirklich atemberaubende Bilder erzielen.

Mein größter Kritikpunkt ist die Bewegungsunschärfe im Porträtmodus. Ich bin mir nicht sicher, woran es liegt, aber ich erhalte viele unscharfe Aufnahmen, wenn die künstliche Hintergrundunschärfe aktiviert ist – selbst bei guten Lichtverhältnissen.

Ansonsten bin ich mit den Bildern, die ich mit dem Magic V6 aufgenommen habe, sehr zufrieden. Die Harcourt-Porträtmodi sind so beeindruckend wie eh und je, und mit den übrigen Filtern lässt sich der Look Ihrer Fotos ziemlich drastisch verändern. Besonders gut gefällt mir der Filter „Nostalgic Negative“, der einen schönen, kontrastreichen Cross-Entwicklungs-Look mit blauen Schatten erzeugt.

Was die Videoaufnahmen angeht, ist das Angebot etwas weniger umfangreich. Das soll nicht heißen, dass es schlecht ist, aber nachdem ich viel Zeit mit dem Oppo Find N6 verbracht habe, habe ich mich an ein ordentliches Log-Profil, 4K-120-Aufnahmen und Dolby Vision gewöhnt. Nichts davon ist hier vorhanden. Es gibt zwar ein Log-Profil, dieses funktioniert jedoch nur mit dem Hauptsensor und erzeugt ungewöhnlich körnige Schatten.

Wenn Sie jedoch keine ganz so professionellen Ansprüche haben, werden Sie mit der Videoleistung wahrscheinlich sehr zufrieden sein. Sie können mit jeder Kamera bis zu 4K bei 60 FPS aufnehmen, mit Ausnahme der Selfie-Kamera, die maximal 4K bei 30 FPS erreicht. Die Bildstabilisierung ist gut, und auch die Mikrofone sind ordentlich.

Akkulaufzeit & Aufladen

6.660-mAh-Akku
80-Watt-Laden über Kabel
66 Watt kabelloses Laden

Das Honor Magic V6 verfügt über den größten Akku aller faltbaren Smartphones, die ich bisher getestet habe. Irgendwie ist es Honor gelungen, einen 6660-mAh-Akku in dieses hauchdünne Gehäuse zu integrieren. Samsun wird hoffentlich davon lernen.

Diese Kapazität verblasst zwar im Vergleich zur chinesischen 1-TB-Version dieses Smartphones, die über einen erstaunlichen 7.150-mAh-Akku verfügt, doch wie wir in letzter Zeit oft gesehen haben, bedeuten die EU-Vorschriften, dass wir im Westen keinen ganz so massiven Akku erhalten können.

Luke Baker

Das spielt jedoch keine allzu große Rolle, denn dieser Akku ist mehr als gut genug. Obwohl ich den großen internen Bildschirm häufig nutzte und unzählige Fotos machte, reichte die Akkulaufzeit dieses Smartphones während des Großteils meiner Tests für eineinhalb Tage. Natürlich verkürzt intensives Gaming die Laufzeit etwas, aber Sie werden das Gerät selten, wenn überhaupt, vor dem Schlafengehen aufladen müssen.

Auch das Aufladen erfolgt schnell, vorausgesetzt, Sie verfügen über ein ausreichend leistungsstarkes Netzteil (im Lieferumfang ist lediglich ein USB-C-Kabel enthalten), um die 80-Watt-Geschwindigkeit nutzen zu können. Ich konnte das Gerät in nur einer halben Stunde von leer auf fast 70 Prozent aufladen; mehr kann man sich kaum wünschen.

Wenn Sie kabelloses Laden bevorzugen, ist dies ohne nennenswerte Einbußen bei der Geschwindigkeit möglich. Das Honor Magic V6 lässt sich mit einem offiziellen kabellosen Ladepad von Honor mit bis zu 66 Watt aufladen.

Software & Apps

MagicOS 10, basierend auf Android 16
Zahlreiche KI-Funktionen
Kompatibilität mit dem Apple-Ökosystem

Auf dem Magic V6 läuft MagicOS 10, Honors eigene Variante von Android 16. Es handelt sich im Wesentlichen um dieselbe Software, die wir bereits von den letzten Honor-Flaggschiffmodellen kennen. Wenn Sie also bereits eines dieser Modelle ausprobiert haben, werden Sie kaum Überraschungen erleben.

Luke Baker

Die Software von Honor kann die Meinungen spalten. Sie weicht ziemlich stark vom typischen Android-Erlebnis ab; stattdessen erinnert sie zunehmend an iOS. Ob das nun gut ist oder nicht, hängt ganz von Ihren Vorlieben ab.

Allerdings lässt sich die Benutzeroberfläche extrem gut anpassen – wenn Ihnen also das Design oder das Layout einer Funktion nicht gefällt, können Sie es höchstwahrscheinlich ändern.

Insgesamt gefällt sie mir recht gut. Sie wirkt schnell und reaktionsfreudig, sieht standardmäßig ansprechend aus und bietet zahlreiche Werkzeuge zur weiteren Anpassung. Wie bereits erwähnt, gibt es zahlreiche Anlehnungen an iOS, darunter einige „Liquid Glass“-ähnliche Elemente, einen „Dynamic Island“-Klon, geteilte Benachrichtigungen/Schnelleinstellungen sowie die Möglichkeit, die App-Übersicht zu deaktivieren.

Das sind zwar nicht die originellsten Entscheidungen, aber sie funktionieren gut, und die meisten sind vollkommen optional.

Honor bietet Ihnen eine Vielzahl von KI-Funktionen, darunter die üblichen Tools für Transkription, Übersetzung und Zusammenfassung sowie einige sehr fortschrittliche Funktionen zur Fotobearbeitung.

Luke Baker

Auch das Multitasking ist hervorragend. Sie können Apps im Split-View nebeneinander anzeigen und dann eine dritte hinzufügen, die nur am Rand des Bildschirms hervorblitzt und bei Bedarf sofort einsatzbereit ist. Oder, wenn Sie es vorziehen, können Sie schwebende Fenster nutzen und so noch mehr Inhalte gleichzeitig auf dem Bildschirm anzeigen.

Am spannendsten finde ich jedoch die Art und Weise, wie sich das Magic V6 in Apple-Produkte integrieren lässt. Insbesondere, wie gut es mit meinem Macbook zusammenarbeitet. Wenn Sie die Honor Workstation-App aus dem Mac App Store installieren, können Sie Dateien austauschen, zwischen Geräten kopieren und einfügen, Ihr Smartphone fernsteuern und das V6 sogar als drahtlosen zweiten Bildschirm nutzen.

Luke Baker

Doch damit ist der Spaß noch lange nicht vorbei. Das Honor Magic V6 kann auch nativ Dateien in Ihrem iCloud-Konto durchsuchen und verwalten, und mit Honor Connect können Sie Dateien auch per AirDrop zu und von iPhones übertragen. Das ist großartig, wenn Sie ständig zwischen verschiedenen Ökosystemen hin- und herwechseln (wie ich es oft tue).

Insgesamt ist es also ein solides Software-Erlebnis, das jedoch nicht ganz ohne Nachteile ist. Meine größte Kritik an Honor-Smartphones ist seit einigen Jahren unberücksichtigt geblieben. Das Akkumanagement ist extrem aggressiv und beendet standardmäßig Hintergrund-Apps mit rücksichtsloser Härte.

Das bedeutet, dass Sie häufig unter stark verzögerten Benachrichtigungen leiden, bis Sie einige Einstellungen anpassen und sicherstellen, dass diese Apps geöffnet bleiben.

Luke Baker

Das lässt sich zwar leicht beheben, und ich kann die Logik dahinter nachvollziehen – schließlich bieten Honor-Smartphones stets eine hervorragende Akkulaufzeit –, doch es ist äußerst ärgerlich, und ich würde mich sehr freuen, wenn dieses Problem behoben würde.

Honor verspricht sieben Jahre lang Software-Updates und Sicherheitspatches für seine Flaggschiff-Geräte und liegt damit auf einer Stufe mit Samsung und Apple. Viel besser geht es kaum, auch wenn Sie möglicherweise eine Weile auf das auf Android 17 basierende MagicOS 11 warten müssen.

Preis & Verfügbarkeit

Das Honor Magic V6 kann ab sofort in Deutschland und den meisten anderen Ländern der Welt bestellt werden, wobei die USA wie üblich ausgeschlossen sind. Hierzulande liegt der Preis bei stolzen 2.299 Euro, wobei es bis zum 31. Juli noch einen Rabatt von 600 Euro gibt (Endpreis: 1.699,90 Euro).

Als besonderes Einführungsangebot legt Honor auch eine Reihe kostenloser Extras wie ein Tablet, einen Stift und ein Set Earbuds bei. Das macht eine Beurteilung des Preises ziemlich schwierig. Auf dem Papier ist das Magic V6 deutlich teurer als das V5, das im vergangenen Jahr für 1.999 Euro auf den Markt kam. Und das schmerzt, insbesondere wenn man bedenkt, dass es seinem Vorgänger so ähnlich ist. Mit dem Rabatt ist es jedoch günstiger.

Preislich bewegt es sich trotzdem noch in einem ähnlichen Rahmen wie viele andere Falt-Smartphones, die wir getestet haben. Darunter das Motorola Razr Fold, das Samsung Galaxy Z Fold 7 und das Google Pixel 10 Pro Fold.

Sollten Sie das Honor Magic V6 kaufen?

Das Magic V6 ist zweifellos eines der attraktivsten faltbaren Smartphones, die derzeit auf dem Markt erhältlich sind. Die Hardware ist hervorragend, die Bildschirme sind beeindruckend, es ist leistungsstark und die Kameras sind erstklassig. Wenn Sie ein faltbares Smartphone mit großem Bildschirm suchen, aber bei den Kameras keine allzu großen Abstriche machen möchten, ist dies eine hervorragende Wahl.

Allerdings könnten versierte Käufer sich für das Vorjahresmodell entscheiden und für deutlich weniger Geld ein sehr ähnliches Gesamterlebnis erhalten. Es ist nach wie vor extrem leistungsstark und verfügt über dieselben Kameras. Es wird jedoch nicht ewig vorrätig sein.

Man sollte auch bedenken, dass das Galaxy Z Fold 8 und das Z Fold 8 Ultra voraussichtlich im Juli 2026 erscheinen werden, während Apples lang erwartetes faltbares iPhone wahrscheinlich im September vorgestellt wird. Ich persönlich würde mir daher erst einmal ansehen, was diese Modelle zu bieten haben, bevor ich den Sprung wage.

Wenn Sie nicht warten können, ist das Honor Magic V6 eine hervorragende Wahl und wird dies wahrscheinlich auch bleiben. Ich kann mir nicht vorstellen, dass Samsung die Akkukapazität übertreffen wird, und ich bezweifle sehr, dass es in puncto Kameraleistung übertroffen wird. Abgesehen von ein paar kleinen Software-Mängeln ist es ein brillantes Smartphone.

Technische Daten

MagicOS 10, basierend auf Android 16
Außen: 6,52 Zoll, 1.080 x 2.420, OLED, 120 Hz
Innen: 7,95 Zoll, 2172 × 2352, OLED, 120 Hz
Fingerabdrucksensor im Ein-/Aus-Schalter
Qualcomm Snapdragon 8 Elite Gen 5
16 GB RAM
512 GB Speicher
Kamera:
50 MP, f/1,6 Hauptkamera
64 MP, f/2,5, 3-fach-Teleobjektiv
50 MP, f/2,2 Ultraweitwinkel
Doppelte 20-MP-Selfie-Kamera mit f/2,2
Videoaufnahmen mit bis zu 4K bei 60 fps (Rückkamera)
Stereolautsprecher
Dual-SIM
WLAN 802.11 a/b/g/n/ac/6e/7
Bluetooth 6.0
6660-mAh-Akku
80-W-Laden über Kabel
66 W kabelloses Laden
156,7 × 74,5 × 8,8 mm (zusammengeklappt)
IP69-zertifiziert
219 g
Farben: Gold, Rot, Weiß, Schwarz

(PC-Welt)

Ultrahuman Ring Pro im Test: Ohne Abonnement und langlebig

Sat, 25 Jul 2026 18:59:22 +0200

Mike Sawh

Auf einen Blick

Pro

Insgesamt solide Tracking-Leistung
Ansprechende Begleit-App mit einigen nützlichen Modi
Beeindruckende Akkulaufzeit
Ohne Abonnement

Kontra

Hoher Preis
Klobiges Design
Softwarefunktionen entsprechen weitgehend denen des Air

Fazit

Der Ultrahuman Ring Pro bietet ein hervorragendes Hardware- und Software-Erlebnis und zählt damit zu den besten Smart-Ringen. Das Problem ist der hohe Anschaffungspreis, ganz zu schweigen davon, dass die Konkurrenz – sowohl bei Modellen mit als auch ohne Abonnement – für manche Nutzer attraktivere Eigenschaften bieten kann.

Der Ultrahuman Ring Pro ist der neueste Smart-Ring eines Unternehmens, das sich seit Langem im Konflikt mit Oura befindet. Nachdem der Verkauf seines Vorgängermodells in den USA vorübergehend untersagt worden war, kehrt Ultrahuman nun mit einem neuen Ring zurück, der über neu gestaltete Sensoren, eine verbesserte Prozessorleistung und eine längere Akkulaufzeit verfügt.

Der Ring Air ist weiterhin als günstigere Alternative zum Pro erhältlich, doch wenn Sie das Beste wollen, was Ultrahuman zu bieten hat, ist dieser hier die richtige Wahl.

Leider sind die Preise für die Ringe von Ultrahuman – ähnlich wie bei dem ebenfalls abonnementfreien Konkurrenten RingConn – leicht gestiegen, was bedeutet, dass sich der Pro wirklich hervorheben muss, um die höheren Kosten zu rechtfertigen und zu einer der ersten Wahl unter den Smart-Ringen zu werden.

Design & Verarbeitung

Erhältlich in vier Farbvarianten
Dickeres Design als der Ring Air
Ladeetui im Lieferumfang enthalten

Der Ring Pro ist ein Smart-Ring mit einem schlichten Design, der in vier verschiedenen Farben erhältlich ist: Bionic Gold, Space Silver, Aster Black und Raw Titanium. Der Kern des Rings besteht aus Titan mit einer PVD-Beschichtung, die ihn vor Kratzern schützt. Ich habe festgestellt, dass frühere Ultrahuman-Ringe zu den am leichtesten zu zerkratzenden gehörten. Daher freue ich mich, dass der Pro Kratzer besser abwehrt.

Mike Sawh

Genau wie der Air ist auch der Pro in den Ringgrößen 5–14 erhältlich und bis zu einer Tiefe von 100 Metern wasserdicht. Ich habe mich für dieselbe Größe wie beim Air entschieden, und die Passform scheint ähnlich zu sein, wenn nicht sogar insgesamt etwas besser, da der Ring seltener an meinem Finger herumrutscht. Er ist sehr bequem und sitzt gut am Finger. Man spürt zwar die Sensoren, doch sie sind nicht so deutlich spürbar wie bei früheren Modellen.

Im Vergleich zum Ring Air von Ultrahuman erhalten Sie einen schwereren und dickeren Ring. Da ich den Oura Ring 5 gleichzeitig getragen habe, wirkt der Pro deutlich größer als der neueste Ring von Oura.

Der Pro wird mit einem Ladecase geliefert, und obwohl man es nicht mit dem neuen Case von Oura verwechseln würde, verfügt es über ein ähnlich robustes Metall-Design, das den Ring schützt, wenn er nicht am Finger getragen wird. Das Etui verfügt über zusätzliche intelligente Funktionen wie kabelloses Laden, einen „Find-my-Case“-Modus für den Fall, dass Sie es verlegen, ganz zu schweigen von der Möglichkeit, Ringdaten bis zu einem Jahr lang zu speichern.

Ein interessanter Aspekt des Designs ist, dass Ultrahuman den Ring so konzipiert hat, dass er im Falle einer Schwellung leicht durchtrennt und entfernt werden kann. Auch wenn ich hoffe, dass niemand jemals in eine solche Situation gerät, ist es beruhigend zu wissen, dass sich der Ring in einem Notfall problemlos entfernen lässt.

Fitness & Tracking

Überarbeitete Temperatur- und Herzfrequenzsensoren
Neuer Dual-Core-Prozessor
Powerplugs bieten zusätzliche Funktionen gegen Aufpreis

Der Ring Pro kann so gut wie alles überwachen, was auch der Air kann. Dazu gehören Herzfrequenz, Schlaf, Stress, Temperatur und die tägliche Schrittzahl. Die größte Änderung besteht darin, dass die optischen Sensoren, die zur Erfassung dieser Messwerte verwendet werden, überarbeitet wurden und nun klarere Signale liefern, um Schlaf- und Erholungsdaten zu erfassen.

Diese Neugestaltung scheint zudem mit den Patentstreitigkeiten mit Oura in Zusammenhang zu stehen.

Mike Sawh

Die App, die diese Daten anzeigt, gehört zu den ausgereiftesten, die Sie bei einem Smart-Ring finden können. Sie steht der Oura-App in nichts nach, was die ansprechende Aufbereitung Ihrer Daten angeht, und regt Sie dazu an, sich tatsächlich damit auseinanderzusetzen.

Mike Sawh

Was die Erfassung der Kerndaten angeht, müssen Sie die zweiwöchige Kalibrierungsphase unbedingt durchlaufen, bis Sie zuverlässige Ergebnisse erhalten. Die Daten zu den durchschnittlichen Herzfrequenzwerten wiesen während dieses Zeitraums erhebliche Abweichungen auf, stabilisierten sich jedoch nach diesen zwei Wochen.

Die Daten zur Ruheherzfrequenz und die Messungen der Herzfrequenzvariabilität stimmten besonders gut mit zwei anderen Trackern überein, die ich parallel zum Pro trug.

Bei der Betrachtung der Schrittzahlen stellte ich fest, dass die gemeldeten Gesamtwerte deutlich niedriger waren als bei zwei anderen Trackern, mit denen ich den Pro verglichen habe.

Die Leistung bei der Schlafaufzeichnung gehört zu den besten, die ich getestet habe, einschließlich Oura. Was die Schlafdauer, die Aufschlüsselung der Schlafphasen und den erfassten Zeitpunkt des Einschlafens betrifft, lieferte der Pro zuverlässige Werte.

Mike Sawh

Ultrahuman hebt sich von der Konkurrenz dadurch ab, dass es komplexe Daten in einem leicht verständlichen Format darstellt, beispielsweise bei der Bewertung des Gehirnalters oder der Erfassung des Schlafdefizits, das sich aus kumulierten schlechten Nächten ergibt. Es überwacht zudem, wie gut Ihr Gehirn während des Schlafs Abfallstoffe abbaut. Sie können auch die „PowerPlugs“ von Ultrahuman erkunden, bei denen es sich größtenteils um kostenlose Add-ons handelt, die eine individuellere Nachverfolgung ermöglichen.

Ich nutze derzeit das neue Parent-Modul, das besonders auf die kumulative Erholung achtet, berücksichtigt, dass der Schlaf wahrscheinlich unruhiger ist, und Ihnen Tipps gibt, wie Sie wieder in die richtige Bahn kommen können.

Mike Sawh

Wie bereits erwähnt, sind die meisten dieser PowerPlugs kostenlos. Es gibt jedoch auch solche, die Einblicke in spezielle Krankheiten wie Migräne bieten oder Daten mit einem Tesla synchronisieren; hierfür ist ein monatliches Abonnement erforderlich. Dabei handelt es sich um kostenpflichtige Funktionen, auf die die meisten Nutzer gut verzichten können.

Akkulaufzeit & Aufladen

Bis zu 15 Tage Akkulaufzeit
Bietet drei Akkubetriebsmodi
Ladeetui sorgt für weitere 45 Tage

Die Akkulaufzeit ist ein wichtiges Thema beim Pro-Modell – und das nicht nur, weil er länger durchhält als der Air. Neben der Verlängerung der Akkulaufzeit von 4–6 Tagen auf 15 Tage stehen Ihnen drei Akkubetriebsmodi zur Verfügung, mit denen Sie das Beste aus jeder Ladung herausholen können.

Wenn Sie den Turbo-Modus wählen, bei dem alle Sensoren aktiviert sind, können Sie mit einer Laufzeit von bis zu 12 Tagen rechnen. Diese verlängert sich auf über 15 Tage, wenn Sie sich für den Chill-Akkumodus entscheiden. Dabei liegt der Schwerpunkt auf der Schlafaufzeichnung, doch wichtige Momente Ihres Tages werden weiterhin erfasst, um sicherzustellen, dass die wesentlichen Erkenntnisse weiterhin von Nutzen sind.

Ich habe mit dem Pro problemlos eine Akkulaufzeit von fast zwei Wochen erreicht, was eine beeindruckende Leistung ist. Das ist besser als beim Oura Ring 5 und liegt in Bezug auf die Akkuleistung auf Augenhöhe mit dem RingConn Gen 2.

Zudem verfügen Sie nun über das Ladecase, das Ihnen eine zusätzliche Akkulaufzeit von 45 Tagen bietet. Sie müssen den Ring jedoch präzise im Ladegerät platzieren; ein akustisches Signal aus dem integrierten Lautsprecher bestätigt, dass der Ring wieder aufgeladen wird. Als der Akku auf 0 Prozent sank, benötigte der Pro weniger als eine Stunde, um wieder auf 100 Prozent zu kommen.

Preis & Verfügbarkeit

Zum Zeitpunkt der Erstellung dieses Artikels kann der Ultrahuman Ring Pro für 499 Euro im Ultrahuman Store vorbestellt werden. Damit gehört er zu den teuersten Smart-Ringen auf dem Markt.

Er ist teurer als das günstigste verfügbare Modell des Oura Ring 5 und andere Smart-Ringe wie der Samsung Galaxy Ring. Der in Kürze erscheinende RingConn Gen 3 wird in den USA teurer sein als der Ring Pro, sollte in anderen Regionen jedoch günstiger sein.

Wie der Ring Air bleibt auch der Pro ein Smart-Ring ohne Abonnement. Einige der PowerPlug-Software-Erweiterungen sind jedoch mit zusätzlichen Kosten verbunden.

Sollten Sie den Ultrahuman Ring Pro kaufen?

Die großen Verbesserungen des Ring Pro gegenüber dem Air liegen in dem größeren Akku und dem robusteren Design. Ich habe die Überwachungsleistung des Air nie als unzureichend empfunden, und wenn Sie noch ein Exemplar ergattern können und ein Fan des Software-Ansatzes von Ultrahuman sind, dann ist er nach wie vor eine kluge (und günstigere) Anschaffung.

Vergleicht man den Ring Pro mit anderen Smart-Ringen, müssen wir über den Preis sprechen. Er ist teuer, und es gibt abonnementsfreie Alternativen zu einem günstigeren Preis. Ob diese Ihnen auf der Softwareseite das gleiche Maß an Sorgfalt und Aufmerksamkeit bieten, ist fraglich. Genau hier setzt sich der Ring Pro gegenüber einem Großteil der Konkurrenz durch.

Er ist vielleicht nicht der kleinste oder dünnste Ring, aber was der Ring Pro zu bieten hat, ist eine Kombination aus Hardware und Software, die ein hervorragendes Gesamtpaket ergibt.

Technische Daten

Bis zu 15 Tage Akkulaufzeit
Kompatibel mit Android und iOS
Wasserdicht bis zu 100 Metern
2,65 mm dick
Gewicht: 3,3–4,8 g
Erfasst den Blutsauerstoffgehalt, die Herzfrequenz und die Körpertemperatur
Erfasst den Schlaf und die tägliche Aktivität

(PC-Welt)

Können wir ohne Arbeit leben? | 42 - Die Antwort auf fast alles | ARTE

Sat, 25 Jul 2026 18:59:16 +0200

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The Mythos Race: Trump’s New EO and Glasswing’s Expansion

Sat, 25 Jul 2026 18:57:47 +0200

A roundup of headline AI developments from this past week is warranted, as fast-moving decisions from the White House to Anthropic demand immediate attention. Plus, a look at what may be the AI metric that matters most.

The new value architecture of the AI-native SaaS era

Sat, 25 Jul 2026 18:55:51 +0200

The traditional methods of measuring success no longer tell the full story. Here’s what should replace them — and why.

In brief:

AI is transforming software as a service (SaaS), and the old ways of keeping score no longer apply.
Smart companies are evolving new metrics that provide deeper insight into how AI-native software is performing in a new marketplace.
These changes impact everything from pricing to valuations.

The transformation of the software-as-a-service (SaaS) industry toward AI-native operating companies is rapidly changing the unit of value across the industry.

The traditional metric of seats — which measured access — is rapidly giving way to credits designed to measure work performed. This evolution is upending the industry in multiple ways, impacting everything from pricing to enterprise valuations.

While many companies still cling to seat-based metrics to measure growth, efficiency and durability, the future is likely to be one in which companies utilize a credit-centric metrics framework, with seats and outcomes as the bookends of a spectrum.

Why do software companies need new metrics?

Why the rethink, and why now? There are five major forces that are driving this shift:

The unit of value is changing. Seats measured who could access software, and credits measure what the software actually does. But in an AI-native world, agents don’t have seats; they have workloads. Over the past 18 months, every major SaaS platform has moved to some forms of credit or consumption unit.
The cost of goods sold (COGS) is exploding. AI inference adds real per-unit costs that scale with usage. In an AI-native world, software companies can’t scale to infinite users at near‑zero marginal cost as before.
Buying is moving up the org chart. AI-native applications shift purchasing to higher-level operators — such as line-of-business leaders or chief operating officers — which expands the market from software budgets to labor budgets. And because AI agents replace services as well as software, the total market opportunity is 3x to 10x larger than traditional SaaS.
Time to value (TTV) is collapsing. With AI-native tools, customers start seeing meaningful results in weeks rather than quarters. Onboarding and setup are fast, workflows are pre-built, and there’s no need for extensive customer success or professional services — dramatically reducing implementation time and costs.
Retention is bifurcating. AI forces clarity in a way that traditional SaaS couldn’t. Products that can provide value become even “stickier” and retain customers. Those that don’t churn faster. In an AI-native marketplace, the middle disappears.

How this shift is impacting pricing

Given how AI-native software is transforming the market, the shift to more variable pricing options is inevitable.

Seats won’t go away completely. Subscription pricing based on the number of users is stable and predictable and will continue to work for some customers. Tokens — the use of pass-through pricing for underlying compute — will fit those customers where the AI feature is commoditized or the buyer wants transparency into costs.

Credits will likely become the dominant architecture because they provide a simple metric for both customers and providers. The vendor sets the conversation ratio between credits and underlying compute, shielding the customer from inference cost details. Credits are easy to understand and can be packaged into annual contracts for multiple features and products.

Finally, the industry will likely see some move toward outcome-based pricing for results such as resolved tickets, recovered revenue or qualified leads. This strategy will mostly be limited to verticals where it is easy to prove AI impacted the result.

Where a software vendor sits on this spectrum is a signal of differentiation and pricing power. Credits are where most defensible AI-native businesses are landing because they balance customer predictability with vendor margin control.

How AI upends classic SaaS metrics

When SaaS was in its infancy, companies settled on key metrics designed to answer a small set of core questions. Are we growing? Are customers using the product? Are we retaining and expanding accounts?

But as AI upends software itself, it is also requiring companies to adopt new metrics to track success. These new metrics fall into three primary buckets, rebuilt around the pricing spectrum described earlier and the trend toward credits as the primary frame:

Revenue composition

Committed credit annual recurring revenue (ARR) vs. burndown ARR: Measuring the credits sold on annual commitment vs. those consumed and replenished. This is the single most important split for valuation. Committed credits behave like subscription and burndown behaves like usage.
Credit utilization rate: The percentage of purchased credits consumed per period. This is a leading indicator of renewal sizing.
Credit burn velocity: How fast is a customer consuming their credits, and is that consumption increasing or decreasing quarter over quarter? This metric predicts expansion or contraction before it shows up in ARR.
Effective price per credit: The real revenue per credit after discounts, overage and rollover, which can detect revenue leakage and help companies set smarter guide rails.

Margin reality

Credit margin: The gross profit the company earns per credit after subtracting inference costs. This is the core economic unit for AI-native, usage-based businesses — the replacement for gross margin per seat used in SaaS.
Inference-adjusted gross margin: By carving out AI inference costs separately in the P&L statement, you can see true AI margins, avoid hiding deterioration inside blended SaaS margins, and clearly distinguish AI economics from legacy SaaS economics.
Compute leverage ratio: This metric measures how efficiently the business converts compute spend into revenue. It shows whether your AI margins are improving as you scale.
AI-adjusted “Rule of 40”: This updated metric recalibrates the traditional growth and profitability benchmark to account for AI’s lower gross margins and variable inference costs, giving a more accurate picture of business health for AI-native companies.

Behavioral and value signals

Time-to-first outcome: Replaces traditional onboarding metrics. Tracks how fast a customer reaches their first measurable result.
Adoption: AI-native adoption is measured by workflow penetration and active agent density, not seat count. As AI replaces human-driven usage, the unit of adoption shifts from people to automated workflows and agents.
Net credit retention (NCR): Credit-volume retention across the customer base, tracked separately from net recurring revenue to avoid price-change impact.

Along with these new metrics, the industry’s transformation is prompting companies to retire or recalibrate old SaaS measures, including per-seat ARR as a primary key performance indicator (KPI), traditional magic number calibrated to subscription dynamics, unadjusted Rule of 40, customer success metrics tied to human touchpoints, and blended gross margin without AI COGS carve-outs.

What does this mean for enterprise value calculations?

As the internal metrics of success change, so do the ways the investment community measures growth and long-term viability.

Increasingly, a company’s valuation multiple depends on whether its revenue behaves like committed subscription ARR or volatile usage ARR, and the commit‑to‑burndown ratio is the metric investors use to decide where the company fits.

For example, a business with 80% committed credit ARR could trade closer to subscription comps and one with 80% burndown could trade closer to usage comps even though both have the same types of customers. Being able to proactively explain the commit‑to‑burndown mix can help companies avoid undervaluation.

In addition, utilization is expected to replace net promoter scores and seat usage as the primary predictor of churn or expansion. Low utilization guarantees downsizing at renewal, so companies must track utilization cohorts the same way SaaS tracks logo retention cohorts today.

We’re also seeing an inversion of the operating model, with R&D and COGS moving up the P&L and sales and marketing (S&M) and customer success (CS) moving down or sideways. The net operating leverage profile is structurally different from classical SaaS, and the cost-to-scale curve looks different too.

Finally, credit margin engineering is a hidden value-creation lever. The gap between price per credit and cost per credit is set by the software vendor and can be optimized. Most operators have barely started managing this rigorously, and the ones who do will pull away on margin.

What this means for leaders, boards and investors

The shift from classic SaaS metrics to new AI‑native measures isn’t cosmetic. It represents the seismic change the industry is experiencing as AI matures and transforms products and organizations.

While these metrics — and perhaps others yet to be determined — may evolve over time, there is no doubt they are already changing how AI companies allocate capital, price products, incent sales teams, evaluate performance and communicate with investors.

It’s important to remember that SaaS metrics were practical tools for a specific era of software. As that era draws to a close, winning companies will choose new metrics that shape behavior and drive smart decision-making.

The views reflected in this article are the views of the author and do not necessarily reflect the views of Ernst & Young LLP or other members of the global EY organization.

This article is published as part of the Foundry Expert Contributor Network.
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How to navigate the AI talent wars

Sat, 25 Jul 2026 18:55:50 +0200

Cloudflare recently beat Q1 2026 earnings. Revenue up 34% year over year. EPS ahead of consensus. Full-year guidance raised. Then, in the same breath, they announced 1,100 layoffs, 20% of the company. CEO Matthew Prince’s explanation: “The way we work at Cloudflare has fundamentally changed.”

Block did the same thing. Beat guidance, raised outlook, cut 4,000+ jobs. Both framed it as architecting for the AI era.

This is not a contradiction. This is the new math boards are running. And if you’re a CIO who hasn’t started running it yourself, you’re behind.

The benchmark has moved

AI-native companies have quietly reset what “efficient” means for a technology organization. Midjourney generates over $500M in revenue with roughly 160 employees, over $3M per head. Anthropic hit a $14B annualized run rate in early 2026 with fewer than 3,000 employees. Across the top AI-native startups, the average revenue per employee is $3.48M, nearly twelve times the traditional SaaS benchmark of $300K.

Boards aren’t comparing you to your 2019 self anymore. They’re comparing you to Anthropic.

This is the pressure Cloudflare and Block are responding to. They’re not cutting people because the business is struggling. They’re cutting because investors have internalized a new denominator. Headcount is no longer a proxy for capacity; it’s a liability on the efficiency ratio.

For CIOs, this creates a hiring problem that looks nothing like the cloud or mobile talent gaps of the past decade. Those gaps were about volume: hire 100 cloud engineers, absorb the cost, build the capability… This one is about density; you’re not looking for 100 people. You’re looking for 10 who can deliver what 100 couldn’t, and justify $1M or more in value per seat.

Finding bodies to fill seats has never been easier. Finding people who operate at that level of leverage is a different problem entirely.

‘Acqui-hires’ are a shortcut with a hidden cost

Companies have figured out that recruiting AI-native talent one by one is too slow and that it’s faster to buy a team. Google’s acquisition of the Windsurf founders, Meta bringing in the Scale AI team, Accenture’s string of AI-focused acquisitions: these are acqui-hires dressed up as M&A. The premium on experienced AI talent is high enough, and the urgency real enough, that organizations are skipping traditional hiring loops entirely and buying their way in.

I’ve been on the other side of this. My company, MadKudu, was acquired by HG Insights specifically to bring AI-native capability into an established enterprise business. HG needed change agents who had already figured out how to build and ship in this new era, not just people who’d read about it. That’s the thesis behind most of these deals.

But there’s a cost that doesn’t show up in the acquisition price.

AI-native teams are fast because they operate with a different set of defaults: full access to tools, minimal governance layers, the ability to experiment and ship without a six-week approval cycle. That operating model is not a perk; it’s the fundamental mechanism. It’s why a team of 10 can do what an enterprise team of 100 can’t.

When you acqui-hire that team and then slot them into your existing approval processes, you’ve bought the people and killed the engine. The change agents you paid for become change-frustrated. The attrition that follows is expensive and predictable.

The harder realization: acquiring an AI-native team means accepting how they work. That requires deliberately carving out space for them to operate differently, not just tolerating it but institutionalizing it. The acquisition is an organizational change program, not just a hiring event.

The CIO’s real problem

The governance stack most enterprise organizations run was designed for a headcount world. Every tool vetting cycle, every vendor review, every security approval was calibrated assuming you were managing a large team where consistency and control were the primary objectives.

That calculus breaks when your goal is talent density. The same approval processes that protect against data leaks are now the reason your best people can’t do their best work. When it takes six weeks to approve a tool that your competitor’s team is already shipping with, you’ve traded velocity for the perception of safety.

The practical fix is structured experimentation: clear guardrails, defined boundaries, but explicit permission to try tools before deciding whether to roll them out broadly. Gating everything prevents you from ever discovering what 10x productivity looks like.

The skills inventory question is also more nuanced than it sounds. Job titles won’t tell you where the leverage is. You need to map the actual tasks within each function and assess which can be automated or augmented with AI. That’s where you find the people who, with the right tools, become your $1M/employee talent, not because you hired differently, but because you enabled better.

This is also where the build-versus-buy question gets genuinely tricky. As AI reshapes how products are built and delivered, your internal operating model — how you work, how fast you ship, how you use data — is becoming core IP. Outsourcing delivery means outsourcing the part of the organization where your competitive advantage is now being built.

Closing the gap without slowing down

The AI talent wars are not primarily a recruiting problem. They’re a rethinking of what organizations are supposed to look like.

Boards have a new benchmark. Cloudflare, Block, Amazon, Meta and others have already started restructuring to meet it, publicly, painfully, even while beating their numbers. The question for CIOs isn’t whether this pressure arrives; it’s whether you’re ahead of it or behind it when it does.

The organizations that navigate this well won’t win by outbidding competitors for a handful of elite engineers. They’ll win by designing operating systems that amplify the leverage of the talent they do have, by enabling their best people rather than constraining them, and by treating AI fluency as a core organizational capability rather than a niche specialization.

Talent density is the new headcount model. The sooner your governance, your tooling and your board conversations reflect that, the better positioned you’ll be when the next efficiency report lands.

This article is published as part of the Foundry Expert Contributor Network.
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Model Context Protocol is going stateless to make scaling simpler

Sat, 25 Jul 2026 18:55:48 +0200

Model Context Protocol (MCP), the emerging standard for connecting AI models to external tools and enterprise data, is undergoing its biggest architectural overhaul yet.

The latest release candidate, scheduled for release on July 28, removes protocol-level sessions in favor of a stateless architecture, a change which industry experts say is intended to make MCP easier to deploy across standard cloud infrastructure as enterprises move AI pilots into production.

“The session-based model made sense when MCP servers were local processes on a developer’s laptop. In production, it became an operational tax,” said Muskan Bandta, cloud associate at ZopDev.

“When your infrastructure team asks whether MCP services can scale like other cloud applications, the answer used to be ‘not quite.’ With the move to a stateless architecture, the answer is now yes,” Bandta added.

Earlier versions of the protocol maintained information about every client connection, meaning servers had to keep track of each session throughout an interaction. While that approach worked well for local development, it complicated deployments across multiple servers because requests often had to be routed back to the same machine, limiting scalability and making MCP a less natural fit for modern cloud architectures.

“Under the new stateless design, every request contains the information needed for any available server to process it independently. Applications that need to maintain context across multiple requests can still do so, but developers must now manage that state explicitly rather than relying on the protocol itself,” she said.

This transition to a stateless design goes beyond simplifying infrastructure by fundamentally changing how AI applications manage and share context across tools, according to Amit Jena, AI development manager at IT consulting firm Kanerika.

Instead of keeping application state hidden inside protocol sessions, the new design makes it explicit, allowing AI models to access, reason over, and pass that information between tools, giving developers greater control over how context is preserved and shared across tools, Jena said.

It should also make AI workflows more portable, resilient, and easier to orchestrate across distributed environments, he said.

MCP’s new features

Other changes to MCP include the addition of a Multi Round-Trip Requests (MRTR) mechanism that changes how AI agents request additional information they need to complete a task.

Instead of relying on a persistent connection between the client and server throughout the interaction, the new mechanism lets the server request additional input through a standard request-response exchange before continuing the task, Jena said.

Routable transport headers, another addition, enable API gateways and other networking infrastructure to identify and route MCP requests without inspecting their contents.

They reduce processing overhead, lower latency, and let enterprise teams enforce routing, rate-limiting and security policies more efficiently using existing API management infrastructure, Jena said.

MCP is also getting an updated authorization framework built around OAuth 2.1 and OpenID Connect; interactive MCP Apps; and deterministic caching of tool and resource listings to improve LLM prompt-cache hit rates, potentially saving on token costs.

Rebuilding the trust boundary

The MCP release steering committee also decided to deprecate some legacy features, including Roots, Sampling, Logging, the older HTTP+SSE transport and Dynamic Client Registration, although these will continue to work in this version and any other released over the next year.

The deprecation of Sampling is likely to have the biggest impact because it changes who is responsible for interacting with foundation models, said Jena.

“Sampling let MCP servers invoke the LLM through the client, which meant the server had a callback path into the model without owning that connection. Deprecating it means rebuilding that trust boundary,” Jena said. “Your server now calls the model provider directly. That changes your network architecture, your auth model, and depending on how you’ve built cost attribution, your billing flow.”

The year-long transition period will be enough for teams to audit their sampling dependencies now, said Jena: “The risk is that teams who haven’t implemented sampling themselves won’t know if a third-party MCP server they’re depending on uses it.”

Updated MCP SDKs

To accompany the protocol update, there are updated MCP SDKs for Python, Typescript, Go, and C#. These support both the old and new protocol versions, so new clients can continue communicating with older servers, while updated servers will also support older clients, reducing the risk of immediate disruptions.

That backward compatibility should make the transition largely incremental, except for enterprises that built custom infrastructure around MCP’s earlier session-based architecture, Bandta said.

Identifying and auditing those session dependencies may not be easy, Jena warned.

“Session management complexity tends to be hidden across multiple layers — the gateway config, the deployment scripts, the monitoring dashboards. The code change is small; finding everywhere the assumption lives is what takes time,” he said.

This article first appeared on InfoWorld.

Getting a grip on shadow tokens and AI blowouts

Sat, 25 Jul 2026 18:55:48 +0200

Four months of Claude Code — that’s all it took for Uber to burn through its entire annual budget for AI. Token after token, engineers embraced the platform with few control mechanisms tying costs to outcomes. The result was a budget runaway and a clear case study in how limited oversight snowballs into an AI blowout.

This is a phenomenon I like to call “shadow tokens” — AI credits paid for by the company but largely invisible to decision-makers. Too many engineers have the final say over how much they consume and, therefore, what it costs. This all-you-can-eat attitude is part of the reason why Microsoft is reportedly winding down many internal licenses across key engineering teams and why one in five organizations is missing its AI spend forecast by more than 50%.

And the trend is only accelerating. By 2028, Gartner predicts that AI coding costs (driven by this kind of ungoverned consumption) will be as much per developer as the salary companies pay that person.

LLMs and agents introduce a new class of variable cost that scales with behavior rather than headcount, putting enterprises on the hook for tools that balloon with workload. I don’t see this as enterprises overspending because they’re reckless — it’s down to a lack of managerial oversight, budget alignment that demands a proven return on investment, and engineer education on how much is too much.

Going forward, CIOs need to thread the AI needle between governance that encourages transparency and reasonable spend without stifling innovation.

When shadow tokens result in real costs

The issue is that AI isn’t a traditional line item. Previously, enterprise leaders onboarded software-as-a-service (SaaS) with a good idea of the total cost. An allocated software seat or annual contract was a known quantity. The cloud added some variation (with fluctuations depending on hosting size), but instances were still modelable. AI flips this status quo on its head — the unit of consumption is behavior and the cost is exponential.

And these specifics aren’t immediately apparent at pilot. Tools can appear inexpensive in controlled experiments yet unpredictably scale depending on session length, context window size, model selection and whether agents run in parallel. This is the fallacy of the $20-per-seat enterprise plan — tokens are charged separately at API rates with no ceiling. The final dollar value of any session is set by factors that finance can’t always model in advance, particularly when these decisions usually rest with the engineers themselves.

According to Deloitte, only 21% of organizations deploying agents have a mature governance model, a real concern because they’re token-eating machines. This is what was happening at Uber — Claude Code in agentic mode was autonomously reading codebases, planning changes across dozens of files and opening pull requests. Each step quickly adds up, with Anthropic’s own documentation noting that agents consume approximately seven times as many tokens as standard sessions.

This is shadow IT and shadow AI, evolved. This time, however, many leaders approved the tool in question without guardrails governing consumption. AI hype adds fuel to the fire and normalizes long sessions. Uber’s CTO, for example, described a company-wide shift toward “agentic software engineering” with employees “who are quietly experimenting, quietly shipping and quietly pushing things forward”. This is an exciting way to test the limits of what’s possible, certainly, but it’s also a position that goes a long way to explaining how the company spent its annual AI budget by April.

Shifting the culture from usage to yield

Engineers haven’t done anything wrong here. In fact, they’re adopting and experimenting as instructed, with Uber creating leaderboards and ranking users by token consumption. More use led to a better ranking, reflecting a culture that lauds new ways of doing things. This behavior is known as “tokenmaxxing,” and its principal knock-on effect is shadow tokens — quantity-over-quality processes that leaders struggle to control until they’re fully realized in the budget. Of course, if management treats adoption metrics as performance metrics, then engineers can’t be blamed for using more tokens. The tension is that the teams driving adoption aren’t the ones managing spend.

None of this is meant to dismiss AI’s productivity possibilities and potential return on investment. Developers save 3.6 hours per week, achieve 60% higher pull request throughput and cut onboarding time in half with automation. Meanwhile, Uber shared that roughly 11% of live backend updates were written by agents with no human in the loop. However, these wins aren’t the problem — it’s that too many teams aren’t connecting input to output. I’ve spoken to admins who discovered their token spend had tripled in a single quarter after using heavier models or accidentally doubling up on agentic applications. Nobody knew until the financial damage was done.

Automation needs to happen sustainably with an eye on the bottom line. In my view, a much better metric for achieving this is AI yield — the measurable business or engineering output generated per dollar spent on tokens. Otherwise, without a feedback loop, even genuinely productive teams are flying blind.

Stopping token waste before an AI blowout

Creating that throughline between AI investment and token consumption starts with established financial metrics. This is possible via maximum spend limits (dictated by spend tagging, workload tiering and cost-per-output benchmarks) per team or project. Then, any additional allocation requires approval, closing the loop between the engineers spending the tokens and the leaders paying for them. AI isn’t cheap and teams should demonstrate a bang for their buck.

This is something we do with our engineering team at Hexnode. Resource allocation for Claude Code and Cursor is tied directly to ROI rather than letting consumption run open-ended. Given the pay-as-you-go nature of these tools, a firm usage limit per team offers simple but essential control.

Similarly, there’s room to apply some of the governance principles IT uses for device management. Things like policy enforcement, role-based access, real-time monitoring and automated alerts can flag usage behavior in advance. Uncovering such insights at the token layer works to identify power users and prevent excessive spending.

We also need to encourage cultures that praise outputs that actually achieve efficiency. AI applications that result in shipping faster, reducing rework and cutting review cycles are gains that should be celebrated. If your company hosts leaderboards, frame unnecessary token burn as wasteful rather than valuable. The organizations creating healthier consumption habits work with their engineers to understand not just how to use AI, but what responsible use looks like and what it costs.

This is a conversation teams need to have now. Anthropic just ended flat-rate pricing for programmatic workloads from June 15. Now, agents, continuous integration pipelines and automated workflows draw from a dedicated monthly credit pool billed separately from the subscription. Once that pool is exhausted, agent tasks either stop entirely or overflow to extra billing. Work can either get very expensive or grind to a halt for teams that aren’t prepared.

Getting a grip on shadow tokens means better rules and tools connecting spend to outcomes. Only by building the financial and cultural infrastructure that encourages sustainable adoption can leaders see what they’re spending, connect it to what they’re getting and course-correct before the costs become a crisis. Ultimately, shadow tokens are only invisible if we choose not to look.

This article is published as part of the Foundry Expert Contributor Network.
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5 endpoint blind spots your EDR/XDR was never built to see

Sat, 25 Jul 2026 18:55:47 +0200

In August 2025, 126 malicious packages landed in the npm registry. Even after the community caught the initial wave, 80 of these hidden backdoors remained actively listed.

That was enough. Over 86,000 downloads. Malicious code in PhantomRaven, packages running in the production systems of Fortune 500 companies worldwide. And throughout the entire window, not a single EDR/XDR alert.

This happened because the attack surface has expanded to a layer EDR/XDR was never designed to see: VS Code extensions, local MCP servers, and rogue AI coding assistants that inherit your engineers’ valid credentials to steal data at machine speed.

To eliminate this structural vulnerability, Palo Alto Networks acquired Koi, an AI-native developer security product engineered for proactive, precision enforcement. Below we compiled a 2026 CISO checklist you can use to audit your environment and see how Koi automates each defense from day one.

#1. Gain real-time visibility into shadow AI & extensions

Your existing asset management tracks binaries and installers, but it cannot see local VS Code extensions, MCP servers, or ad-hoc Python scripts running on developer endpoints. This visibility gap was recently exposed by the MaliciousCorgi campaign, where two marketplace extensions with 1.5 million combined installs silently harvested every file a developer opened. Neither triggered any detection because they were not binaries, not executables, not anything your inventory was built to flag. To counter this, Koi closes the gap by analyzing what extensions actually do after installation, exposing hidden data-harvesting channels running inside your active workspace.

#2. Distinguish between human and autonomous agent behavior

When a rogue AI agent exfiltrates your proprietary source code, it uses a developer’s valid credentials during normal working hours, making the session look entirely legitimate to standard XDR baselines. Moving beyond static permission lists, Koi deploys behavioral profiling within the workspace runtime. By actively intercepting unauthenticated background tasks and blocking unauthorized file-system reads, it stops automated data exfiltration in real time.

#3. Establish guardrails for automated package updates on endpoints

Developers prioritize speed, often allowing software packages to auto-update on their endpoints the moment a new version appears. Attackers weaponize this supply chain vulnerability, as seen in the May 2026 Team PCP attack where 3,800 GitHub repositories were compromised in just 36 minutes via poisoned auto-updates. Securing agentic endpoints against these rapid breaches requires behavior-based inspection within the active workspace context. Koi operates at this layer by providing safe deployment buffers that automate version cooldowns, blocking bleeding-edge updates until they are vetted. By continuously auditing process creation within the IDE runtime, Koi instantly drops unauthorized remote connections before malicious payloads can exfiltrate credentials from the endpoint.

#4. Enforce principle of least privilege for AI agents

AI coding assistants inherit the privileges of whoever deployed them. In practice, that means read access to production databases, write access to core repositories, and access to every secret in environment files and configuration directories. To restrict this excessive access, Koi applies dynamic sandboxing directly to AI agent processes at the kernel level. It enforces a strict zero-trust boundary that segregates sensitive workspace vectors, preventing agents from pulling data outside their approved scope without interrupting developer workflows.

#5. Maintain continuous endpoint posture management

Signature-based scanning only stops known threats. Sophisticated repository attacks often arrive as functional, high-rated software that carries no known bad signature. Koi’s research into the DarkSpectre campaign found eight browser extensions, all carrying “featured” badges from Google and Microsoft, installed by over 8 million users, silently harvesting every conversation from ChatGPT, Claude, and Gemini in the background. Koi addresses this by operating upstream: scanning marketplace listings every hour, using LLM-driven code analysis to compare what software promises against what its code does, sandboxing it, and scoring the risk before it ever reaches the endpoint.

Summary

Securing the modern enterprise is no longer about patching individual gaps. As AI agents redefine the workforce, Agentic Endpoint Security (AES) is now a strategic imperative for every CISO. By establishing a mandatory control plane for the AI-native workspace, AES ensures that your organization can scale engineering velocity without ever compromising enterprise integrity.

Ready to secure the future of your software stack? See how Koi Agentic Endpoint Security delivers complete visibility, risk scoring, and real-time prevention across every endpoint in your enterprise.

Developer withdraws plans for Perth datacentre after fierce community opposition

Sat, 25 Jul 2026 18:55:42 +0200

Three-storey GreenSquare datacentre in Hazelmere was to power cloud computing and the acceleration of AI

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A 15,000 sq metre datacentre near Perth will no longer go ahead after the developer withdrew plans amid community opposition over its impact on culturally significant sites.

The three-storey, 120-megawatt GreenSquare datacentre in the town of Hazelmere had been intended to power cloud computing and the acceleration of artificial intelligence, but faced fierce community backlash – as is increasingly common with such developments.

Sharing is scaring: The WhatsApp scam you didn’t see coming

Sat, 25 Jul 2026 18:52:37 +0200

How a fast-growing scam is tricking WhatsApp users into revealing their most sensitive financial and other data

Vulnerability Scanning with OpenVAS 9 part 4: Custom scan configurations

Sat, 25 Jul 2026 18:52:23 +0200

For all scans so far, we’ve only used the default scan configurations such as host discovery, system discovery and Full & fast. But what if we don’t want to run all NVTs on a given target (list) and only test for a few specific vulnerabilities? In this case we can create our own custom scan [...]

The post Vulnerability Scanning with OpenVAS 9 part 4: Custom scan configurations appeared first on Hacking Tutorials.

Netflix Probeabo: Der kostenlose Testzeitraum ist in Deutschland zurück

Sat, 25 Jul 2026 18:51:57 +0200

Netflix bietet in Deutschland nach fast sechs Jahren wieder kostenlose Probeabonnements an. Das Angebot richtet sich ausschließlich an Personen ohne bisheriges eigenes Konto und erscheint bei berechtigten Neukunden auf der Startseite. (Weiter lesen)

Publish to Microsoft Store as a company—now with free registration and faster onboarding

Sat, 25 Jul 2026 10:17:50 +0200

We’re seeing growing demand from companies that want to reach Windows users across both consumer and enterprise devices, and we’ve heard consistent feedback that getting started should be simpler. Today, we’re addressing that with three updates

The post Publish to Microsoft Store as a company—now with free registration and faster onboarding appeared first on Windows Developer Blog.

Build 2026: Furthering Windows as the trusted platform for development

Sat, 25 Jul 2026 10:17:37 +0200

Build is one of our favorite moments each year - a chance to connect with the global developer community and share what we’ve been building.

Over the past year, we have connected with many developers pushing the boundaries of what’s possible on

The post Build 2026: Furthering Windows as the trusted platform for development appeared first on Windows Developer Blog.

Windows platform security for AI agents

Sat, 25 Jul 2026 10:17:36 +0200

Making Windows the trustworthy OS for agents

AI agents are no longer just answering questions, they are taking actions across systems with increasing autonomy. As they become persistent participants in how software runs, they introduce new r

The post Windows platform security for AI agents appeared first on Windows Developer Blog.

Build for the future with the Android XR Developer Catalyst Program — Apply now!

Sat, 25 Jul 2026 10:15:51 +0200

Posted by Android XR Team

The Android XR ecosystem is expanding, and we’re committed to supporting developers who will build its next great experiences. Today, we’re opening applications for the Android XR Developer Catalyst Program, a dedicated initiative to accelerate the development of Android XR apps ready to launch within the next year.

This program is designed to provide the resources, hardware, and grants to help you build and scale innovative experiences across wired XR glasses, like XREAL’s Project Aura, and intelligent eyewear (audio and display glasses). We are especially interested in seeing innovative experiences across media, gaming, productivity, and health, but we welcome any unique use case that helps users expand what's possible.

Why join the catalyst program?

We want to help developers navigate common barriers to entry for XR development by providing:

Development Kits: Get early access to hardware development kits for wired XR glasses (XREAL’s Project Aura) and / or intelligent eyewear (audio and display glasses).
Technical support: Gain access to specialized technical resources and support forums specifically designed to help you prepare your app for Google Play.
Grant Opportunities: Submit a request and you may be eligible to receive a non-recoupable grant to accelerate your development.

Ready to start building?

Applications are open to developers looking to publish apps for the Android XR ecosystem in the next 6-12 months. You can build with Kotlin and the Jetpack XR SDK, or with Unity, Unreal Engine or Godot. If you need a spark of inspiration, you can check out existing XR Experiments and Samples to see how you can use the SDK for everything from spatial music to navigation.

Once you have your concept ready, be sure to submit your application by June 30th by 11:59PM PDT. We can’t wait to see what you build.

Start Your Application

Explore this announcement and all Google I/O 2026 updates on io.google.

Android CLI Now Stable 1.0: Accelerate developing for Android using any agent

Sat, 25 Jul 2026 10:15:49 +0200

Posted by Simona Milanovic and Ben Trengrove, Developer Relations Engineers

As Android developers, you have many choices when it comes to the agents, tools, command-line interfaces (CLI), and LLMs you use for app development. Whether you use Gemini in Android Studio, Antigravity 2.0, Antigravity CLI, or third-party agents like Anthropic's Claude Code or OpenAI'sCodex, our mission remains the same: to ensure that high-quality Android development is possible everywhere.

At Google I/O ‘26, we shared the latest leaps forward in agentic development, and showcased some of the newest capabilities of Android CLI—now stable at version 1.0 and ready for all Android developers to use. From new skills to enabling agent access to powerful Android Studio capabilities, we’re giving your agents the right tools to build alongside you.

If you’re already using Android CLI and want to jump into using all the new features, just run android update. Otherwise, read further to learn more about how we’re making the agents you choose be better at building for Android.

Android development unlocked for Antigravity

Google Antigravity now includes an optional bundle of Android resources—including the Android CLI and skills—that you can install. You can either install the bundle during onboarding after installation, or later from the Settings > Customizations > Build With Google Plugins menu.

This provides Antigravity with all the powerful tools and knowledge of Android CLI, enabling it to perform the core tasks necessary for Android app development more easily and efficiently—from creating projects to deploying your app on a new Android virtual device.

You can now easily install Android CLI for use with Google Antigravity 2.0.

Unlocking Android Studio capabilities for any agent

Android CLI provides a lightweight interface for AI Agents to perform tasks and retrieve knowledge about Android development. However, there's benefits to specialization — Android Studio contains over a decade of Android expertise, built to handle even the most complex Android projects. This includes Android Studio's powerful static analysis engine, refactoring tools, dependency management, UI design and rendering libraries, and more. AI Agents can now tap into Android Studio's tools to gain many of these same capabilities.

Your agents can now use Android CLI to access powerful capabilities of Android Studio.

The latest version of Android CLI introduces the new android studio command. This enables the agent of your choice to leverage the deep, contextual capabilities of Android Studio to better understand and perform actions on an open Android project. By running Android Studio alongside your preferred agent with Android CLI, your agent’s tasks can more efficiently navigate the codebase to produce more precise code changes. And, when you use Android CLI to create and iterate on your project, transitioning to Android Studio is much easier, so that you can use the purpose built tools—such as, performance profilers, Compose Previews, and Android Device Streaming—to get that production-grade polish.

When you have a project open in the latest preview version of Android Studio Quail, you (or your agent) can run the following command to check whether Android CLI has a connection established with your open project:

$ android studio check
pid: 32942
version: Android Studio
Projects:
    READY     JetSet /Users/adarshf/AndroidStudioProjects/jetset-main

From there, the agents can use the android studio command to access powerful IDE tools to interact with projects more efficiently. Key commands include:

analyze-file: Analyzes a file for errors and warnings using the editor's built-in inspections.
find-declaration: Finds the exact definition site of a symbol (class, method, variable, field, constant, or Android resource/color) across the project using semantic resolution.
find-usages: Finds all references and declarations of a symbol (class, method, variable, or Android resource) across the entire project using semantic analysis.
render-compose-preview: Renders a Jetpack Compose UI Preview and returns a path to the image and UI hierarchy if successful.
version-lookup: Get the latest information about which versions for specified app dependencies are available in common repositories, such as the Google Maven repository. By providing a programmatic solution, dependency management is less tedious and much less prone to flakiness.
open-file: Opens a file directly in Android Studio. This is useful if the agent wants to direct your attention to view Compose Previews, performance traces, or other specific files in the IDE.

For example, agents can now run the following commands to render a Compose preview for a new layout for your Android app, and then open the previews in Android Studio for you to take advantage of seeing multiple Compose Previews side by side and make AI-assisted edits right from the IDE.

$ android studio find-declaration HotelDetailScreen
$ android studio analyze-file .../JetPacker/feature/detail/src/main/java/com/example/jetset/feature/detail/HotelDetailScreen.kt
$ android studio open-file feature/detail/src/main/java/com/example/jetset/feature/detail/HotelDetailScreen.kt

To learn more about how to use these commands, run android help. And, to make sure your agents understand how to work with this tool, make sure to update the Android CLI skill by running android init.

More ways to get started

To make integrating Android CLI into your environments as seamless as possible, we’re making it available in more ways. You can now download and install Android CLI using more package managers: apt-get, winget, and homebrew. For example, you can run the following to install Android CLI using winget:

winget install -e --id Google.AndroidCLI

We’ve also updated the installation to a user-local directory, by default. You can find the commands for all supported operating systems plus additional download options on the Android CLI page.

Support for Journeys

Journeys are natural language descriptions of core user experiences.

We are also introducing support for Journeys. With Journeys tools and skills included with Android CLI, any agent of your choice can now create and run Journeys—which are natural language descriptions of user journeys for your app that are saved directly to your project.

(sped up) An agent running a Journey it generated for an app.

Agents can run these journeys using the Android CLI to navigate your app exactly like a user would. This unlocks entirely new ways to test, validate, or collect data across the critical experiences of your app, all driven by natural language and executed by your agent.

Expanding Android skills

To help models better understand and execute specific patterns that follow our best practices, we are continuing to expand our library of Android skills. We’re shipping new skills that make Android development everywhere more capable, efficient, and productive:

Display Glasses and Jetpack Compose Glimmer for XR: Provides guidelines for developing projected applications for Android Display Glasses using the Jetpack Compose Glimmer UI toolkit.
Migration to CameraX: Helps you migrate legacy Android camera implementations (Camera1 or raw Camera2 APIs) to CameraX.
Perfetto SQL: Translates natural language data prompts into Perfetto SQL queries and executes them against a local trace file.
Adaptive UI: Instructions to make or update an app's UI so that it adapts to different Android devices
Testing setup: Creates a basic testing strategy.
Styles: Helps with adoption of the new Jetpack Compose Style API for new components, and supports migration to Styles API.
AppFunctions: Analyzes Android codebases to recommend and implement new AppFunctions, and refines KDoc documentation for Model Context Protocol optimization.

You can add these new skills to your workflow directly from the command line. To help your agents understand and use Android CLI right away, you can initialize your environment and install the base android-cli skill by running:

android init

From there, you can browse and set up your agent workflow by searching for the exact capabilities your agent needs:

android skills list

Once you've found the right skill, install it to your environment by running:

android skills add –skill=

Get started today

To download the stable 1.0 release of the Android CLI, explore the new tools, and browse the complete documentation, head over to d.android.com/tools/agents today! Also, make sure you update to the latest preview version of Android Studio to unlock the latest features that Android CLI offers. We can't wait to see what you build with Android CLI 1.0 and how these new features supercharge your daily workflows. Join our vibrant community on LinkedIn, Medium, YouTube, or X and share your feedback.

Explore this announcement and all Google I/O 2026 updates on io.google.

Increasing app discovery and engagement on Google TV

Sat, 25 Jul 2026 10:15:47 +0200

Posted by Paul Lammertsma, Developer Relations Engineer

With over 300 million monthly active devices across Google TV and Android TV, it’s clear that the living room is a massive, distinct platform for apps to accelerate growth. Today, we’re excited to share Google TV features and developer tools designed to increase the discoverability of your content and prepare your app for future TV experiences.

Drive discovery and engagement with Gemini

Last year, we brought our AI voice assistant, Gemini, to our platform, so that people can easily find what to watch, learn something new on the big screen, and get everyday tasks done with just their voice.

Since launch, we’ve made improvements to how Gemini provides tailored responses to questions. Gemini shares a mix of visuals, videos, and text to help users find what they need, when they need it. For our streaming partners, Gemini is a helpful discovery engine—pulling from your app's metadata to surface your relevant content to viewers.

Declare support for pointing modality

The TV experience that we once knew is changing. Gemini is changing the way we discover and stream content with voice, but how we use the remote is evolving, too.

Pointer remotes bring motion-controlled input to the big screen, unlocking faster user navigation across the Google TV Home page and within content-heavy apps. To ensure your app is ready for this shift and provides a great experience for all users, now is the time to start thinking about pointing input. Here’s how to get started:

1. Adapt your TV app UI Library

You’ll need support for hover states, scrollable containers, and cursor clicks to enable pointer remote interactions for your app on Google TV. While implementation varies by UI stack, Jetpack Compose streamlines this transition, as most core components handle these multi-modal interactions natively out of the box.

Hover state: Every focusable element on your screen (buttons, movie posters, setting toggles) needs a clear visual feedback mechanism for a hover state. This is often subtler than a focus state but critical for feedback.
Scrollable containers: Pointer remotes will also have a small circular touchpad for scrolling. Users can use this touchpad to scroll up or down, or left or right in your app. Your app will need to respond to touch events to scroll.
Cursor clicks: Many TV apps today expect a simple D-pad OKAY button “click.” With a pointer remote, a user may “click” on an element that’s not the D-pad focus state, but is instead from a hovered state (similar to a mouse click).

2. Test pointing interactions with a mouse today

To see how your app handles hover, scroll, and clicks, simply connect a bluetooth mouse or wired mouse to your Google TV. Keep in mind that a mouse has more precise control, since users are closer to the screen and typically rest the mouse in a stable position. Pointer remotes can often be less precise, since users are sometimes 10 feet away from the screen, making rough gestures with the remote from their couch. As a TV designer or developer, you can mitigate this lack of input precision by having larger hover targets for elements.

3. Declare TV app support for pointer remotes on Google Play

Finally, tell Google Play that your TV app is designed to work with a pointer. This ensures that users with pointer remotes will be able to easily find, install, and interact with your app.

Within your AndroidManifest.xml, declare the meta-data tag, android.software.leanback.supports_touch. This tag informs the platform that your TV app “spatially supports touch,” since pointer remotes simulate touch events from a distance.

AndroidManifest.xml

...

Tips:

The android.software.leanback feature declaration indicates that your app supports D-pad navigation and is intended for distribution only on TV devices via Google Play.
The new software attribute of android.software.leanback.supports_touch declares that in addition to D-pad, you have ensured that your TV app works well for pointer/cursor experiences via mouse (of today) and pointer remotes (of future).
If you haven't already, now is the time to adopt Jetpack Compose. Hover, scroll, and clicks are common input modalities that are supported on various form factors, and building your app with an adaptive UI framework enables code reusability and reduced maintenance.

Onboard the Engage SDK

The Engage SDK, formerly known as the Video Discovery API, optimizes Resumption, Entitlements, and Recommendations across all Google TV form factors to boost app discovery and engagement.

Resumption: Partners can easily display a user's paused video within the 'Continue Watching' row from the Home page.
Entitlements: The Engage SDK streamlines entitlement management, which matches app content to user eligibility. Users appreciate this because they can enjoy personalized recommendations without needing to manually update all their subscription details. This allows partners to connect with users across multiple discovery points on Google TV.
Recommendations: The Engage SDK even highlights personalized recommendations based on content that users watched inside apps.

It’s a great time to start onboarding the Engage SDK now, since the legacy Watch Next API, which has been powering your continue watching 1.0 experience, will lose support in the 2nd half of 2027. To get started, head to goo.gle/engage-tv to learn more.

We're excited to see how our latest Gemini experience and developer tools will optimize your discovery and drive user engagement on our platform.

Explore this announcement and all Google I/O 2026 updates on io.google.

Build native Android apps in Google AI Studio

Sat, 25 Jul 2026 10:15:46 +0200

Posted by Emma-Louise Leavey, Group Product Manager and Mike Taylor-Cai, Product Manager

Starting today Google AI Studio can build entire Android apps for you in minutes from just a prompt. You don't need to install any software or configure any libraries, which significantly lowers the barrier to development. Whether you’re a seasoned developer looking to prototype at lightning speed or a creator building your first-ever mobile experience, you can now go from a single prompt to a high-quality, Kotlin-based Android app in AI Studio. You can easily install the app on your device, share it with others for testing, or send it to Android Studio for any further development.

The power of native Android

While AI has made it easy to generate web-based apps, people want more on their mobile devices. They expect the beautiful and usable modern app design and capabilities that come with native Android user experiences, built with the Kotlin programming language using Jetpack Compose, the official and recommended toolkit for Android development. Native Android apps bring the reliability of offline support, continuous background services, and the deep integration of hardware sensors like GPS, Bluetooth, and NFC. We've brought the technology that enables you to quickly create new projects with Gemini in Android Studio directly into the web-based AI Studio. Now, you get the best of both worlds: the ease of a prompt-based interface paired with the power of the Android SDK, all in your browser, no installation required.

A seamless, end-to-end workflow

We have streamlined the entire development lifecycle so you can focus on your idea:

1. Create your app and iterate in the cloud: Use the embedded Android Emulator directly in your browser to preview and interact with your app as it’s being built. No heavy SDKs to download, no local setup required.

Use the embedded Android Emulator to create and edit Android Apps right in the web browser

2. Install instantly: Connect your Android phone using a USB cable and install your app directly from AI Studio using the integrated Android Debug Bridge (adb).

Install the app on your Android device

3. Streamlined Publish to Google Play: Using your Google Play developer account, you can now publish your app directly from AI Studio for testing. AI Studio will automatically create your app record, package the bundle, and upload it to an internal testing track in Google Play Developer Console. Your app is available for you to install within minutes, and you can automatically update your app on your device as you develop it further in AI Studio.

Publish the app to an internal test track in Google Play

Seamless app development handoff

As you iterate on your app in AI Studio, you may find you need more advanced Android tools or support for a wider variety of Android device types. To move beyond the browser, you can seamlessly hand off your project to Android Studio by downloading a ZIP file or exporting it directly to GitHub.

Download zip file of Android app project files

When transitioning to a team environment or local development, you can leverage any IDE or agent you prefer. For a specialized experience, we recommend Gemini in Android Studio, which features models designed with Android in mind, or Antigravity, which integrates Android CLI commands into Google’s agentic development platform. This workflow makes building high-quality apps more accessible while giving you total flexibility in how you use AI to scale your project.

Start building today

To ensure a safe, high-quality ecosystem from day one, we have focused our initial release on specific capabilities including:

Personal utilities and simple social apps: You can rapidly prototype single or multi-screen apps, such as habit trackers, study quizzes, or event itineraries.
Hardware-enabled experiences: Because you are building native apps, you can leverage device features like the Camera, GPS/Location, Accelerometer and Bluetooth using the native Android APIs, letting you optimize hardware-level performance.
AI-powered experiences: You can create apps that feature Gemini API integrations, seamlessly embedding powerful AI capabilities directly into your mobile experience.

What’s Next?

We are moving fast to expand what’s possible for creators in AI Studio. Here is a sneak peek at what is coming soon:

Managing Google Play Test Tracks: Coming soon, we will be adding the ability to invite testers to try your app directly from AI Studio.
Firebase integrations: Out-of-the-box support for Firestore, Firebase Auth, Firebase App Check and other tooling critical for Android developers is coming soon.

Head over to Google AI Studio right now to start building. Here is some inspiration to get you started…

Turn your Google Pixel Watch into an aviation assistant

Prompt:

Build a small airplane "6-pack" instrument app for Google Pixel Watch. The 6 instruments should include attitude indicator, airspeed indicator, altimeter, turn coordinator, vertical speed indicator, and heading indicator. Use the Google Pixel Watch's sensors to power the instruments and display them clearly. Display one instrument at a time on the display. Swiping to the left or right should cycle through the instruments.

Interactive Harmonium app on Google Pixel Fold
Prompt: Build a Harmonium app for Pixel Fold devices, which plays like the instrument based on the hinge angle and touch gestures. The app should simulate the bellows and reeds accurately.

An Android app for guitarists to become better musicians by jamming to backing tracks

Prompt:

Build an Android guitar practice companion app that features a two-tab navigation system: 'Fretboard' and 'Library'.

The 'Fretboard' primary screen must contain an interactive guitar neck UI that visually maps out user-selected root notes, musical scales, and chords. Above the fretboard, implement a WebView-based YouTube player configured to play embedded videos inline. Additionally, include an AI generation feature that uses Retrofit to call Gemini Lyria 3 to create custom, 30-second backing tracks based on the user's currently selected key and scale. The generated audio files and their metadata must be saved locally using a database and displayed as a list in the 'Library' tab, where users can delete or play them.

Finally, implement a persistent, globally visible mini audio player at the bottom of the screen, complete with play/pause toggles, a progress slider for seeking, and timestamp text, allowing the user to seamlessly practice on the fretboard tab while listening to their tracks.

We are looking forward to seeing what you build next!

Explore this announcement and all Google I/O 2026 updates on io.google.

17 Things to know for Android developers at Google I/O

Sat, 25 Jul 2026 10:15:45 +0200

Posted by Matthew McCullough, VP, Product Management, Android Developer

Today at Google I/O, we announced the many ways we’re powering agentic workflows to increase your productivity and ensure your apps shine across the expanding Android ecosystem. Here’s a recap of 17 of our favorite announcements for Android developers; you can also see what was announced last week in The Android Show: I/O Edition. Stay tuned over the next two days as we dive into all of the topics in more detail!

Build High Quality Android Apps Using Agents

1: Android CLI: helping you build with any agent, LLM, and tool

Android CLI is now stable. It offers programmatic tools that allow any AI agent, including Claude Code, Codex, or Antigravity, to perform core Android tasks much more easily and efficiently. With today’s release, it also provides a bridge to tap directly into the "heavy-lifting" power of Android Studio to give you the production-ready polish needed for professional Android development. By leveraging the new android studio commands, developers can now grant their preferred agents the ability to perform semantic symbol resolution, analyze files for warnings, and even render Jetpack Compose previews. This release also enables official support for "Journeys" through new Android skills, which enables agents to execute end-to-end UI tests under your direction. Watch the developer keynote, and tune into the What’s New in Android tools talk for more information.

You can now easily install Android CLI for use with Google Antigravity 2.0.

2: Build production-ready apps with ease in Google AI Studio

Developers and creators can now build native Android apps, simply with a prompt in Google AI Studio. The apps are built with development best practices like Jetpack Compose, Kotlin, and APIs that leverage our recommended developer patterns. Google AI Studio enables developers to prototype, iterate via an embedded emulator, and deploy to physical devices without heavy local installations. Developers are then able to take those apps and share them to Android devices, as well as share them with others for testing through Google Play Console’s internal testing track. If a developer wants to prepare their app for a wider release, they’re able to take it to Android Studio for advanced debugging, testing, and UI polish. Watch the developer keynote, and tune into the What’s New in Android tools talk for more information.

Use the embedded Android Emulator to create Android apps in Google AI Studio

3: Accelerating AI coding assistance with Android Bench

Android Bench is our LLM leaderboard for Android development challenges. The goal is to accelerate model improvements, so you have more useful options for AI assistance. Many of you have been using open-weight models for AI assistance, so we’re now adding commonly used ones, such as Gemma 4, to the leaderboard, so you can see how LLMs that offer offline access and additional flexibility for power-users measure up. We're continuously working on increasing the difficulty of challenges we’re giving LLMs, to continue encouraging more useful improvements.

4: Convert iOS apps to Android with the Migration Assistant in Android Studio

The Migration Assistant in Android Studio is designed to port apps from platforms like iOS, React Native, or web frameworks to native Android. By simply selecting an existing project, developers can have the agent intelligently map features, convert assets like storyboards and SVGs, and implement Android best practices using Jetpack Compose and our recommended Jetpack libraries. This effectively transforms what used to be weeks of manual porting into a streamlined agentic workflow that only takes hours. We shared a preview of the incoming feature in the developer keynote.

A sneak peek of the Migration Assistant converting an iOS app into a native Android app

Building AI Into Your Apps

5: Building Intelligent Apps with generative AI

Generative AI enables you to create apps that are more intelligent, personalized, and agentic than ever before. This year, we introduced the latest advancements in on-device intelligence with a preview of Gemini Nano 4 for tasks like data extraction and summarization. We also expanded cloud capabilities via Firebase AI Logic, allowing developers to leverage Gemini models with robust grounding (including URL, Maps, and web search) to build smarter, more capable assistants. Furthermore, we unveiled our hybrid inference approach and the new Agent Development Kit (ADK) for Android, alongside communication protocols like AG-UI and A2UI that simplify the creation of autonomous, agentic experiences. To start integrating these powerful features, explore the developer documentation, and watch the technical deep dive session where we showcase all these technologies.

6: Experiment with AppFunctions today

AppFunctions is an Android platform API with an accompanying Jetpack library to simplify building Android MCP integrations. It empowers your apps to behave like on device MCP servers, contributing functions that act as tools for use by agents and assistants. AppFunctions integration with Gemini is currently in a private preview with trusted testers, and you can begin preparing your apps already. You can sign up for the Early Access Program and start experimenting using the API guidance, sample, and skill today.

The Future is Adaptive

7: Android is now Compose First; Views are now in maintenance mode.

Compose is our standard for UI development, and we are moving to a Compose-first approach for all future guidance and libraries. Building on five years of evolution, the latest releases deliver a more mature toolkit, from the highly customizable Styles API to refined shared element transitions and enhanced input support. These updates allow you to build beautiful, adaptive apps with less code and better performance. Learn more about what Compose-first means for Android Development in our blog post.

Build Android UI with Compose

8: Building seamless Android experiences across devices with Jetpack Compose

The Android ecosystem is now Adaptive by Default, moving fluidly across phones, foldables, tablets, cars, XR, and expanding usages with Googlebook and connected displays. With over 580 million large-screen devices, and users on multiple devices spending up to 14x more on apps, the investment in adaptive design presents a massive opportunity. Jetpack Compose is the definitive engine for this transition, offering core tools like our latest Jetpack Navigation 3 release, new experimental Grid and FlexBox layouts, enhanced non-touch input support, and CameraX for correct camera previews across any window size. Furthermore, new skills in Android Studio make updating your existing app to adopt these adaptive patterns easier than ever. Notability’s Android debut sets a new standard for premium productivity apps. Built with Jetpack Compose, Navigation 3, and Kotlin Multiplatform, it delivers an intuitive, adaptive experience across devices.

9: Create seamless experiences for Googlebook

Last week we announced Googlebook, a high-performance laptop that provides a large-screen canvas for your existing apps. Building with adaptive principles today helps ensure your app will work on Googlebook. Get started by reviewing relevant design guidance and developer guidelines for desktop experiences. Try out the new Desktop Emulator available in the Android Studio Canary to to test your apps for this form factor today.

New Desktop Android Emulator

10: Unified widget development experience with Jetpack Glance

Android 17 marks a shift toward a single, Compose-based development model for all widgets. By unifying the experience across mobile, Wear OS, and cars through Jetpack Glance, you can soon scale UI components across the ecosystem with a familiar workflow.

The breakthrough this year is the integration of RemoteCompose. On mobile and cars, it powers high-fidelity animations, while on Wear OS, it allows Wear Widgets (formerly Tiles) to render complex UI logic natively on remote surfaces. This ensures peak performance on low-power hardware while allowing a cohesive user journey—like checking a flight status on your car dashboard and seeing gate change updates on your wrist.

Four widgets are shown cycling through in the Android Auto interface. A clock, a contact card, Google Home favorites and a photo.

11: Expand your reach on the road with Android for Cars
To help you expand your reach when you build in-car experiences, we're making it easier to build once and deliver your apps to Android Auto and Android Automotive OS. With the latest releases of the Car App Library, you can build customized, distraction-optimized templated media apps for both platforms. We're introducing new components and template capabilities to give you increased flexibility and more options for laying out content. Parked experiences are expanding too, with immersive video playback coming to Android Auto for phones running Android 17. You can easily adapt your video apps for these parked experiences; apply now to the early access program to publish in these beta categories and learn more about the latest updates in our blog.

12: Accelerate your development with Android XR Developer Preview 4

Inspired by the innovative experiences you’ve built for the platform, we’re continuing to mature our tools with Developer Preview 4 of the Android XR SDK. A key milestone in this journey is the transition of our core libraries, XR Runtime, Jetpack SceneCore, and ARCore for Jetpack XR, moving to Beta soon to provide a more stable and performant foundation. We are also accelerating hardware access through the Android XR Developer Catalyst Program, where you can apply for XREAL’s Project Aura, audio glasses, or display glasses developer kits. Watch The latest in Android XR session or read our blog to see how these updates help you build experiences across the ecosystem.

Early preview of the Geospatial API in ARCore for Jetpack XR, enabling high-precision anchoring of digital content to real-world locations.

13: Android is your new home for professional-grade media experiences

Android 17 streamlines the entire media lifecycle with a production-ready toolkit. High-fidelity capture is now simplified with the CameraXViewfinder Composable, which handles complex scaling and responsiveness on foldables and tablets. For post-production, the new Media3 AI Effects library provides a single interface for premium features like Magic Eraser and Studio Sound, automatically optimizing for the device's hardware.

The pipeline is completed by CodecDB, offering chipset-specific encoding recommendations to eliminate export noise, and a new Scrubbing Mode in ExoPlayer for ultra-smooth seeking. Whether you’re compositing multi-asset edits with Media3 Transformer or using the streamlined CastPlayer API, these updates ensure a professional-grade experience with significantly less development overhead.

Low Light Boost and Magic Eraser in action

14: Increase app discovery and engagement on Google TV

Pointer remotes, which enable motion-controlled input, will be a future way for users to interact with Google TV as it unlocks faster user navigation. App developers can start declaring support for pointing input to ensure their apps are discoverable on future TVs with pointer remotes. Additionally, the Engage SDK, formerly known as the Video Discovery API, optimizes Resumption, Entitlements, and Recommendations across all Google TV form factors to boost app discovery and engagement. It’s a great time to start onboarding the Engage SDK now, since the legacy Watch Next API, which has been powering your continue watching 1.0 experience, will lose support in the 2nd half of 2027. Get all the details in our blog.

15: Performance: the foundation of a great app experience

To help developers navigate memory limits in Android 17, we've launched a suite of optimization tools. The R8 Configuration Analyzer identifies keep rules that are bloating your binary, while ProfilingManager and the integrated LeakCanary in Android Studio streamline memory leak detection. Furthermore, the new Android Performance Analyzer offers advanced AI integration for complex trace analysis and automated SQL query generation to pinpoint performance bottlenecks.

And The Latest on Driving Business Growth

16: What’s new in Google Play

Today's updates from Google Play help expand your reach and scale your business with less complexity. We’re redefining Play Store discovery with an immersive, short-form video format called Play Shorts, while expanding your audience beyond the store with app discovery in the Gemini app on Android and web. Plus, we’re introducing powerful new capabilities like agentic catalog management for seamless bulk price and SKU updates, and using Gemini models to enable Play Console to pre-populate store listings from imported documents—making global localization effortless.

Gemini will provide users with app suggestions during a search

17: And of course, Android 17

Android 17 includes new performance & system architecture improvements (in addition to app memory limits) like a lock-free MessageQueue and a GC with more frequent, less intensive young-generation collections to ensure system-wide stability and smoother UIs. The new contact picker and eyedropper API help minimize the use of sensitive permissions and unnecessary access to user data.

Review the behavior changes to make sure your app is ready for Android 17, including background audio hardening and SMS OTP protection. Get ready to target Android 17 (API 37) with changes such as mandatory large-screen resizability, certificate transparency by default, and restricted local network access. You can start testing today by enrolling your device in the Beta or using the latest 17.0 emulator images.

One more thing. the third beta of our Android 17 quarterly platform release (QPR1) just came out, and it contains a minor SDK release to support a few features that just couldn't wait for QPR2.

Check out all of the Android & Play Content at Google I/O

This was just a preview of some of the updates for Android developers at Google I/O. Tune into What’s New in Android for the latest news and announcements and follow Google I/O for much more over the following week!

Top AI on Android updates for building intelligent experiences from Google I/O ‘26

Sat, 25 Jul 2026 10:15:43 +0200

Posted by Jingyu Shi, Staff Developer Relations Engineer

At Google I/O 2026, we introduced Android’s shift from an operating system to an intelligence system. We also demonstrated how you can build intelligent experiences natively with the system and bring the power of Google’s AI into your apps. If you missed these updates, check out our quick recap video here:

1. Putting your apps at the center of the intelligence system

The Android OS already enables agents like Gemini to complete task automation, where it can navigate an app on the users behalf.

AppFunctions (Android MCP) provides you with more control over how your app integrates with the intelligence system. This new platform API and Jetpack library are currently available in experimental preview.

Android MCP: AppFunctions allows your application to act as an on-device Model Context Protocol (MCP) server. It means you seamlessly share your app's tools, services and data to the system and agents.

Streamlined Development: You can leverage the new skill to easily generate AppFunctions within your codebase.

Exploration and Testing: We’ve released a new test agent that allows you to experiment and debug your AppFunctions in a simulated agent environment.

Early Access Program: Want to be among the first apps to deploy app functions in production? Join our early access program today!

To see it in action, check out the live demo showcased during the What’s New in Android presentation.

2. On-Device Power with Gemini Nano 4 Preview

Last month, we launched Gemma 4, our state-of-the-art open models. You can already preview and prototype with the next generation of Gemini Nano (Nano 4) models with the AIcore developer preview. To make productionizing with Gemini Nano more reliable and performant, we are adding a few new features in ML Kit GenAI APIs:

Prototype to Production: Transition from prototyping in the AICore Developer Preview to building production-ready apps using the ML Kit GenAI Prompt API to leverage Gemini Nano 4 that’s launching in flagship devices later this year.

Structured Output: The upcoming Structured Output API will allow you to define object classes to be returned as outputs from Prompt API, ensuring reliable outputs in productionizing your intelligent features.

Prefix Caching: It optimizes your on-device inference performance with the prompt API. The new Prefix caching reduces inference time by storing and reusing the intermediate LLM state of processing a shared and recurring part of the prompt.

For highly customized or niche use cases, you can also use LiteRT-LM to bring your own fine-tuned small language model to Android.

3. Hybrid Inference & Agents

To help you build more advanced AI features like hybrid inference and explore building in-app agents, we’ve released new APIs, framework and guidances:

Firebase AI Logic Hybrid Inference: This new API provides the simple routing capability between on-device models and powerful cloud infrastructure. You can set explicit orchestration modes, such as PREFER_ON_DEVICE, PREFER_CLOUD, ONLY_ON_DEVICE, or ONLY_CLOUD, based on your need.

A2UI Jetpack Compose Renderer: The new A2UI library allows your agents to "speak UI". With the upcoming Jetpack Compose Renderer, you can automatically render these A2UI messages as native UI components.

ADK for Android: The first version of ADK for Android is available for experimentation. It allows you to build multi-agent workflows across both on-device and Cloud models while managing orchestration, context handling and sessions between agents.

From building with on-device models, exploring hybrid inference to building agents, you can see them in action in this talk:

Start Building Today

Whether you are experimenting with AppFunctions to prepare for the intelligence system, or looking to bring the power of Google’s AI within your own app, we’ve got you covered. Dive deeper into the code snippets, samples and comprehensive developer guides on the Android AI hub. For the full breakdown of what’s new, check out the official AI on Android at Google I/O 2026 playlist.

We are excited to see what you build!

Building Premium Android Experiences at Google I/O ‘26

Sat, 25 Jul 2026 10:15:42 +0200

Posted by Ataul Munim, Android Developer Relations Engineer

A truly differentiated Android experience is about delivering premium delight wherever your users are. At Google I/O ‘26, we showcased how the latest advancements in the Android ecosystem can help you elevate your app's quality while maximizing development efficiency.

To help you build apps that stand out, we're diving into the key tools and libraries designed to optimize your core performance, extend the surfaces of your app to other devices, and streamline how your app handles high-quality media.

Here is a recap of the essential updates and sessions you need to know to deliver a next-level experience across form factors!

Maximize app performance and ROI with the R8 Configuration Analyzer

A premium experience is only as good as its foundation, and a performant foundation is what allows your app to scale across the Android ecosystem. This is especially true with the release of Android 17, which introduces conservative, device RAM-based app memory limits to target extreme memory leaks and outliers before they cause system-wide instability. To stay below these new system thresholds and prevent your app from being terminated, having a lean footprint is no longer optional: it’s a critical requirement.

This year, we’re making it easier to build highly optimized, fast apps by introducing the R8 Configuration Analyzer in Android Studio. R8 is your most powerful tool for improving app performance, but its effectiveness is often limited by overly broad "keep rules" that prevent the compiler from stripping away unused code. The new Configuration Analyzer provides optimization, obfuscation, and shrinking scores, allowing you to identify specific rules that are preventing the benefits of R8 optimization.

By optimizing their R8 configurations, developers at Monzo achieved a 30% improvement in cold starts and a 35% reduction in ANRs. Smaller, faster code isn't just about efficiency; it's about ensuring your app has the memory headroom to deliver delight on every form factor, from the phone to the car.

Extend your reach with a unified approach to Widgets on Phones, Watches and Cars

User interaction is shifting toward quick, glanceable moments—short bursts of information that keep users connected without needing to open the full app. To help you increase the reach of your app content, we are unifying the development experience across the Android ecosystem with Jetpack Glance. By using a consistent, Compose-based model, you can elevate the content most important to your users straight to the phone’s home screen, Wear Widgets (previously Tiles!), and cars with a familiar workflow.

In order to help users engage with your content and features, even outside your app, we are making widgets more expressive and adaptive with RemoteCompose. On Wear OS, RemoteCompose allows you to use the Compose tools you’re already comfortable with to define UI logic that renders natively on remote surfaces, ensuring that your glanceable experiences remain highly performant and responsive even on resource-constrained hardware. On mobile and cars, RemoteCompose is used as a new framework giving Widgets new expressive capabilities.

You can use Jetpack Glance (together with RemoteCompose on Wear) to deliver a cohesive user journey. Whether it’s viewing flight status on the car dashboard, checking a gate change on a watch, or managing a boarding pass from a phone widget, this shared approach maximizes your app’s presence while keeping your development effort focused and efficient.

Supercharge your media pipeline with a complete, production-ready toolkit

Android has become a world-class home for the entire media lifecycle, and we are simplifying the journey from the first capture to the final playback. By leveraging Jetpack CameraX and Media3, you can build professional-grade experiences that feel native across the entire ecosystem.

It starts with high-fidelity capture using the CameraXViewfinder Composable, which ensures your preview remains perfectly scaled and responsive on any form factor, including foldables and tablets. Use this to build adaptive capture experiences like a picture-in-picture view for multi-tasking, or that take advantage of modern features like high-frame-rate or slow-motion capture with CameraX v1.5.

The new Media3 AI Effects library will provide a unified interface for premium features like Image & Video Enhance, Magic Eraser, and Studio Sound. This allows you to focus on the creative intent while Media3 handles the heavy lifting of choosing the most efficient and reliable path for the device. Then, use the latest improvements in multi-asset editing with Media3 Transformer to composite your edited videos together!

Complete the pipeline with tools designed for professional-grade export and viewing, including:

CodecDB, which offers data-driven encoding recommendations tailored to specific chipsets, ensuring your exported videos maintain high visual quality with minimal noise or blurriness
Scrubbing Mode in ExoPlayer to provide the buttery-smooth seeking experience users expect from premium media apps
Enhanced Cast support with the new CastPlayer API in Media3

By unifying these technical pillars, you can build a cohesive, high-performance media journey that delivers both delight for your users and high ROI for your development team.

For more details, check out the premium Android experience YouTube playlist.

Prioritizing Memory Efficiency: Essential Steps for Android 17

Sat, 25 Jul 2026 10:15:41 +0200

Posted by Alice Yuan, Developer Relations Engineer, Ajesh Pai, Developer Relations Engineer, and Fung Lam, Developer Relations Engineer

While app performance is often equated with a smooth UI and fast start times, memory serves as the silent foundation upon which these visible metrics are built. It's no secret that we're seeing a shift where device memory is more important than ever. Not only have we made strides in Android memory optimizations with Android 17, we're providing the tooling and API support to help you stay ahead of stricter memory requirements later this year.

To ensure device stability, starting in Android 17, the system will begin enforcing app memory limits based on the device's total RAM. If an app exceeds those limits, Android will kill the process with no associated stack trace.

Beyond these forced terminations, unoptimized memory usage inevitably degrades the user experience. When the app approaches heap memory limits, it triggers frequent garbage collection—leading to noticeable UI stutters. Furthermore, when a device runs out of available memory, the system scrambles to reclaim pages, causing CPU strain, UI latency, and battery drain. If the memory shortage is too severe, it can cause Low Memory Killer (LMK) events that abruptly terminate background processes and force apps to have slow cold starts and lose user state.

To build highly performant apps and avoid these forced terminations, we recommend that you adopt the following memory optimization strategies:

A condensed version of this blog post is also available in video format, go check it out!

Understanding Android 17 app memory limits

App memory limits are being introduced in Android 17 to prevent "one bad actor" from destroying the multitasking experience and stability of the user’s entire device.

Here is a breakdown of the reasons driving this architectural change:

Preventing cascading kills: When an app becomes bloated or leaks memory while holding a privileged state (e.g. it’s running a Foreground Service), it is initially shielded from the system's Low Memory Killer (LMK). As this single app grows unchecked and hoards RAM, the LMK is forced to compensate by killing off dozens of smaller, well-behaved cached apps and background jobs to reclaim space for the memory hog.
Preserving multitasking and user state: When the system is forced to purge cached apps to accommodate a single leaking process, the multitasking experience is severely degraded. Users returning to prior cached applications encounter sluggish cold starts instead of near-instant warm resumes. This inefficiency generates more CPU strain and accelerates battery depletion. It can also destroy the user’s context in recently used apps, such as scroll positions, navigation stacks, and in-game progress.

To determine if your app session was impacted by these constraints in the field, you can call getDescription() within ApplicationExitInfo. If the system applied a limit, the exit reason is reported as REASON_OTHER and the description string will contain "MemoryLimiter:AnonSwap". You can also leverage trigger-based profiling using TRIGGER_TYPE_ANOMALY to automatically capture heap dumps when the memory limit is reached. Furthermore, Android is actively working to surface more in-field memory metrics to developers within the Google Play Console.

We have also expanded our memory limits documentation to include local debugging commands, allowing you to simulate memory constraints in your local environment and validate your application's behavior under any memory limit enforcement.

Maximize bytecode optimization with R8

A highly effective way to reduce your app's memory footprint is to enable the R8 optimizer. By shrinking classes, methods, and fields into shorter names and stripping out unused code and resources, R8 significantly reduces your app's memory footprint by minimizing the amount of resident code required during execution.

R8 minimizes resident code, shrinking the memory footprint and lowering LMK termination risk. This results in more frequent warm starts over slow cold starts. Additionally, streamlined bytecode reduces main-thread CPU overhead, directly cutting ANR rates for a more fluid user experience. For example, the digital bank Monzo enabled full R8 optimization and saw a 35% reduction in their ANR rate, a 30% improvement in cold start rate, and a 9% reduction in overall app size.

The digital bank Monzo enabled full R8 optimization and boosted performance metrics by up to 35%.

To properly configure R8 in your build.gradle file:

Set isShrinkResources = true and isMinifyEnabled = true.
Use proguard-android-optimize.txt instead of the legacy proguard-android.txt, which actually prevents optimizations and is no longer supported in Android Gradle Plugin 9.
Remove android.enableR8.fullMode = false from your gradle.properties.

If you are using reflection in your code base, then add Keep rules to prevent R8 from optimizing those parts of the code. Make sure to scope the keep rules narrowly to get the maximum optimization.

To get the maximum optimization, make sure to follow these best practices in your keep rule file.

Remove global options like -dontoptimize, -dontshrink, and -dontobfuscate that prevent R8 from optimizing the entire codebase
Remove keep rules that prevent optimizing Android components like Activity, Services, Views or Broadcast receivers.
Refine the broad package wide keep rules to target only specific classes or methods.

To see more best practices, view our keep rules documentation.

Library Developer R8 Best Practices

If you are a library developer, strictly place the rules your consumers need into your consumer-rules file, and keep your library's internal protection rules in your proguard-rules.pro file. For more information on how to optimize libraries, see Optimization for library authors.

R8 Configuration Analyzer

To audit your R8 optimization, use the Configuration Analyzer. Configuration analyzer shows the current state of optimization with Obfuscation, Optimization, and Shrinking scores. With configuration analyzer, you can also understand how many classes, methods or fields are prevented from optimization by each keep rule. Refine these broad package wide keep rules to unlock the maximum optimization.

Using configuration analyzer, you can also identify keep rules that are subsuming other keep rules, redundant keep rules and unused keep rules.

The Configuration Analyzer shows the current state of optimization with Obfuscation, Optimization, and Shrinking scores.

R8 Agent Skill

You can also leverage the R8 Agent Skill with Android Studio agent or other AI tools to resolve misconfigurations and refine your rules resulting in improved app performance. (Insights from AI-driven skills will require technical verification)

Optimize image loading

Bitmaps are usually the largest common objects residing in your app's memory. They represent the final stage of the image loading process where compressed files, like JPEGs or PNGs, are decoded into raw pixel data for display. This means a tiny 100KB compressed image can balloon into several megabytes of RAM because memory consumption is determined by the image's pixel dimensions and color depth. Since bitmap operations are frequently on the critical path to drawing frames, unoptimized images cause severe memory bloat and UI jank.

Google recommends leveraging image loading libraries Coil for Kotlin-first projects, particularly when developing with Jetpack Compose and Glide for Java-based applications.

Adopt these five best practices

Downsample images: If you’re loading bitmaps manually, avoid loading a massive image into a tiny thumbnail view; use inSampleSize to load a smaller version. Glide and Coil downsamples images by default and you can configure this downsample strategy using DownsampleStrategy and ImageLoader respectively.
Cropping: Avoid embedding padding directly into an image file for letterboxing purposes (e.g., creating a transparent border to expand an image dimensions). Rather than baking in these borders, utilize InsetDrawable or apply padding directly within the View or Composable containing the bitmap.
Config: Balance memory and quality by choosing the right pixel format. Use RGB_565 when transparency isn't needed, which uses half the memory of the default ARGB_8888 format. In Glide you can configure this by using DecodeFormat and in Coil you can use bitmapConfig property.
Prioritize vector drawables: For basic geometric assets, leverage ShapeDrawable as a lightweight alternative to decoding rasterized bitmaps. By defining these assets once via XML, you ensure they scale seamlessly across all display densities while effectively eliminating resource-driven memory bloat.
Reuse: If your application manages Bitmaps manually then to minimize memory churn, when a bitmap is no longer required, the app should call bitmap.recycle() and immediately discard the Bitmap reference. If you use an image loading library like Glide or Coil, return the bitmap to the library’s managed pool. By providing an existing buffer for future memory needs, the pool effectively avoids the overhead of new allocations.

Check out our documentation on Optimizing performance for images to learn more.

Android Studio tooling

You can also eliminate redundant bitmaps using Android Studio Narwhal 4. Here is how to hunt them down in five simple steps:

Open the Profiler tab in Android Studio
Click Heap Dump (or "Analyze Memory Usage") and hit record to take a snapshot of your app’s current memory state.
Scan the analysis results for the yellow warning triangle ⚠️, which Android Studio uses to flag duplicate bitmaps being stored multiple times. Alternatively, navigate to the profiler header, choose "Filter by:" and pick the "Duplicate Bitmaps" setting.
Click on any flagged entry to open the Bitmap Preview pane, allowing you to see exactly which image is the repeat offender.
Use that visual confirmation to track down the redundant loading logic in your code and implement a better caching strategy.

Look for the yellow warning triangle ⚠️ in heap dumps when using the Android Studio Profiler.

Detect and fix memory leaks with Android Studio

Memory leaks in Android occur when your code holds onto an object's reference long after its lifecycle has ended. This prevents the Garbage Collector (GC) from reclaiming that memory, eventually leading to sluggish performance or OutOfMemoryError (OOM).

Android Studio Panda 3 features a dedicated LeakCanary profiler task, allowing developers to analyze real-time memory leaks and map traces within the IDE.

The LeakCanary profiler task in Android Studio actively moves the memory leak analysis from your device to your development machine, resulting in a significant performance boost during the leak analysis phase as compared to on-device leak analysis.

LeakCanary memory leak analysis contextualized with Go to declaration for debugging

Additionally, the leak analysis is now contextualized within the IDE and fully integrated with your source code, providing features like go to declaration and other helpful code connections that drastically reduce the friction and time required to investigate and fix memory leaks.

Examples of common memory leaks

Memory leaks occur when an object persists in memory beyond its intended lifespan. This typically happens due to:

Retaining references to Fragments, Activities, or Views that are no longer in use.
Mismanaging Context references.
Failing to properly unregister observers, listeners, and receivers.
Creating static references to objects that are bound to components with shorter lifecycles.

Here are a few example scenarios:

Scenario	Compose-based example	View-based example
Leaking Context	Example: Passing LocalContext.current to a ViewModel Fix: Keep `Context` dependent logic within the UI layer. For non-UI layers, refactor to use dependency injection or observe UI state using Kotlin flow.	Example: Storing an `Activity` in a companion object or static variable. Fix: Don’t hold static references to UI components. Refactor to use dependency injection or observe UI state using Kotlin flow.
Leaking Listeners	Example: Using `DisposableEffect` to start a listener but leaving `onDispose` empty. Fix: Perform the unregistration and cleanup logic inside the `onDispose` block.	Example: Registering for SensorManager updates and forgetting to unregister. Fix: Manually call `unregisterListener()` in `onStop()` or `onDestroy()` lifecycle.
Leaking Views	Example: Holding a reference to a legacy `View` inside an `AndroidView` without a release strategy. Fix: Use the `release` block of the `AndroidView` composable to clean up the legacy `View`.	Example: Keeping a reference to a view binding object after the `Fragment` is destroyed. Fix: Set the binding variable to `null` inside the `onDestroyView`() lifecycle method.

Trim memory when app leaves visible state

Android can reclaim memory from your app or stop your app entirely if necessary to free up memory for critical tasks, as explained in Overview of memory management. Android will usually reclaim memory from your app when it’s not visible to the user, such as by discarding some of your app’s code and data pages in memory or compressing your heap allocations. When the user resumes your app and your app tries to access some memory that’s been reclaimed, the OS will swap that memory back in on demand. This swapping behavior can be slow, and cause unexpected jank or stutters in your app.

If you leave it to the OS to decide what memory to reclaim from your app, you may find that the OS reclaimed memory that you’ll need shortly after resuming your app. Instead, your app can voluntarily discard memory allocations that it can regenerate later, on demand and at a low cost. To do so, you can implement the ComponentCallbacks2 interface. You can implement onTrimMemory in your Activity, Fragment, Service, or even your custom Application class. Using it in the Application class is highly effective for global cache management.

The provided onTrimMemory() callback method notifies your app of lifecycle or memory-related events that present a good opportunity for your app to voluntarily reduce its memory usage.

In terms of memory lifecycle management, your implementation should focus exclusively on TRIM_MEMORY_UI_HIDDEN and TRIM_MEMORY_BACKGROUND. Since Android 14, the system has ceased delivering notifications for other legacy constants, which were formally deprecated in Android 15.

TRIM_MEMORY_UI_HIDDEN: This signal indicates that your application's UI has transitioned out of the user's view. This provides an opportunity to release substantial memory allocations tied strictly to the interface—such as Bitmaps, video playback buffers, or complex animation resources.

TRIM_MEMORY_BACKGROUND: At this level, your process is residing in the background and is now a candidate for termination to satisfy the system's global memory needs. To extend the duration your process remains in the cached state, and reduce the number of app cold starts, you should aggressively release any resources that can be easily reconstructed once the user resumes their session.

import android.content.ComponentCallbacks2
// Other import statements.

class MainActivity : AppCompatActivity(), ComponentCallbacks2 {

    /**
     * Release memory when the UI becomes hidden or when system resources become low.
     * @param level the memory-related event that is raised.
     */
    override fun onTrimMemory(level: Int) {

        if (level >= ComponentCallbacks2.TRIM_MEMORY_UI_HIDDEN) {
            // Release memory related to UI elements, such as bitmap caches.
        }

        if (level >= ComponentCallbacks2.TRIM_MEMORY_BACKGROUND) {
            // Release memory related to background processing, such as by
            // closing a database connection.
        }
    }
}

Note: The onTrimMemory integration may depend on SDK support. For instance, certain games rely on their game engine to enable this capability. Please check out the game memory optimization documents.

Advanced memory observability with ProfilingManager

To catch and diagnose memory issues in the field that cannot be reproduced locally, you should leverage the ProfilingManager API. Introduced in Android 15, this advanced observability API allows you to programmatically collect real-user Perfetto profiles.

For teams that lack a dedicated infrastructure to manage and host performance artifacts, Crashlytics is exploring a specialized solution to streamline this workflow. They are inviting developers to provide feedback.

Android 17 introduces new event-driven triggers, most notably TRIGGER_TYPE_OOM and TRIGGER_TYPE_ANOMALY:

The OOM trigger automatically collects a Java heap dump at the exact moment an OutOfMemoryError crash occurs, providing precise allocation states. A collected OOM profile is provided the next time the app starts and registers the registerForAllProfilingResults callback.
The Anomaly trigger detects severe performance issues, such as excessive binder spam or breached memory thresholds. The memory anomaly delivers a heap dump just prior to the system terminating the app.

  val profilingManager = 
applicationContext.getSystemService(ProfilingManager::class.java)
    val triggers = ArrayList()  


    triggers.add(ProfilingTrigger.Builder(
                 ProfilingTrigger.TRIGGER_TYPE_ANOMALY))
    val mainExecutor: Executor = Executors.newSingleThreadExecutor()
    val resultCallback = Consumer { profilingResult ->
        if (profilingResult.errorCode != ProfilingResult.ERROR_NONE) {
            // upload profile result to server for further analysis          
            setupProfileUploadWorker(profilingResult.resultFilePath)
        } 

    profilingManager.registerForAllProfilingResults(mainExecutor, resultCallback)
    profilingManager.addProfilingTriggers(triggers)

Once you’ve collected the heap dump, you can download the profile from the server, or locally via adb pull and drag and drop the file into the Perfetto UI. To streamline your memory debugging workflow, use the Heap Dump Explorer, this is the new default view for heap dumps in Perfetto UI. This tool provides an intuitive interface for inspecting Java heap dumps, allowing you to visualize object allocation hierarchies, compute retained memory sizes, and identify the shortest path from garbage collection root. By leveraging the Heap Dump Explorer, you can rapidly pinpoint memory leaks, bloated retained objects such as excessive bitmap allocations, and analyze heap object allocations all in one place.

Use the Heap Dump Explorer’s embedded flamegraph to visually inspect and navigate through objects with the highest heap allocations.

Conclusion

Optimizing bytecode with R8, adopting image loading best practices, and resolving memory leaks are critical steps toward delivering a high-quality user experience while managing resources effectively under pressure. Adopting these proactive measures helps maintain app stability and performance, preventing unexpected terminations while safeguarding user context. To further your performance expertise, explore our revised memory guidance.

Datadog delivers millions of in-depth performance insights with ProfilingManager

Sat, 25 Jul 2026 10:15:39 +0200

Posted by Alice Yuan, Developer Relations Engineer at Google, Arti Arutiunov, Product Manager at Datadog and Nikita Ogorodnikov, Staff Software Engineer at Datadog

Performance regressions are notoriously hard to reproduce, making regressions a massive bottleneck for mobile developers. Although signals like ANR rates indicate what issues occur in production, pinpointing the specific line of code that resulted in the performance issue has historically necessitated exhaustive manual reproduction or speculative trial-and-error experimentation.

Datadog collaborated with Google to mitigate this frustration by integrating the ProfilingManager API (available on Android 15+ devices) into its Real User Monitoring (RUM) and Continuous Profiling platforms. This integration transforms the debugging workflow, allowing developers to move beyond surface-level symptoms to being able to detect the why behind a performance bottleneck.

By leveraging this system-level API, Datadog now processes millions of production profiles weekly across the globe according to Datadog internal data of June 2026. It provides engineering teams with a new level of visibility into real-world performance, all while maintaining a low runtime overhead for production-scale performance monitoring.

The impact of ProfilingManager

ProfilingManager is a system service introduced in Android 15 that enables apps to programmatically collect performance data such as call stack samples, field traces and memory heap dumps directly from production environments. This capability shifts the engineering paradigm from reactive manual reproduction to proactive field analysis.

For example, a Google communications app used field traces to investigate why its cold start times were slower on newer, more powerful hardware. By diving into the field-collected traces and comparing traces across different device types, the engineer discovered a hidden scheduling issue: a background text-to-speech service was unnecessarily being prewarmed during app startup. The traces revealed that this background process was monopolizing the device's highest-performing big CPU core, forcing the app's main thread to sleep while the prewarm occurred.

Solving the Android code-level visibility challenge

Prior to the implementation of ProfilingManager, Datadog’s Real User Monitoring (RUM) focused on high-level application health and session-level telemetry to assess the user journey. Engineering teams could monitor Android performance signals like time to initial display, ANR rates, CPU load, and frozen frames. These insights extended to granular interactions, such as network latency, touch events, and main thread hangs. However, while this data effectively highlighted which performance bottlenecks were surfacing in the field, it provided no clear path to identifying the root cause of these failures.

To address this, Datadog needed a profiling engine capable of capturing Android traces directly from devices in production with minimal performance impact. After evaluating alternative approaches, such as writing their own trace processor using Android Debug APIs, the team selected ProfilingManager because it is the most performant solution of the profiling options they evaluated and offloads the sampling decisions overhead to the OS.

ProfilingManager supports a wide range of collection methods, including CPU traces, call stack sampling, memory analysis through Java heap dumps and native heap profiles. It enables developers to profile production builds, upload trace files to external storage, and review them in the Perfetto trace analyzer UI. As a SaaS provider, Datadog uploads, visualizes, and analyzes these profiles collected via its SDK, providing a unified view of application health.

By centralizing high-fidelity telemetry within a unified observability API, ProfilingManager empowers Datadog and its clients to proactively monitor, investigate, and remediate complex Android performance regressions through key technical advantages:

Granular session diagnostics: ProfilingManager enhances debuggability by delivering direct OS-level trace data, overcoming the visibility and alignment challenges typical of custom logging with system services. To dive deeper, developers can download these traces from Datadog to investigate further in visualization tools like the Perfetto UI.
Automated telemetry triggers: By leveraging native system events to initiate trace recordings at key optimization points, Datadog reduces the need to build custom collection logic. While the initial rollout focuses on the APP_FULLY_DRAWN signal, there are already plans to expand this observability to include ANR, OOM, and COLD_START triggers.
Proactive trace snapshots: By interfacing directly with the system-level Perfetto service (traced), ProfilingManager utilizes a proactive background recording model designed to capture unpredictable issues. This ensures that developers receive a precise visualization of the events leading up to a performance anomaly, offering a level of insight that exceeds what is possible through manual instrumentation.
Bottleneck detection at scale: Datadog is able to synthesize telemetry from across Datadog’s global customer base to uncover regressions that only emerge under unique hardware configurations and variable network environments.
System-enforced resource stability: The API leverages sampling trace collection to ensure performance and user experience impacts remain unnoticeable.
On-device data controls: ProfilingManager filters out irrelevant information from other processes on-device before the profile is delivered to the app. This minimizes file sizes and ensures that only data relevant to the app's processes is provided.

Processing millions of weekly profiles to optimize real-world apps

An example of Datadog's time to initial display measurement with
stack sampling powered by ProfilingManager

Integrating a system-level profiling API into a global monitoring SDK required solving infrastructure challenges. Because ProfilingManager generates highly detailed performance traces, the Datadog engineering team had to build a pipeline capable of parsing and analyzing these profiles on the server side at scale. Beyond profile collection, Datadog also emphasizes the importance of balancing sampling frequency with collecting enough data to generate meaningful insights about your application. Datadog relies on ProfilingManager’s built-in rate limiting as a critical stability safeguard, preventing excessive telemetry requests from overburdening user devices.

The team has been profiling Datadog's own native Android application and a number of early adopters’ applications for months, gathering millions of profiles to ensure a fast, error-free launch experience and to refine their performance-detection algorithms. Today, the production integration seamlessly scales across a variety of Android devices.

Conclusion

By integrating Android’s ProfilingManager API, Datadog successfully closed the visibility gap between backend systems and mobile client applications for their customers. By processing millions of profiles weekly with negligible device overhead, Datadog equips Android developers with the code-level insights necessary to diagnose complex performance bugs instantly, helping developers build smoother applications and improve their app’s performance signals in the Play Store. To adopt the ProfilingManager API directly into your performance observability framework, check out our documentation.

In the future, Datadog aims to make Android profiling data a first-class input for coding agents to autonomously resolve performance bottlenecks, closing the feedback loop between detection and remediation. Datadog is working toward making Android profiling broadly accessible to developers.

To get started using the Datadog real user monitoring feature powered by ProfilingManager, visit Datadog Mobile Real User Monitoring.

Top 3 updates for Android developer productivity

Sat, 25 Jul 2026 10:15:38 +0200

Posted by Simona Milanovic, Developer Relations Engineer

Every year, Google I/O brings new announcements and resources across ecosystems and products, including Android development. As development shifts toward AI and agent-assisted tooling, we’ve expanded our offerings to better support you, however you decide to build for Android.

To help you stay up to date, here is a summary of the top 3 announcements for Android Developer Productivity at I/O.

1. Android CLI is now stable

Android CLI is now stable at version 1.0, with more capabilities and integrations.

The latest version of Android CLI introduces many new features, like programmatic version lookup and support for Journeys, and bridging capability to allow agents to integrate directly with Android Studio, via the studio command.

Running Android Studio alongside the agent and Android CLI enables more efficient navigation in your project, more precise output, and access to Android Studio’s unique tooling, such as performance profilers, Compose Previews, and Android Device Streaming.

Android CLI now integrates seamlessly with Android Studio

Additionally, Google Antigravity now officially supports Android development, with the Android resources bundle, which includes the Android CLI and skills.

You can either install the bundle during onboarding after installation, or later from the Settings > Customizations > Build With Google Plugins menu. This provides Antigravity with all the powerful tools and knowledge of Android CLI to enable it to perform core tasks—from creating projects to deploying your app on a new virtual device—much more easily and efficiently.

Google Antigravity now offers the Android resources bundle

Android CLI is now available through more package managers: like npm and homebrew. For more information, check out the Android CLI blog post and official documentation.

2. Android skills keep growing

To help models gain expertise for specific development patterns that follow our best practices, we are continuing to expand our repository of Android skills, available through Android CLI and GitHub.

Android skills ground LLMs in specialized workflows and domain knowledge, for the most common and more complex user journeys they might struggle with. We’ve shipped a fresh new batch of skills, with now more than 17 skills for areas such as:

Adaptive UI
Display Glasses and Jetpack Compose Glimmer for XR
Migration to CameraX
Perfetto SQL and Trace Analysis
Jetpack Compose Styles API
AppFunctions
Verified email retrieval with Android Credential Manager
Engage SDK integration
Testing setup
Wear OS Jetpack Compose Material3

Android skills keep growing

You can browse skills and install using the Android CLI commands:

android skills list
android skills add –skill=

For more information, check out the official documentation.

3. Android Bench adds new models

Earlier this year, we launched Android Bench - our leaderboard for testing LLMs on real-world Android development challenges and tasks, with the goal of accelerating model improvements, so you have more helpful options for AI assistance.

Latest results from Android Bench leaderboard

You asked us to evaluate open models. So, at I/O, we added more commonly used ones, including our local model Gemma 4, to the leaderboard. We also added the latest models including Gemini 3.5 Flash.

We are also working on increasing the difficulty of challenges we’re giving LLMs, including creating long running tasks, to continue encouraging improvements. These tasks will be coming soon to Android Bench. Check out the Android Bench leaderboard to see the latest results.

Android development anywhere

By expanding our AI-assisted Android development offerings to Antigravity, through Android CLI and Android skills, and solidifying with the pro capabilities and production grade polish of Android Studio, we’re supporting Android developers wherever they choose to build.

Have fun bringing your ideas to life faster and easier than ever before - we’re excited to see what you build in this new era of agentic development.

Check out the full Developer productivity at Google I/O 2026 YouTube playlist for more information.

Android 17 is here

Sat, 25 Jul 2026 10:15:36 +0200

Posted by Matthew McCullough, VP of Product Management, Android Developer

Today we're releasing Android 17 and making it available on most supported Pixel devices. Look for new devices running Android 17 in the coming months.

Android 17 marks the start of our transition to an intelligence system, putting your apps at the center. It's shifting to an adaptive-first development standard by introducing mandatory large-screen resizability, all while delivering next-generation privacy, security, media, camera, and performance. We'll cover all that in this post, as well as how we're bringing together next generation tools, libraries, and agent skills to help your apps embrace the opportunity.

Throughout the past year, from our Canary channel to our Beta releases, we’ve collaborated with you in the developer community to build a platform you and your users can trust. To that end, this moment marks the availability of the source code at the Android Open Source Project (AOSP). This allows you to examine the source code for a deeper understanding of how Android works.

Let's dive deeper into Android 17.

An intelligence system

With deep integration between hardware, software and AI, we’re transforming Android from an operating system to an intelligence system. It's about delivering new helpful experiences that anticipate user needs, and it brings more opportunities for engagement with your apps. To that end, Android 17 expands the capabilities of AppFunctions, a platform API with a corresponding Jetpack library. It allows you to contribute your app's unique capabilities as orchestratable "tools" for Android MCP, the on-device equivalent of the Model Context Protocol. AI agents and assistants (like Google Gemini) can discover and execute AppFunctions to perform workflows on behalf of the user with direct access to the app's local state.

The Jetpack library, currently in alpha, makes adding AppFunctions as easy as annotating a class and adding KDoc comments.

/**
 * A note app's [AppFunction]s.
 */
class NoteFunctions(
    private val noteRepository: NoteRepository
) {
    /**
     * Adds a new note to the app.
     *
     * @param appFunctionContext The execution context.
     * @param title The title of the note.
     * @param content The note's content.
     */
    @AppFunction(isDescribedByKDoc = true)
    suspend fun createNote(
        appFunctionContext: AppFunctionContext,
        title: String,
        content: String
    ): Note {
        return noteRepository.createNote(title, content)
    }
}

We’ve also launched an AppFunctions agent skill that analyzes your app’s key workflows, automatically generates the required Kotlin code, optimizes your KDocs for LLM tool-calling, and provides ADB commands for testing and debugging.

The Gemini integration is currently in a private preview with trusted testers, but you can begin preparing your apps now. In addition to ADB commands to execute your AppFunctions, we've provided a test agent app that includes an interface to discover and execute your app functions and simulate an AI agent integration. Join our integration early access program at goo.gle/eap-af for a chance to be among the first apps to deploy AppFunctions to production.

Adaptive-first

Your users no longer rely on a single form factor; they transition between phones, foldables, tablets, laptops, automotive displays, and immersive XR environments. Now, with over 580 million large screen devices in the hands of users and the forthcoming launch of Googlebooks, the next generation of ChromeOS built on the Android stack, adaptive is no longer just a technical goal. It’s a massive opportunity to reach highly engaged users, which is one of the reasons we're shifting to an adaptive-first development standard.

No resizability/orientation restrictions on large screens

To ensure apps deliver a premium experience across all form factors, including mobile devices running in desktop mode on connected displays, Android 17 (API level 37) removes the developer opt-out for orientation and resizability restrictions on large screen devices (sw > 600 dp) for apps targeting API level 37. The system will ignore legacy manifest attributes and runtime APIs, including screenOrientation, setRequestedOrientation(), resizeableActivity=false, and aspect ratio constraints (minAspectRatio/maxAspectRatio). Games (based on app category in Google Play) remain exempt. Your app must be ready to adapt to any window size, respect the user's preferred device posture, and support free-form windowing natively.

Next-gen multitasking: App Bubbles, Bubble Bar, and desktop interactive PiP

Android 17 introduces powerful new windowing capabilities that redefine how users multitask, demanding even greater layout flexibility from your apps:

App Bubbles: Moving beyond the messaging bubbles API, users can now transform any app into a floating bubble by long-pressing its icon on the launcher. This feature is available across phones, foldables, and tablets, enabling lightweight multitasking for any workflow.
The Bubble Bar: On large screens (tablets and foldables), the system taskbar now includes a dedicated Bubble Bar to organize, transition between, and dock these floating app bubbles.
Desktop interactive PiP: In desktop environments, Android 17 introduces interactive Picture-in-Picture (PiP). Unlike traditional PiP windows which are read-only, these pinned windows remain fully interactive while staying always-on-top of other application windows.

App Bubbles and Bubble Bar in action

Activity recreation updates

To prevent disruptive state loss and stutter, Android 17 updates the default behavior for Activity recreation. The system will no longer restart activities by default for typical configuration changes that do not require a full UI redraw (including CONFIG_KEYBOARD, CONFIG_KEYBOARD_HIDDEN, CONFIG_NAVIGATION, CONFIG_TOUCHSCREEN, and CONFIG_COLOR_MODE).
Instead, running activities will receive these updates via onConfigurationChanged(), enabling smooth transitions. If your application explicitly relies on a full restart to reload resources for these changes, you must now explicitly opt-in using the new android:recreateOnConfigChanges manifest attribute.

Continue On

Android 17 adds Continue On to help users seamlessly transition a task between Android devices. The user sees a suggestion for the most recently opened app from their mobile device in their tablet taskbar, providing a one-tap affordance to launch the app and deep-link where they left off. Continue on can support app-to-web transitions, including falling back to using the web if the app isn't installed.

Handoff Suggestion on a Tablet

class MyHandoffActivity : Activity() {

    ...

  override fun onCreate(savedInstanceState: Bundle?) {
    super.onCreate(savedInstanceState)
    // Do stuff
    ...
    // Enable handoff
    setHandoffEnabled(true, null)
  }

  // Override and implement onHandoffActivityDataRequested
  override fun onHandoffActivityDataRequested(handoffRequestInfo: HandoffActivityDataRequestInfo) : HandoffActivityData {
    // Create and return handoff data
  }
}

Go adaptive-first with Jetpack Compose

To help you adapt your apps to meet the new Android 17 requirements, we've launched the Jetpack Compose adaptive skill. This AI-powered developer workflow helps you implement the best adaptive practices:

Adaptive navigation: Automatically transition between bottom navigation bars on mobile and edge-anchored navigation rails on large screens using NavigationSuiteScaffold from the Material 3 Adaptive library.
Multi-pane layouts: Implement list-detail and supporting pane layouts natively using Navigation 3 Scenes (ListDetailSceneStrategy and SupportingPaneSceneStrategy) instead of fragile fragment transactions.
FlexBox & Grid APIs: Utilize Compose 1.11's dynamic layout components to easily adjust row and column spans on the fly, ensuring your content always fills the space beautifully.
Advanced non-touch input: Leverage Compose 1.11's enhanced trackpad and mouse support, including native focus rings and new APIs (like TrackpadInjectionScope and performTrackpadInput) to easily test and deliver a true "laptop-class" experience on Googlebooks and Desktop Mode.
Dynamic window states: Leverage Compose's reactive state model to seamlessly adapt your UI when the app transitions from full screen to a floating App Bubble or an interactive Desktop PiP window, ensuring a premium experience even at minimal dimensions.

Android is Compose-first

Compose offers the easiest way to build adaptive apps, and that's just one of the many reasons we believe that all Android UI should be built with Compose. To that end, Android development is now Compose-first. All new Android APIs, libraries, tools, and developer guidance will be built exclusively for Jetpack Compose. Legacy View components (in the android.widget package) and View-based Jetpack libraries (like Fragments, RecyclerView, and ViewPager) are now in maintenance mode. They will receive only critical bug fixes, and no new features.

TIP
Ready to migrate? Use our AI-driven XML to Compose Migration Skill to automatically analyze your legacy View layouts and convert them into highly-adaptive Compose code.

Performance & efficiency

App performance means a smooth user interface, fast app start times, and efficient multitasking; Android 17 has impactful improvements in all of these areas.

App memory limits

Memory usage is one of the silent foundations of overall performance. When a foreground app or service grows unchecked, memory management spikes CPU and battery utilization and eventually leads to the termination of other well-behaved cached apps and background jobs, ultimately forcing slower cold starts and impaired multitasking.

Starting in Android 17, the system will enforce strict app memory limits based on a device's total RAM, abruptly terminating offending processes. New things to help you navigate these tighter requirements:

R8 Optimizer: The R8 optimizer significantly reduces your app's bytecode memory footprint by shrinking classes, methods, and fields into shorter names, and stripping out unused code and resources. Use R8 in full mode along with the new R8 configuration analyzer to make sure your app is getting the most from R8.

The R8 Configuration Analyzer

LeakCanary in Android Studio Panda: The profiler now features native LeakCanary integration as a dedicated task, fully integrated with your IDE and source code.
ApplicationExitInfo: If your app is terminated by these limits, getDescription() from ApplicationExitInfo will return "MemoryLimiter:AnonSwap".
On-Device Anomaly Detection: Part of ProfilingManager, you can leverage trigger-based profiling using TRIGGER_TYPE_ANOMALY to automatically capture heap dumps when the memory limit is reached.

val profilingManager = applicationContext
   .getSystemService(ProfilingManager::class.java)

val triggers = ArrayList().apply {
  add(ProfilingTrigger.Builder(
    ProfilingTrigger.TRIGGER_TYPE_ANOMALY).build())
}
profilingManager.addProfilingTriggers(triggers)

And, we're working to surface more in-field memory metrics to you within Google Play Console.

Generational garbage collection

Android 17 introduces more frequent, less resource-intensive young-generation collections to ART's Concurrent Mark-Compact garbage collector (GC). By separating short-lived objects from stable, long-lived ones, the system runs frequent, lightweight "young-generation" sweeps rather than expensive full-heap scans, drastically reducing CPU usage, power drain, and UI stutter. Our testing has shown significant improvements in GC interference with application threads and a reduction in the maximum memory resident set size (RSS). ART improvements are also available to over a billion devices running Android 12 (API level 31) and higher through Google Play System updates.

Lock-Free MessageQueue

For apps targeting SDK 37 or higher, the core android.os.MessageQueue now implements a lock-free architecture, significantly reducing missed frames, improving app startup time, and radically improving the performance of busy queues in multithreaded scenarios. Note: This can break apps that use reflection on private MessageQueue fields and methods. The peekWhen and poll APIs have been added to TestLooperManager for instrumentation testing without relying on MessageQueue internals.

Static final fields now truly final

Starting from Android 17, apps targeting SDK 37 or higher won’t be able to modify “static final” fields, allowing the runtime to apply performance optimizations more aggressively. An attempt to do so via reflection (or deep reflection) will lead to an IllegalAccessException being thrown. Modifying them via JNI’s SetStaticField methods family will immediately crash the application.

Custom notification view restrictions

To reduce memory usage we are further restricting the size of custom notification views. This update closes a loophole that allows apps to bypass existing limits using URIs. This behavior is gated by the target SDK version and takes effect for apps targeting API 37 and higher.

Privacy & Security

Maintaining user trust is at the heart of the Android ecosystem. Android 17 introduces robust features that protect sensitive data while simplifying user experiences.

Privacy-preserving choices

Historically, apps required broad, permanent permissions to access information like contacts, precise location and media files. Android 17 continues the shift toward privacy-preserving choices that grant temporary, session-based access only to the data the user explicitly selects:

System-Level Contact Picker: Utilizing ACTION_PICK_CONTACTS, apps can request temporary access only to specific fields (e.g., email or phone number) chosen by the user, eliminating the need for the broad READ_CONTACTS permission. It also fully supports work/personal profile separation.
Customizable Photo Picker aspect ratio: UsingPhotoPickerUiCustomizationParams, you can customize the system photo picker to show thumbnails in portrait mode. This is perfect for apps that always display photos and videos in portrait such as video based social media apps.
System-rendered Location Button: A new system-rendered location button that you can embed in your app grants precise location access for the current session only.
EyeDropper API: A new system-level API, ACTION_OPEN_EYE_DROPPER, allows your app to create a system-powered eyedropper enabling the user to select color from any pixel on the display. This provides a secure, privacy-preserving color-picking experience that eliminates the need for broad, sensitive screen capture or media projection permissions.

val eyeDropperLauncher = registerForActivityResult(ActivityResultContracts.StartActivityForResult()) { result ->
   if (result.resultCode == Activity.RESULT_OK) {
       val color = result.data?.getIntExtra(Intent.EXTRA_COLOR, Color.BLACK)
       // Use the picked color in your app
   }
}
fun launchColorPicker() {
   val intent = Intent(Intent.ACTION_OPEN_EYE_DROPPER)
   eyeDropperLauncher.launch(intent)
}

Picking a color from anywhere on the screen with the system EyeDropper

Local network access

Apps targeting Android 17 now either require the ACCESS_LOCAL_NETWORK runtime permission or the use of system-mediated, privacy-preserving device pickers for local network communication, such as talking to smart home devices or casting receivers. Because ACCESS_LOCAL_NETWORK falls under the existing NEARBY_DEVICES permission group, users who have already granted other NEARBY_DEVICES permissions will not be prompted again.

SMS OTP protection

Android 17 expands SMS one-time-password (OTP) protection by delaying access to SMS messages for three hours:

WebOTP Format: Delayed for all apps that are not the intended recipient (domain mismatch).
Standard SMS OTP: Delayed for all apps targeting SDK 37+.
Exemptions: Default SMS, assistant, and connected companion apps are exempt. Apps are strongly encouraged to migrate to the SMS Retriever or SMS User Consent APIs.

Post-Quantum Cryptography (PQC)

Android 17 is ready for the next generation of cryptographic security:

Keystore Integration: Supported devices can generate ML-DSA (Module-Lattice-Based Digital Signature Algorithm) keys in secure hardware to produce quantum-safe signatures, exposed via standard JCA APIs.
Hybrid APK Signing: Introducing the v3.2 APK Signature Scheme, which combines classical signatures with ML-DSA signatures to secure app delivery.

Safer native dynamic code loading

If your app targets SDK 37 or higher, the Safer Dynamic Code Loading (DCL) protection introduced in Android 14 for DEX and JAR files now extends to native libraries. All native files loaded using System.load must be marked as read-only. Otherwise, the system throws UnsatisfiedLinkError

Smarter password protection for physical inputs

With Android 17, we're making it safer to enter passwords, PINs, and other secrets when using a physical keyboard by no longer showing the last typed character by default.

Users can still easily customize these display settings to match their preferences (availability may vary by device manufacturer).

These enhanced privacy protections are automatically supported byAndroid's built-in SDK components and will be supported in Compose 1.12 for SecureTextFields.

Smarter password protection for physical inputs

Media and camera features that empower creators and delight users

Android 17 introduces new creator features that give access to pro-quality cameras and media, all while improving the experience for consumers.

Eclipsa Video: HDR video standard built upon the SMPTE ST 2094-50 specification that introduces new metadata to help devices adapt content for their display headroom and ambient light conditions, as well as improve the simultaneous display of standard and HDR content.
RAW14 image format: New support for the RAW14 image format provides a way for your professional camera app to capture the highest level of detail and color depth from compatible camera sensors.
Vendor-defined camera extensions: Vendor-defined extensions enable hardware partners to define and implement custom camera extension modes, providing access to the best and latest camera features.
Extended HE-AAC software encoder: A new system-provided Extended HE-AAC software encoder, supports both low and high bitrates using unified speech and audio coding, providing significantly better audio quality for voice messages in low-bandwidth conditions, including support for loudness metadata.
Versatile Video Coding (H.266): Enables OEMs to add codec support by defining the video/vvc MIME type in MediaFormat, adding new VVC profiles in MediaCodecInfo, and integrating support into MediaExtractor.
Camera device type: New APIs that query the underlying device type to identify if a camera is built-in hardware, an external USB webcam, or a virtual camera.
Constant Quality for Video Recording: SetVideoEncodingQuality in MediaRecorder configures a constant quality (CQ) mode for video encoders to ensure uniform visual fidelity across the entire video.

Better support for hearing aids

Bluetooth LE Audio hearing aid support: Android now includes a specific device category for Bluetooth Low Energy (BLE) Audio hearing aids with the new AudioDeviceInfo.TYPE_BLE_HEARING_AID constant, so your app can distinguish hearing aids from regular headsets to provide a tailored experience for users with assistive listening devices.
Granular audio routing for hearing aids: Android 17 allows users to independently manage where specific system sounds are played. They can choose to route notifications, ringtones, and alarms to connected hearing aids or the device's built-in speaker, helping to avoid unwanted in-ear interruptions while maintaining a Bluetooth connection for hearing aid management apps.

CameraX and Media3

CameraX and Media3 have been updated for Android 17. They are there to do the heavy lifting, smoothing the rough edges of media development and simplifying building reliable camera capture, smooth media playback, and creative and complex editing experiences.

We've released an agent skill that can migrate legacy Android camera implementations (Camera1 or raw Camera2 APIs) to CameraX.

Note: You'll need to update your CameraX version to either 1.5.2 or 1.6.0+ to avoid a crash related to an added dynamic range mode on Android 17 devices.

Get your apps, libraries, tools, and game engines ready!

If you develop an Android SDK, library, tool, or game engine, it's critical to prepare any necessary updates now to prevent your downstream app and game developers from being blocked by compatibility issues and allow them to target the latest SDK features. Please let your downstream developers know if updates are needed to fully support Android 17.

Testing involves installing your production app or a test app making use of your library or engine using Google Play or other means onto a device or emulator running Android 17 Beta 4. Work through all your app's flows and look for functional or UI issues. Each release of Android contains platform changes that improve privacy, security, and overall user experience; review the app impacting behavior changes for apps running on and targeting Android 17 to focus your testing, including the following:

Resizability on large screens: Once you target Android 17 (SDK 37), you can no longer opt out of maintaining orientation, resizability and aspect ratio constraints on large screens.
Dynamic code loading: If your app targets SDK 37 or higher, the Safer Dynamic Code Loading (DCL) protection introduced in Android 14 for DEX and JAR files now extends to native libraries. All native files loaded using System.load() must be marked as read-only. Otherwise, the system throws UnsatisfiedLinkError.
Enable CT by default: Certificate transparency (CT) is enabled by default. (On Android 16, CT is available but apps had to opt in.)
Local network protections: Apps targeting SDK 37 or higher have local network access blocked by default. Switch to using privacy preserving pickers if possible, and use the new ACCESS_LOCAL_NETWORKpermission for broad, persistent access.
Background audio hardening: Starting in Android 17, the audio framework enforces restrictions on background audio interactions including audio playback, audio focus requests, and volume change APIs. Based on your feedback, we’ve made some changes since beta 2, including targetSDK gating while-in-use FGS enforcement and exempting alarm audio. Full details available in the updated guidance.
NPU access declaration: Apps targeting Android 17 that need to directly access the NPU must declare FEATURE_NEURAL_PROCESSING_UNIT in their manifest to avoid being blocked from accessing the NPU. This includes apps that use the LiteRT NPU delegate, vendor-specific SDKs, as well as the deprecated NNAPI.

Get started with Android 17

Your Pixel device should get Android 17 shortly if you haven't already been on the Android Beta. If you don’t have a Pixel device, you can use the 64-bit system images with the Android Emulator in Android Studio. If you are currently on Android 17 Beta 4.1 and have not yet taken an Android 17 QPR1 beta, you can opt out of the program and you will then be offered the release version of Android 17 over the air.

Getting the Android 17 beta on partner devices

Android 17 is available in beta on handset, tablet, and foldable form factors from partners including Honor, iQOO, Lenovo, OnePlus, OPPO, Realme, Sharp, vivo, and Xiaomi.

For the best development experience with Android 17, we recommend that you use the latest Canary build of Android Studio Quail. Once you’re set up, here are some of the things you should do:

Test your current app for compatibility, learn whether your app is affected by changes in Android 17, and install your app onto a device or Android Emulator running Android 17 and extensively test it.

Thank you again to everyone who participated in our Android developer preview and beta program. We're looking forward to seeing how your apps take advantage of the updates in Android 17, and have plans to bring you updates in a fast-paced release cadence going forward.

For complete information on Android 17 please visit the Android 17 developer site.

Building a Mixed-Reality Tour Guide with Android XR, the Geospatial API, and Gemini

Sat, 25 Jul 2026 10:15:35 +0200

Posted by Coco Fatus, UX Designer, Alon Hetzroni, UX Engineer, Azin Mehrnoosh, Product Manager Android XR

At this year's Google I/O, we announced an update for spatial experiences: the Geospatial API is now available as a preview in ARCore for Jetpack XR. By bringing Google's Visual Positioning System (VPS) to Android XR, Android XR enables anchoring digital content to the physical world with sub-meter accuracy and precise orientation in supported areas.* To explore what the Geospatial API could unlock, our team built a demo: the XR Geospatial Tour.

Imagine walking into a new city, putting on a pair of wired XR glasses (like the upcoming XREAL Project Aura), and instantly having a knowledgeable, local guide showing you around. You don't need to stare down at a 2D map—instead, 3D models gently guide your path, and an intelligent voice tells you about the historical landmarks right in front of you. We combined the Geospatial APIs, Gemini API using Firebase AI Logic, Google Maps Grounding, and Jetpack XR SDK to create a hands-free, immersive walking tour experience.

*Disclaimer: Video and Tour Guide application are for demonstration purposes only. Some sequences have been shortened. Any hardware depicted may be under development; final product details may differ.

Let’s walk through the implementation details and show how we tied these APIs together to build a world-scale spatial experience.

1. Pinpointing the User with ARCore Geospatial API (VPS)

Enhance your navigation experience on XR by combining the power of GPS with the precision of VPS. The accuracy and precise orientation that comes with VPS allows 3D waypoints to align with the physical world.

This is why the Geospatial API on Android XR can help you build custom experiences. By using advanced computer vision, VPS tries to provide a GeospatialPose (including latitude, longitude, and heading) that is more accurate than GPS.

Here's how we retrieve the user's Geospatial pose by mapping the device's orientation to a Geospatial coordinate:

// Retrieve the current geospatial pose from the ARCore session
val result = geospatial.createGeospatialPoseFromPose(arDevice.state.value.devicePose)
if (result is CreateGeospatialPoseFromPoseSuccess) {
    val pose = result.pose
    Log.d("VPS", "Accurate Location: ${pose.latitude}, ${pose.longitude}")
}

Because the entire experience relies on this accuracy, we monitor the horizontalAccuracy and orientationYawAccuracy until they meet our thresholds. If the user is indoors or in an unrecognized area, we prompt them to "walk to an outdoor public space and look around".

2. Crafting the Itinerary with Gemini API & Google Maps Grounding

Once we have a location, we use the Gemini API using Firebase AI Logic to prompt the Gemini model to act as a local tour guide. We pass the user's coordinates to the model and ask it to output a structured JSON response containing nearby walking tours:

   val configForTools = ToolConfig(
      functionCallingConfig = null,
      retrievalConfig = retrievalConfig {
        latLng = FirebaseLatLng(pose.latitude, pose.longitude)
        languageCode = "en"
      }
    )
    val responseJsonSchema = Schema.obj(
      mapOf(
        "locationIntro" to Schema.string(),
        "tours" to Schema.array(
          Schema.obj(
            mapOf(
              "title" to Schema.string(),
              "description" to Schema.string(),
              "stops" to Schema.array(
                Schema.obj(
                  mapOf(
                    "name" to Schema.string(),
                    "detailedName" to Schema.string(),
                    "description" to Schema.string()
                  )
                )
              )
            )
          )
        )
      )
    )
    val model = Firebase.ai(backend = GenerativeBackend.googleAI()).generativeModel(
      modelName = "gemini-3.5-flash",
      tools = listOf(Tool.googleMaps()),
      generationConfig = generationConfig {
        responseMimeType = "application/json"
        responseSchema = responseJsonSchema
      }
    )
   val result = model.generateContent("The user is at latitude ${pose.latitude} and longitude ${pose.longitude}. Generate exactly 3 diverse tours near this location (e.g., historical, food, nature). All tour ideas should be walking distance only.")

Large Language Models are great at generating rich descriptions, but they can sometimes hallucinate exact latitude/longitude coordinates. To solve this, we used Google Maps Grounding to ground the AI.

3. A Voice to Guide You: Gemini 2.5 TTS

To make the tour guide feel truly present, we implemented dynamic voiceovers.

Using the gemini-2.5-flash-tts model, we can configure our model generation config to natively return audio data instead of just text! Here’s how you can request the ResponseModality.AUDIO:

val ttsModel = Firebase.ai(backend = GenerativeBackend.googleAI())
    .generativeModel(
        modelName = "gemini-2.5-flash-tts",
        generationConfig = generationConfig {
            // Instruct the model to return Audio
            responseModalities = listOf(ResponseModality.AUDIO)
        }
    )
val response = ttsModel.generateContent("Say in a neutral but positive voice:\n$prompt")
// Extract the raw audio bytes from the response
val audioBytes = response.candidates.firstOrNull()?.content?.parts
    ?.filterIsInstance()
    ?.firstOrNull { it.mimeType.contains("audio") }?.inlineData

4. Bringing it to Life in 3D with Jetpack XR

The final piece of the puzzle is rendering this data in the user's field of view. The Jetpack XR SDK makes it intuitive to transition from a 2D Android UI to spatial computing.

We used Jetpack Compose for XR to build spatial components. To represent points of interest along the tour, we built a Composable called InfoSphere, which contains a GltfModel of a 3D orb that floats in space and can be interacted with to reveal information.

Using Jetpack XR SDK, we can place 3D models alongside the Compose UI using SpatialBox and SceneCoreEntity. We also used InteractableComponent to respond to user taps.

@Composable
fun InfoSphere(
    content: InfoBubbleContent,
    session: Session,
    sphereModel: GltfModel,
    isSelected: Boolean,
    onClick: () -> Unit
) {
    // SpatialBox lets us arrange 3D components and SpatialPanels together
    SpatialBox(
        SubspaceModifier
            .offset(x = 2.dp, y = 1.dp, z = (-3).dp) // Positioned in 3D space
    ) {
        // Smoothly animate the visibility of our 2D Compose UI Panel
        AnimatedSpatialVisibility(visible = isSelected) {
            SpatialPanel {
                InfoBubble(content) // Regular 2D Compose UI
            }
        }
        // Render our interactive 3D sphere
        SceneCoreEntity(
            factory = {
                GltfModelEntity.create(session, sphereModel).also { entity ->
                    // Make the 3D model respond to user taps
                    entity.addComponent(InteractableComponent.create(session) { inputEvent ->
                        if (inputEvent.action == InputEvent.Action.UP) {
                            onClick()
                        }
                    })
                }
            }
        )
    }
}

By combining AnimatedSpatialVisibility for traditional Compose UI surfaces with SceneCoreEntity 3D elements, we're able to seamlessly blend data into the physical world.

Explore what’s possible with Android XR today

Building the XR Geospatial Tour app showed us that the barrier to entry for world-scale spatial experiences is lower than ever for Android developers. With the Geospatial API now available in preview on Android XR, your apps can seamlessly understand the physical world around them. By combining Compose for XR’s APIs with the high-precision location data of VPS and the generative capabilities of Gemini, we can create experiences that understand both where the user is and what they are looking at.

To help you get hands-on with Android XR, we are thrilled to open applications for the Android XR Developer Catalyst Program, which includes XREAL Project Aura. Starting today, you can apply to get access to an XREAL Project Aura devkit or our display glasses devkit over the coming months!

Android developer verification: Building a safer ecosystem together

Sat, 25 Jul 2026 10:15:33 +0200

Posted by Matthew Forsythe, Director Product Management, Android App Safety

July 15, 2026: Updated Play Console requirements for Play developers

To meet Android developer verification and updated Play Console Requirements, Play developers must register their Play apps in Play Console. While 99% of apps on Play have been registered automatically, you should check your Play Console Home page to register any remaining apps by September 30, 2026 to avoid global removal from Google Play and ensure a seamless user installation experience.

You can also use Play Console to register apps you distribute outside of Google Play to ensure they can be installed on certified Android devices.

Last year, we introduced Android developer verification to strengthen ecosystem security and stop malicious actors from hiding behind anonymity to release harmful apps. Millions of apps have been registered since the verification launched in March, covering nearly all installs on Google Play and a large majority of installs from outside of Google Play. We appreciate the feedback and partnership from industry leaders, developers, and Android communities that helped us design this experience and drive strong adoption.

Initial launch across seven stores and four countries

These new developer verification protections will take effect on September 30, 2026, starting with users in Brazil, Indonesia, Singapore, and Thailand.

This rollout is an industry-wide effort to create a safer ecosystem. We will begin by verifying app installations from the following stores:

Google (Google Play)
Honor (HONOR App Market)
OPlus (OPPO App Market)
Samsung (Galaxy Store)
Transsion (Palm Store)
vivo (V-Appstore)
Xiaomi (GetApps)

Following this initial phase with our partners, we will expand these protections globally for all apps on certified Android devices in 2027.

Automate your workflow with new APIs

To further streamline app registration, we are launching a suite of developer-requested APIs to help you register apps in bulk or directly through your continuous integration and deployment (CI/CD) pipelines. The Android Developer ID Status API will let you check if a package name has already been registered, and the Android Developer Console API will let you register and manage package names directly within your development environment. Both APIs also support OAuth delegation, allowing third-party platforms, like Android app stores, to perform these operations natively on your behalf.

We'll launch these APIs over the next few months.

What’s next

June 2026: Starting this month, we are rolling out a new system service that will be automatically installed on most Android devices. This service will be used later this year to verify developer registration.
July 2026: We’ll launch the Android Developer ID Status API globally and begin early access for the Android Developer Console API. Early access also starts for limited distribution accounts on Android Developer Console. This new type of Android developer account is designed for students, hobbyists, and learners and lets you share your apps to up to 20 devices without a government-issued ID or a fee.
August 2026: Limited distribution accounts and the new Android Developer Console API will launch globally. We’ll also launch an advanced flow for installing apps from unverified developers, which includes security checkpoints to resist coercion scams, while allowing power users to maintain the ability to sideload apps from unverified developers.
September 30, 2026: App registration becomes required for participating stores in Brazil, Indonesia, Singapore, and Thailand. Unregistered apps can be sideloaded with Android Debug Bridge (adb) or advanced flow.
2027 and beyond: After incorporating the feedback from our partners, users, and developer community, we’ll expand the Android verification requirement globally.

Get started with Android developer verification

If you distribute apps in Brazil, Indonesia, Singapore, or Thailand via the stores listed above, please ensure your verification is complete by the September deadline.

Google Play developers: Most Play developers are already verified, and over 99% of their apps have been registered. Go to your Play Console Home page to see your app’s verification status, and register apps you want to continue distributing that weren't automatically registered.
Developers who distribute only outside of Google Play: Sign up for the Android Developer Console today to register your apps.

Students and hobbyists: Sign up here for early access to limited distribution accounts to help us refine the feature with your feedback.

Thank you for helping us build a safer Android ecosystem. Stay tuned for more updates as we approach September and the 2027 global rollout.

Expanded billing choice and lower fees on Google Play

Sat, 25 Jul 2026 10:15:32 +0200

Posted by Paul Feng, Vice President, Google Play Eng, Product, UX

At Google Play, we are committed to delivering the best possible experience to users, while ensuring developers have the tools and adaptability to succeed. Guided by this commitment, earlier this year we announced updates to our business model introducing more billing flexibility, lower fees, and new programs to help your business thrive.

With some of these changes rolling out soon, the breakdown below outlines what is coming, where to find more information, key dates, and how to get started.

More billing flexibility

Google Play’s billing system safely, efficiently, and intuitively handles the complexities of taxes, compliance, and subscriptions across 195+ markets with 300+ local payment methods. However, we understand there are situations where your business needs more flexibility, and that's why we're offering you more options in how you handle digital commerce.

Building from existing programs, the new billing choice program is available to all developers globally who provide digital services or content to users within the United Kingdom and the European Economic Area, alongside programs in the United States. Following this initial phase, we will continue expanding availability to additional markets. You will find the global release schedule at the bottom of this post.

Through these programs, developers can offer an alternative billing system or link users to their own website for purchases, alongside Google Play’s billing. You may also design your own choice screen in accordance with our UX guidelines, as an alternative to Google Play’s default version.

Please find all the details in the program page here.

Lower, separate fees

To enable this new level of flexibility, we're separating our service fee from the billing fee. This starts on June 30, 2026, beginning with the United States, European Economic Area, and United Kingdom.

Regardless of whether you use Google Play's billing system, alternative billing, or external web links, the service fee starts at 10% on your first $1M (USD) in annual earnings. This 10% service fee also applies to all auto-renewing subscriptions. For all other transactions, the rates in the table below applies:

For other transactions, the service fee will be determined by whether the transacting user's install is new or existing relative to the regional rollout date:

New installs: A transaction from a user whose first-time install or first update of the app from Google Play occurred on or after the date that the new fee structure launched in their region.
Existing installs: A transaction from a user whose first-time install or first update of the app from Google Play occurred before the date that the new fee structure launches in their market.

For transactions that use Google Play’s billing system, an additional billing fee applies. In the United States, United Kingdom, and the European Economic Area, the billing fee is set at 5%. We'll announce billing fee details for other markets soon. For transactions processed via alternative billing or external web links, the billing fee does not apply.

Review this Help Center article to understand how these rates apply to your business.

Games Level Up and Apps Experience program guidelines

We are also excited to announce even more opportunities for partners who deliver exceptional user experiences across the Android ecosystem: the revamped Games Level Up and the new Apps Experience program. Detailed guidelines are now available on the respective program websites.

Apps and games that meet all requirements are eligible for a new program rate card with reduced rates. See the table below for details:

Visit the Games Level Up and Apps Experience program websites, review the guidelines, and start preparing your games and apps ahead of September 30, 2026, when the program rate cards officially become available.

Global release schedule

Evolving our business model requires technical infrastructure and alignment with local regulations, so these updates will roll out on a staggered timeline. To help you plan, here is the previously announced release schedule for each update across all markets:

Here is a quick recap of the resources available to help you get started:

Review the billing choice program;
Learn more about Google Play's lower service fees;
Explore detailed guidelines on the Games Level Up and Apps Experience program websites.

We look forward to building the next generation of Google Play experiences together.

Eclipsa Video: HDR That Looks Right on Every Screen

Sat, 25 Jul 2026 10:15:30 +0200

Posted by Tibian Elsheikh, Product Manager, Android Core Graphics and Jeffrey Jose, Product Manager, Android Core Graphics

We’ve all been there: You’re scrolling through your favorite social media feed in a dim room, and suddenly an HDR video pops up. It’s so intensely bright that you have to squint, or maybe you find yourself turning down your screen brightness just to read the caption. Other times, a video that looks vibrant on your phone looks flat, dark, or washed out when you watch it on your living room TV.

While High Dynamic Range (HDR) technology was designed to make videos look richer and more lifelike, the lack of unified industry guidelines means that the exact same clip can render in unexpected and jarring ways depending on the display you’re using.

To solve this, we’re introducing Eclipsa Video—a new standard built to make your favorite videos look consistent, balanced, and comfortable on every screen. Eclipsa Video builds on the open SMPTE ST 2094-50 specification, which Google developed in collaboration with Apple and NBCUniversal.

Sudden brightness spikes during feed scrolling—fixed with Eclipsa Video.

More consistency, comfort, and creative control

Eclipsa Video moves past individual display guesswork. Instead of leaving it up to your device to interpret a video’s brightness on its own, our format carries precise guidelines that tell compatible displays exactly how to render the image.

Designed to scale with your hardware, Eclipsa Video provides three core benefits:

A consistent baseline: Eclipsa Video introduces a shared rulebook for screens. It establishes a consistent benchmark for normal brightness—known as the HDR reference white. This ensures standard text, app interfaces, and standard-range colors remain vibrant and readable without causing uncomfortable screen glare.
Adaptive headroom: Screens have different physical brightness limits, or "headroom." Eclipsa Video guides how displays handle highlights dynamically. Bright details remain brilliant on a premium television, while being scaled intelligently on a mobile screen to prevent sudden blinding transitions.
Preserved creative intent: Rather than applying a single static setting to an entire video, Eclipsa Video carries adaptive, frame-by-frame instructions. Think of it as a set of digital notes from the creator traveling with the video, ensuring the exact colors, contrast, and mood they graded are preserved on your display.

Eclipsa Video preserves true highlight detail on any screen you watch.

Built natively into Android 17

Starting with Android 17, support for Eclipsa Video is built directly into the platform. This means a more comfortable, true-to-life HDR experience is coming natively to the phones, tablets, and TVs you rely on every day. The video you capture carries its creative intent with it, and the video you watch is shown exactly the way it was meant to be seen.

Guidelines for developers & creators

We’re inviting the developer and creator ecosystem to help build a more reliable HDR environment:

Get started with implementation: Learn how to configure playback and capture in your apps with our official guide.
ExoPlayer & Media3 integration: Standard playback handling built directly into Jetpack Media3, allowing ExoPlayer to support Eclipsa Video metadata automatically with no additional player configuration.
Explore open source tools: View and inspect SMPTE ST 2094-50 metadata and dynamic gain curves in real time using HDR Explorer.

What’s next

Eclipsa Video is rolling out now, and you’ll see more apps and devices supporting it over time. Because it’s an open standard, any app developer or hardware manufacturer can integrate it to elevate the viewing experience.

Try out the new tools in Android 17, explore the open-source metadata, and let us know what you think on our developer channels. We can’t wait to see what you create.

Notes & Availability

1. Device Compatibility: Eclipsa Video playback and capture are supported natively on devices running Android 17 (API level 37) and above with HDR displays passing Eclipsa Compliance tests.

2. Developer Resources: The SMPTE ST 2094-50 Specification is openly accessible for technical evaluation.

Android Studio Quail 2 is Stable: Multi-task with the Android Studio AI agent

Sat, 25 Jul 2026 10:15:29 +0200

Posted by Amman Asfaw, Product Manager, Android Studio

Android Studio Quail 2 is now stable and ready for you to use in production, bringing a shift to your IDE with concurrent agentic workflows, natively integrated memory leak profiling, and context-aware crash remediation. Whether you are performing a sweeping architectural overhaul, tracing a memory leak, or resolving a critical production crash, Android Studio keeps you anchored in your workspace by reducing manual friction.

Here’s a deep dive into what’s new:

Multi-tasking with parallel chats

In Android Studio Quail 2, we've been hard at work redesigning Agent Mode from the ground up. This new architecture provides better performance, offers more flexibility for decomposing complex tasks, and improves the suite of internal tools the agent uses to do its work.

In addition to these behind-the-scenes improvements, these changes also allow you to converse across multiple agent chats simultaneously. Waiting for the Android Studio agent to finish a task before you can ask another question or initiate a separate task in Agent Mode is a bottleneck of the past. You can multi-task seamlessly: kick off a UI refactor in one tab, fix a ProGuard rule in a second, and generate documentation in a third.

You can also change which models the agent uses from chat to chat based on the requests you have. Take a look at Android Bench for an analysis of how LLMs perform Android development tasks.

How to use: Click the "+" icon to start a new parallel conversation, and use the History icon to navigate between active tasks. Alternatively, select File > New > New Agent Tab to open a conversation in a dedicated tab.
Note: Worktree support is currently unavailable. Exercise caution when running concurrent chats that modify the same project files, which can potentially lead to editor conflicts.

Run multiple agent tasks in parallel with different models of your choice.

Use the History icon to navigate between active tasks.

Memory leak detection with LeakCanary

Memory leaks in Android occur when your code holds onto an object's reference long after its life cycle has ended. This prevents the Garbage Collector from reclaiming that memory, eventually leading to sluggish performance or OutOfMemoryError.

Hunting down memory leaks can be a tedious, manual task. Starting with Android Studio Quail 2, the popular open-source leak detector LeakCanary is natively integrated directly into the Profiler as a dedicated, first-class task.

This integration transforms your debugging performance by lifting and shifting the heap analysis off your resource-constrained testing phone, and onto your powerful development computer. By running the analysis on your computer, leak tracing is up to five times faster and jank-free, leaving your test app running smoothly on the device.

Once a leak is detected during a profiling session:

The Profiler renders an interactive, color-coded leak trace, grouping occurrences and estimating lost memory.
You can click Go to declaration on any leaking object in the trace to instantly jump to that exact line of code in your editor.
You can click Fix with Agent to have the Gemini agent ingest the trace, explain the root cause of the retained reference, and write the exact code change (such as unbinding a listener or clearing a static reference) to plug the leak.

Review memory leaks identified via LeakCanary through the Fix with Agent button.

App Quality Insights agent integration

Tracking down the root cause of an app crash can require manually synthesizing stack traces, device data, and source code. However Android Studio’s App Quality Insights (AQI) is now fully integrated with Agent Mode to do the heavy lifting for you.

When you click on a crash in the AQI panel, you immediately get a concise, high-level summary of the issue. If you need to dig deeper, simply click See more. This opens a dedicated chat where the agent uses your selected model and pulls in local source code and the full stack trace to deliver a comprehensive explanation of the failure.

With the new agent integration, you move directly from issue identification to resolution. By clicking Fix with AI, the agent will analyze the issue, propose a step-by-step fix plan, and—upon your approval—apply the necessary code changes directly to your project and verify the resulting fix

The Fix with AI button triggering the agent to analyze the issue, then propose the fix

Quality & stability improvements

Beyond new features, we’ve continued our focus on quality by addressing numerous bugs and incorporating the latest stability and performance improvements from the IntelliJ platform, making this a significant enhancement for your daily development.

Get Started

Ready to dive in and accelerate your development? Download Android Studio Quail 2 and start exploring these new features today! As always, your feedback is crucial to us. Check known issues, report bugs, and be part of our vibrant community on LinkedIn, Medium, YouTube, or X.

Build intelligent Android apps: Integrate into Android's intelligence system using AppFunctions

Sat, 25 Jul 2026 10:15:27 +0200

Posted by Ben Weiss, Senior Developer Relations Engineer, Android Developer Relations

Welcome back to the blog post series "Build intelligent Android apps" where we take a basic Android app and transform it into a personalized, intelligent, and agentic experience. In our previous post, we explored how to leverage Firebase AI Logic to build cloud-hosted and hybrid AI features.

Traditional mobile UIs excel at focused, hands-on tasks, and the Android intelligence system is introducing complementary features to make complex, multi-step actions even easier. By supplementing traditional user interfaces, AppFunctions provide a powerful new entry point: A privileged agent on the device can access app features in the background. This can be particularly helpful when users are driving, walking or otherwise multitasking.

In this article, we'll show you how we designed and integrated these capabilities into our travel planning app, JetPacker, using Android AppFunctions. We'll explore the rationale behind our feature choices, discuss the specialized tooling we used to accelerate development, and dive into the code that makes it all work.

Designing AI-ready features: making choices that matter for your users

To select which features to provide to the intelligence system, we looked for tasks where a voice or text command is objectively faster than tapping through screens. In this side-by-side screen recording you can see this contrast perfectly: on the left, a user tapping through multiple screens to log an expense; on the right, the same task completed instantly in the background via a privileged agent.

Our first choice was expense tracking. Logging a coffee expense during a trip usually takes quite a few taps—unlocking the phone, opening the app, finding the active trip, navigating to the expenses tab, tapping the add button, taking a picture of the receipt, and checking the result. By providing the addExpense and getExpenses features as AppFunctions, the system agent handles the heavy lifting. When the user says, "Add a five-dollar coffee expense to my Paris trip," the agent automatically searches for the correct trip ID in the background and inserts the expense, skipping the manual UI flow entirely.

We also prioritized itinerary management. Finding what activity is next on a busy trip itinerary usually requires scrolling through a dense timeline view. By providing getItinerary and addItineraryEvent to the system, the user can simply ask, "What am I doing next in Paris?" and get an immediate answer.

Finally, we focused on hands-free note capturing. Typing out reminders or notes while walking down a busy street is difficult and unsafe. Exposing a voice note capability allows the user to say, "The flight was amazing, I saw a beautiful sunset and managed to sleep well," and the privileged agent automatically transcribes and saves it directly into the travel database using the addVoiceNote AppFunction.

Android MCP powered by AppFunctions

This entire experience is built on Android MCP. Under this design, the app acts as a local MCP server. Rather than remote APIs, you provide your app features directly to the on-device intelligence system.

Android AppFunctions is the API that brings this concept to life. It reads annotated Kotlin functions and compiles them into type-safe, sandboxed tool definitions that the privileged agent can discover and invoke locally on the device.

Diagram highlighting our apps, the android platform, and system agents coordinate AppFunctions.

Under the Android MCP model, your app acts as a local MCP server that exposes structured tools, while the Android platform serves as the central tool registry. On the MCP client side, agent apps are registered with the intelligence system after being granted system-privileged permissions to access the registry.

When a user interacts with a registered agent, its LLM determines if the request can be handled by an AppFunction, queries the platform's metadata, and executes the appropriate registered functions in the background. This local MCP client-server design gives you full control: you choose exactly which features are accessible to the agent, keeping the rest of your app's data private.

How we accelerated development with Android skills

To streamline the integration process, we leveraged the AppFunctions development skill. The AppFunctions development skill is a complete development companion. It guided us through the entire lifecycle: mapping Kotlin data classes to serialize parameters, generating the necessary Service entry points, refining our KDoc documentation to ensure the LLM understands parameter boundaries, and setting up automated testing using ADB.

Providing app features to the intelligence system

Enough with the theory, let's dive into the implementation.

Configuration and dependency setup

We begin by adding the AppFunctions dependencies. One for the API and one for the Kotlin Symbol Processing compiler.

implementation("androidx.appfunctions:appfunctions:1.0.0-alpha10")
ksp("androidx.appfunctions:appfunctions-compiler:1.0.0-alpha10")

Modeling custom data types

Any custom object exchanged with the agent must be annotated with @AppFunctionSerializable. In our TripSerializable.kt file, we define our trip data model:

@AppFunctionSerializable(isDescribedByKDoc = true)
data class TripSerializable(
    /** The trip's unique identifier. */
    val id: String,
    /** The trip's title. */
    val title: String,
    /** The trip's destination location. */
    val location: String,
    /** The trip's start date in milliseconds. */
    val startDate: Long,
    /** The trip's end date in milliseconds. */
    val endDate: Long,
    /** A list of participants. */
    val participants: List,
)

Providing features using the @AppFunction annotation

Next, the skill wrote the Kotlin functions that perform the database queries and annotate them with @AppFunction. We can view this in searchTrip:

/**
 * Looks for trips based on optional filters like id, title (name), location, and dates.
 *
 * @param id The unique identifier of the trip.
 * @param title The title or name of the trip.
 * @param location The destination location.
 * @param startDate The minimum start date in milliseconds.
 * @param endDate The maximum end date in milliseconds.
 * @return A list of trips matching the filters.
 */
@AppFunction(isDescribedByKDoc = true)
suspend fun searchTrip(
    id: String? = null,
    title: String? = null,
    location: String? = null,
    startDate: Long? = null,
    endDate: Long? = null
): List {
    return withContext(Dispatchers.IO) {
    // implementation
}

Since AppFunctions run on the UI thread by default, we use withContext(Dispatchers.IO) to switch to a background dispatcher. Additionally, we refine our KDoc to use clear, imperative verbs and specify parameter constraints. This documentation compiles directly into the tool's schema, which the privileged agent uses to resolve parameters and handle runtime errors.

The service entry point and Hilt integration

To register these features with the intelligence system, we create an abstract base class that extends AppFunctionService. We annotate it with @AppFunctionServiceEntryPoint:

@RequiresApi(36)
@AndroidEntryPoint
@AppFunctionServiceEntryPoint(
    serviceName = "JetPackerAppFunctionService",
    appFunctionXmlFileName = "jetpacker_app_function_service"
)
abstract class BaseJetPackerAppFunctionService : AppFunctionService() {
    @Inject internal lateinit var tripDao: TripDao
    // DAOs and database references are injected here...
}

During compilation, KSP generates the final concrete service subclass, JetPackerAppFunctionService, as declared with the serviceName parameter. We also register app_metadata.xml in the app's manifest. This file provides global operational rules for JetPacker's declared AppFunctions.

Testing and verifying your AppFunctions

Once implemented, you should verify that your AppFunctions are registered and working correctly.

Running devices or emulators with Android 17 or newer, you can use ADB commands from your terminal to list and invoke your functions. Running adb shell cmd app_function list-app-functions displays all registered functions for your package. You can then execute a specific function and test its database integration by running adb shell cmd app_function execute-app-function while passing a raw JSON parameters string.

Instead of these ADB commands, you can also use the AppFunctions Testing Agent to inspect your configuration, list and execute AppFunctions, and even see how your AppFunctions behave in a real conversational flow.

Wrapping it up

When thinking about app features that can be contributed to the intelligence system using AppFunctions requires a slight shift in how we think about code and documentation. AppFunctions enable you to use this new interaction model for apps, which allows using an agent to access app features..

First, the AppFunctions development skill is an essential lifecycle tool, helping you discover features, implement and refine AppFunctions for your apps. Second, KDoc comments are a compiled API asset; clear parameter descriptions directly impact the execution accuracy of the system agent. Finally, Android MCP provides local-first execution allowing apps to safely collaborate with AI agents.

Contributing app features through AppFunctions makes your application ready for the intelligence system. Let us know how you are adapting your apps for the agentic era!

Learn more

Check out the other parts of this blog post series:
Part 1: Introduction of the app and a high-level overview.
Part 2: On-device intelligence. Deep-dive into ML Kit’s GenAI APIs and Gemini Nano to build privacy-first features like itinerary summarization, receipt parsing, and local audio processing.
Part 3: Hybrid and cloud reasoning. Explore how to use Firebase AI Logic to ground LLM answers in real-world data like Google Maps and web context.
Part 4 (this post!): System integration. Integrating with the Android intelligence system using AppFunctions.
Part 5 (coming soon): In-app agentic workflows. Extend the app with an end-to-end booking assistant powered by A2UI and ADK.

Interested in more on Android Development? Follow Android Developers on YouTube or LinkedIn!

All code snippets in this blog post follow the following copyright notice:

Copyright 2026 Google LLC.
SPDX-License-Identifier: Apache-2.0

Build intelligent Android apps: Introduction to Jetpacker

Sat, 25 Jul 2026 10:15:26 +0200

Posted by Jolanda Verhoef, Senior Developer Relations Engineer, Android Developer Relations

Building GenAI features in your app usually means navigating through various models, APIs and architecture choices:

Execution location: Where does your model run? On device, in the cloud, or both?
Complexity: How complex is your setup? Are you doing a single inference call or do you need a more agentic flow?
In-app or Android System: Should your feature be built into your Android app or does it fit better as an Android system integration?

In this blog post series we'll navigate these choices with you. We will take you along on a journey, starting with a basic mobile app and transforming it into a personalized, intelligent, and agentic experience.

Jetpacker: a demo travel app

Jetpacker is a technical showcase app that our team built from the ground up for this year's Google I/O (built using Antigravity). At its core, Jetpacker helps users plan, explore, and enjoy their next big adventure. It shows an overview of your trips, the itinerary of each trip, and details of each event on that trip. Of course following all best practices of Android development, including a beautifully expressive Material UI design.

And best of all? It's fully open source!

Today we are publishing a series of technical blog posts diving deep into each of these features. We’ll provide detailed implementation steps, code snippets, and architectural insights to help you build your own intelligent Android applications.

On-device intelligence

On-device features in Jetpacker: Summarizing trip itineraries, managing expenses, and voice notes

Using an on-device model comes with no additional cloud inference costs, means you don't have to worry about internet connectivity, and lets users be confident that private information will be processed locally, on the device, without any of their data being sent to the cloud.

In Jetpacker, we chose on-device inference for three of our features:

The trip overview feature transforms a messy, multi-day itinerary into a concise, actionable summary. It leverages Gemini Nano through the ML Kit GenAI APIs to process data locally on the device. We consider this a nice-to-have feature where we don't want to incur extra cloud costs, making on-device inference the right choice.
The expense tracker automatically extracts structured data from receipt images to help users track their travel spending. It uses the multimodal capabilities of Gemini Nano 4 through the ML Kit GenAI APIs. We choose an on-device solution so that any privacy-sensitive information on the receipt images never leaves the user's device.
The audio diary records, transcribes, and categorizes voice notes into relevant trip activities. It is powered by the ML Kit Speech Recognition and GenAI Prompt APIs. We chose an on-device solution for privacy and connectivity reasons.

Cloud & hybrid inference

Cloud and hybrid features in Jetpacker: Museum assistant with web grounding, hybrid restaurant review drafting, and hotel support chat featuring custom-routed live translation.

Sometimes your use-case requires AI models with greater world knowledge or a much larger context window and with greater ability in handling complex tasks. In that case, we can switch from running an on-device model to using a cloud model instead.

Or, if you want to get the best of both worlds, you can use hybrid inference to dynamically choose either a cloud or on-device model at runtime. This allows us to lower costs by moving inference to the device when it is available, but at the same time support all Android devices running the app.

In Jetpacker, we implemented several features using cloud or hybrid inference:

The place Q&A feature answers user questions about specific locations by grounding responses in real-world data. It uses Firebase AI Logic integrated with Google Maps and web context. Using a cloud model is necessary here for its greater world knowledge.
The review drafting feature helps users compose detailed reviews for the places they have visited. It leverages both on-device and cloud models through Firebase AI Logic's new Hybrid inference API. This is a feature we wanted to make available to all app users, so we're using a cloud model as a fallback when an on-device model is unavailable.
The automatic chat translation dynamically translates chat messages in real time to facilitate seamless communication, demonstrating custom hybrid inference logic. Again, we want this feature to be available to all app users, but at the same time have some specific considerations on when to choose on-device versus cloud.

System integration

While not a feature you see in the app itself, the Android system integration opens up the app's core capabilities directly to the Android operating system. It uses the AppFunctions API to integrate with system-level intelligence.

In-app agentic workflows (coming soon!)

The booking assistant shows several in-progress flight bookings, asking the user for input before making a final booking.

Agenticness introduces a higher level of autonomy, enabling models to act as agents. Instead of a single inference call, an agent works towards a specific goal via an orchestration loop that allows it to reason, use tools, and adapt its path. Depending on your requirements, these intelligent agents can run either in the cloud, directly on-device, or in a hybrid setup.

For Jetpacker we added a booking assistant that automates end-to-end booking workflows directly within the application to streamline reservations. It is built using A2UI and ADK running in the cloud. The Android app functions as a front-end to the multi-agentic system running in the cloud.

Learn more

Check out the other parts of this blog post series:

Part 1 (this post!): Introduction of the app and a high-level overview.
Part 2: On-device intelligence. Deep-dive into ML Kit’s GenAI APIs and Gemini Nano to build privacy-first features like itinerary summarization, receipt parsing, and local audio processing.
Part 3: Hybrid and cloud reasoning. Explore how to use Firebase AI Logic to ground LLM answers in real-world data like Google Maps and web context.
Part 4: System integration. Integrating with the Android intelligence system using AppFunctions.
Part 5 (coming soon): In-app agentic workflows. Extend the app with an end-to-end booking assistant powered by A2UI and ADK.

Interested in more on Android Development? Follow Android Developers on YouTube or LinkedIn!

Build intelligent Android apps: On-device inference

Sat, 25 Jul 2026 10:15:25 +0200

Posted by Caren Chang, Developer Relations Engineer, Android Developer Relations

Welcome back to the blog post series "Build intelligent Android apps" where we take a basic Android app and transform it into a personalized, intelligent, and agentic experience. In our previous post we introduced Jetpacker, the demo app we'll use throughout this series.

In this blog post, we will share how you can use Gemini Nano through ML Kit’s Prompt API to build intelligent on-device features.

Building intelligent on-device features refers to the ability to process prompts and data directly on a device without sending data to a server. This offers a few advantages:

User data can be processed locally on the device, preserving user privacy
Functionality of the model is reliable even with spotty or no internet connection
No additional cloud inference cost, since everything runs on the user’s hardware

With the benefits of on-device in mind, we identified three features to add in Jetpacker that can improve the user experience: summarizing trip itineraries, managing expenses, and capturing voice notes.

On-device features in Jetpacker: Summarizing trip itineraries, managing expenses, and voice notes

High quality tailored summarization of short texts

The itinerary screen gives users a quick overview of all activities for a given trip. Since this screen contains a lot of information, it can quickly become overwhelming. To help users prepare without feeling overwhelmed, we can add a ‘Get ready for your trip’ section at the top.

The romantic Paris trip is summarized as a classic Parisian adventure blending art, sights, and delicious food. A tip and some useful phrases are also added.

By inputting a trip itinerary and asking an LLM to summarize it, we can generate a quick summary of the trip along with packing tips and useful local phrases. This is a great use case for an on-device model for several reasons:

Performance and quality: Both the input and output text are relatively short. With that, we can expect the performance and quality of an on-device solution to be on par with more powerful cloud models.
Scalability: Shifting inference on-device allows us to scale this feature from a few users to millions without worrying about managing increasing cloud inference costs.
Low latency and reliability: On-device inference guarantees low latency, providing a reliable experience even when users are offline.

To build with on-device, we use Gemini Nano, Google’s most efficient model optimized for mobile devices. Gemini Nano was first introduced a few years ago, and is now running on over 140 million devices. The latest version of the model, Gemini Nano 4, is built on the architecture foundation of the recently released Gemma 4 model, and is further optimized for maximum battery and performance efficiency.

Using ML Kit’s Prompt API, we can take advantage of Gemini Nano 4’s new model capabilities to prototype our on-device features. We’ll create a prompt that includes the itinerary of a trip and ask the model to generate a summary along with any preparation tips.

// implementation("com.google.mlkit:genai-prompt:1.0.0-beta3") 

// Define the configuration for Gemini Nano 4 E2B preview model
val previewFastConfig = generationConfig {
    modelConfig = modelConfig {
        releaseStage = ModelReleaseStage.PREVIEW
        preference = ModelPreference.FAST
    }
}

val geminiNano2BPreviewModel = Generation.getClient(previewFastConfig)

val tripItinerary = ...

val getReadyForYourTripSummary = geminiNano2BPreviewModel
 .generateContent("Given this trip itinerary: $tripItinerary, 
     generate the following: overall vibe, tips on how to prepare for this
     trip, and common short phrases to learn for the trip.")

Finding the optimal prompt usually requires some iteration, and the AICore app is perfect for this step in the process. After opting into the developer preview option for AICore, we can download preview models such as Gemini Nano 4 to test prompts and see the model’s expected outputs. With a few iterations on the prompt, we were able to improve the speed of the response from 13 seconds to under 2 seconds! Check out the final code implementation and prompt here.

The first iteration of our prompt generated way too many tokens, and optimizing it helped keep responses quick and to the point.

Local processing for sensitive user input

Next, to help users enjoy their trip even more, we’ll build a simple expense manager that takes the manual work out of sorting through receipts and calculating budgets.

Taking a photo of a restaurant bill, data is parsed and shown in the expense overview screen of the app.

Since receipts might contain sensitive information like credit card number and addresses, this is another great use case for an on-device solution. With on-device, users can be confident that private information will be processed locally on the device without any of their data being sent to the cloud.

In addition, Gemini Nano 4 has improved model capabilities for multimodality, especially for image understanding tasks like OCR and visual data extraction, making it a great solution for tasks like extracting information from receipts.

For this use case, the prompt will analyze an image of the receipt, and output information such as: a generated title, amount spent and category of the expense. To ensure the model outputs the information in the preferred format, we can use ML Kit’s Structured Output API to seamlessly output a Kotlin data object that we define.

// implementation("com.google.mlkit:genai-prompt:1.0.0-beta3")
// ksp("com.google.mlkit:genai-schema-compiler:1.0.0-alpha1")

@Generable("Information extracted from an expense receipt")
data class ParsedReceipt(
  @Guide("Generated title for the expense less than 6 words. Based on restaurant or activity name.")
  val title: String,
  @Guide("Total amount of the expense. Look for values at the bottom and words like total or balance due.")
  val amount: Double,
  @Guide("Type of expense", enumValues = ["travel", "food", "shopping", "entertainment", "other"])
  val category: String,
)

val prompt = "Determine if the image is a receipt or expense. 
    If it is NOT a receipt or expense, output the text 'NOT_A_RECEIPT'.
    Otherwise, parse the receipt information."

val request = generateContentRequest(ImagePart(bitmap), TextPart(prompt)) {}
val requestWithStructuredOutput = generateTypedContentRequest(request, ParsedReceipt::class)

// Define the configuration for Gemini Nano 4 E4B preview model  
// When selecting models, you can specify which performance charactertists are most important
//  for your use case. Use ModelPreference.FULL when you want to prioritize reasoning power over speed. 
//  Use ModelPreference.FAST when complex logic is not required and latency is a priority.
val previewFullConfig = generationConfig {
    modelConfig = modelConfig {
        releaseStage = ModelReleaseStage.PREVIEW
        preference = ModelPreference.FULL
    }
}

val geminiNano4BPreviewModel = Generation.getClient(previewFullConfig)
val response = geminiNano4BPreviewModel.generateContent(requestWithStructuredOutput)
val parsedReceipt: ParsedReceipt? = response.candidates.firstOrNull()?.response

Multimodal input

Lastly, to help users record audio memos during the trip, let’s build a fully on-device voice notes feature. Using ML Kit’s Speech Recognition API, we’ll enable users to record short voice notes that are automatically transcribed to text. With the transcribed text, we’ll use ML Kit’s Prompt API to identify which trip activity is associated with the recorded voice note, letting users easily recap their trip as they scroll through the trip’s itinerary.

The Roman holiday itinerary shows voice note extracts.

The ML Kit GenAI Speech Recognition API allows you to transcribe audio content to text fully on-device using two distinct modes. Basic mode uses a traditional on-device speech recognition model and is available on most Android devices with API level 31 and higher. Advanced mode uses Gemini Nano to offer broader language coverage and better quality, and is currently supported on Pixel 10 devices.

For our feature we combine the Speech Recognition API with the ML Kit GenAI Prompt API:

// implementation("com.google.mlkit:genai-prompt:1.0.0-beta3")
// implementation("com.google.mlkit:genai-speech-recognition:1.0.0-alpha1")

val tripEvents = ... 

// Set up speech recognition
val speechRecognizerOptions =
    speechRecognizerOptions {
        locale = Locale.US
        preferredMode = SpeechRecognizerOptions.Mode.MODE_ADVANCED
    }
val speechRecognizer: SpeechRecognizer = SpeechRecognition.getClient(speechRecognizerOptions)

suspend fun transcribeVoiceNote(recognizer: SpeechRecognizer) {
    // Display partial text as the user is recording audio
    var partialTextResponse = ""

    // Display the full text once user is finished recording audio
    var transcription = ""

    val request: SpeechRecognizerRequest
        = speechRecognizerRequest { audioSource = AudioSource.fromMic() }
    recognizer.startRecognition(request).collect { response ->
        when (response) {
            is SpeechRecognizerResponse.PartialTextResponse -> {
                partialTextResponse = response.text
            }
            is SpeechRecognizerResponse.FinalTextResponse -> {
                transcription = response.text
                processAndCategorizeVoiceNote(transcription, tripEvents)
            }
        }
    }
}

fun processAndCategorizeVoiceNote(transcribedVoiceNote: String, events: List) {
    val prompt = "Given the voice note $transcribedVoiceNote
     and the following events for this trip: $events, rewrite this transcription
     to remove filler words. Then, identify which events from the
     list this rewritten transcription matches to."

     // Utilize ML Kit's Prompt API to process voice note and tag it with the relevant trip activities
     Generation.getClient().generateContent(prompt)
}

Conclusion

Using ML Kit’s GenAI APIs, we were able to take advantage of Gemini Nano to develop fully on-device intelligent features for the JetPacker app, and provide an improved user experience without any additional cloud costs.

Check out the full source code for Jetpacker on Github, and watch the video Build Intelligent Android apps with Google’s AI to learn more about how to integrate intelligent features directly into your app using on-device models, cloud-powered reasoning, and the latest agentic frameworks.

Learn more

Check out the other parts of this blog post series:

Part 1: Introduction of the app and a high-level overview.
Part 2 (this post!): On-device intelligence. Deep-dive into ML Kit’s GenAI APIs and Gemini Nano to build privacy-first features like itinerary summarization, receipt parsing, and local audio processing.
Part 3: Hybrid and cloud reasoning. Explore how to use Firebase AI Logic to ground LLM answers in real-world data like Google Maps and web context.
Part 4: System integration. Integrating with the Android intelligence system using AppFunctions.
Part 5 (coming soon): In-app agentic workflows. Extend the app with an end-to-end booking assistant powered by A2UI and ADK.

Interested in more on Android Development? Follow Android Developers on YouTube or LinkedIn!

All code snippets in this blog post follow the following copyright notice:

Copyright 2026 Google LLC.
SPDX-License-Identifier: Apache-2.0

Build intelligent Android apps: Cloud and hybrid inference

Sat, 25 Jul 2026 10:15:23 +0200

Posted by Thomas Ezan, Jolanda Verhoef, Caren Chang, Senior Developer Relations Engineers, Android Developer Relations

Welcome back to the blog post series "Build intelligent Android apps" where we take a basic Android app and transform it into a personalized, intelligent, and agentic experience. In our previous post we explored how to build intelligent on-device features using Gemini Nano through ML Kit's Prompt API.

In this post, we will look at how you can leverage Firebase AI Logic to build cloud-hosted and hybrid AI features:

Grounding answers in real-world context
Routing requests dynamically between cloud and local execution using hybrid inference
Translating content with custom routing systems

Sometimes a use case requires AI models with greater world knowledge, a much larger context window, or the ability to handle complex queries. In those scenarios, we can leverage cloud models.

Other times, you want the best of both worlds: using hybrid inference to run on-device when available to lower costs, while falling back to the cloud to ensure compatibility for all devices.

Cloud and hybrid features in Jetpacker: Museum assistant with web grounding, hybrid restaurant review drafting, and support chat featuring custom-routed live translation.

Let’s look at how we implemented three cloud and hybrid features in Jetpacker:

a museum assistant with web grounding
hybrid restaurant review drafting
hotel support chat featuring custom-routed live translation.

Use LLM grounding for up-to-date informationMuseum assistant chatbot with LLM grounding

The Museum assistant is an interactive chatbot designed to help users plan their museum visits. It provides visitors with up-to-date details regarding specific exhibits, current opening hours, ticket pricing, and more.

Museum assistant is a chatbot that answers questions, such as

‘How can I get a ticket discount for Le Louvre?’

When building AI features, getting the model to answer with fresh, accurate, and specific real-world information is a common challenge. While cloud models possess massive amounts of world knowledge, they might not know about seasonal exhibits or the current day’s opening hours.

Grounding data is added to the context window to enable the model

to answer questions correctly and accurately.

To bridge this gap, we can use grounding techniques to add extra context to the model’s context window. The Firebase AI Logic SDK supports three types of grounding:

URL grounding: Grounding responses using content from a specific webpage (e.g. current ticket prices or museum rules).
Google Search grounding: Letting the model query the real-time Google search index for up-to-date details.
Maps grounding: Using Google Maps location data.

In Jetpacker, we dynamically construct the available tools based on enabled feature flags and initialize the generative model using the Firebase AI SDK:

// implementation("com.google.firebase:firebase-ai-logic")

private var toolList = mutableListOf()

init {
    if (ENABLE_SEARCH_GROUNDING) {
        toolList.add(Tool.googleSearch())
    }
    if (ENABLE_URL_GROUNDING) {
        toolList.add(Tool.urlContext())
    }
}

private val generativeModel = Firebase.ai(backend = GenerativeBackend.googleAI())
    .generativeModel(
        modelName = "gemini-3-flash",
        systemInstruction = content {
            text("You are a helpful museum assistant answering questions about a museum. Use plain text.")
        },
        tools = toolList
    )

When the user queries the assistant, if URL grounding is enabled, we append the specific museum resource URLs directly into the prompt:

val groundingText = if (FeatureFlags.ENABLE_URL_GROUNDING) {
    "\n If the following message above is about the rules and terms to visit Le Louvre, " +
    "if needed answer this urls ${urlList.joinToString()}"
} else {
    ""
}

val prompt = "$text $groundingText"

var response = chat.sendMessage(prompt)

Hybrid inference: On-device review generation with Maps deep link

Not every AI task requires a cloud-based model, and not every device is online. To help developers balance latency, cost, and offline availability, we recently introduced the Firebase API for Hybrid Inference.

In Jetpacker, the restaurant review feature lets users review select topics and automatically drafts a review. To enable this for all users, we prioritize local execution with Gemini Nano, and fall back to cloud models on devices that don’t support Gemini Nano.

The restaurant review feature uses hybrid inference to draft a review based on topics

// implementation("com.google.firebase:firebase-ai-logic")
// implementation("com.google.firebase:firebase-ai-ondevice:16.0.0-beta03")


// Initialize the model with hybrid routing configuration
val reviewModel = Firebase.ai.generativeModel(
    modelName = "gemini-3.1-flash-lite",
    onDeviceConfig = OnDeviceConfig(
        inferenceMode = InferenceMode.PREFER_ON_DEVICE
    )
)

The Hybrid Inference API supports four distinct routing modes:

PREFER_ON_DEVICE: Prioritizes local execution and falls back to cloud if Gemini Nano is unavailable.
PREFER_IN_CLOUD: Prioritizes cloud execution and falls back to on-device if the device goes offline.
ONLY_ON_DEVICE: Restricts execution strictly to the device.
ONLY_IN_CLOUD: Restricts execution strictly to the cloud.

Once the review is generated, we copy it to the clipboard and use an intent to open Google Maps directly to the restaurant's review page, providing a seamless user experience:

private fun copyAndOpenMapsReview(context: Context, reviewText: String, placeId: String) {
    val clipboard = context.getSystemService(Context.CLIPBOARD_SERVICE) as ClipboardManager
    val clip = ClipData.newPlainText("User Review", reviewText)
    clipboard.setPrimaryClip(clip)

    val uri = Uri.parse("https://search.google.com/local/writereview/mobile?placeid=$placeId")
    val intent = Intent(Intent.ACTION_VIEW, uri).apply {
        setPackage("com.google.android.apps.maps")
    }
    context.startActivity(intent)
}

Custom hybrid routing: Hotel support chat translation with simulated personas

The hotel support chat was built to let users finalize logistics and check on hotel details. This feature uses system instructions to configure a localized receptionist assistant. By passing specific information—such as the preferred language and hotel information—in the instructions, we can set up a conversational persona representing a specific hotel.

private val generativeModel = Firebase.ai(backend = GenerativeBackend.googleAI())
    .generativeModel(
        systemInstruction = content {
            text("""
              You are a helpful hotel receptionist at $hotelName only speaking $language. 
              Answer politely in $language. The bar closes at 10pm and breakfast is from 7am to 10am.
              There's someone at the desk 24/7. You can retrieve your luggage from the storage room 
              at the back of the lobby at any time.
              """)
        },
        modelName = "gemini-3-flash-preview"
    )

Because receptionist responses are in the hotel's local language (for example, French for Hotel Le Meurice in Paris), we need to translate messages to the user’s preferred language.

Hotel support chat messages are automatically translated to the user’s preferred language

While hybrid models can configure simple routing preferences, complex scenarios require custom routing logic. In Jetpacker, we implement a custom routing stack that takes into account:

Language identification: Using the on-device ML Kit Language Identification API, we can detect the incoming message language.
On-device translation (Gemini Nano): ML Kit’s Prompt API lets us translate common language pairs directly on the device, saving bandwidth and cloud cost.
Cloud translation (Gemini 3 Flash): For more complex languages, we use Gemini Flash 3 to get a higher quality translation.

// implementation("com.google.android.gms:play-services-mlkit-language-id:17.0.0") 

// ML Kit for Language Identification (powered by Google Play Services)
private val languageIdentifier = LanguageIdentification.getClient()

// On-device translator model (prefer Gemini Nano) for translating common language pairs
private val hybridTranslationModel = Firebase.ai(backend = GenerativeBackend.googleAI())
    .generativeModel(
        modelName = "gemini-3-flash",
        onDeviceConfig = OnDeviceConfig(mode = InferenceMode.PREFER_ON_DEVICE)
    )

// Cloud translator model for more complex language pairs
private val cloudTranslationModel = Firebase.ai(backend = GenerativeBackend.googleAI())
    .generativeModel(
        modelName = "gemini-3-flash"
    )

When a message needs to be translated, we identify the source language and apply our custom routing logic, executing either on-device or cloud translation:

fun translateMessage(message: SupportChatMessage) {
    viewModelScope.launch {
        // 1. Detect language using ML Kit Language Identification
        val sourceLang = try {
            Tasks.await(languageIdentifier.identifyLanguage(message.text))
        } catch (e: Exception) {
            "Undefined"
        }

        // 2. Custom routing: we've verified the translation quality for English and Korean with Gemini Nano, and will translate message on-device for those two languages
        val routeToCloud = sourceLang != "en" && sourceLang != "kr"

        val prompt = "Translate the following text to $selectedLanguage. Just return the translated sentence: ${message.text}."

        val (translatedText, routePrefix) = if (routeToCloud) {
            val result = cloudTranslationModel.generateContent(prompt)
            result.text to "[Cloud]"
        } else {
            val result = hybridTranslationModel.generateContent(prompt)
            result.text to "[On-Device]"
        }

        if (translatedText != null) {
            _translations.update { current ->
                current + (message.id to "$routePrefix: $translatedText")
            }
        }
    }
}

In this example, the custom routing logic only takes into consideration the translation’s source and target language. However, based on your app’s use case, you can expand the routing logic to include other factors such as the on-device model version, network connectivity, battery status, and more.

Securing the AI Pipelines: Firebase App Check

Lastly, using AI in the cloud opens up possibilities of API key abuse or unauthorized billing. To secure API calls, we integrated Firebase App Check using both Play Integrity (production) and the local Debug Provider (for local development or emulators).

In the JetPackerApplication.kt file, we install the debug provider at startup and trigger anonymous authentication to establish a secure user session:

//  implementation("com.google.firebase:firebase-appcheck-playintegrity") 
//  implementation("com.google.firebase:firebase-appcheck-debug")  
//  implementation("com.google.firebase:firebase-auth") 

override fun onCreate() {
    super.onCreate()
    Firebase.initialize(context = this)
    Firebase.appCheck.installAppCheckProviderFactory(
        DebugAppCheckProviderFactory.getInstance()
    )
    Firebase.auth.signInAnonymously()
}

When building locally on an emulator, App Check prints a local token secret to logcat:

Enter this debug secret into the allow list in the Firebase Console: a8c2dd4c-xxxx-xxxx-xxxx-ef6c114ba27e

Once registered in the Firebase console, local requests are fully verified and authenticated by App Check, protecting our backend while letting us test the app locally.

Conclusion

By combining cloud model capabilities (grounding, system instructions) with on-device capabilities (hybrid routing, translation, security app checks), we created a travel app that is smart, secure, and available offline.

Check out the full source code for Jetpacker on GitHub, and explore the Firebase documentation to get started:

Firebase AI Logic Documentation
Firebase Hybrid Inference API

Learn more

Check out the other parts of this blog post series:

Part 1: Introduction of the app and a high-level overview.
Part 2: On-device intelligence. Deep-dive into ML Kit’s GenAI APIs and Gemini Nano to build privacy-first features like itinerary summarization, receipt parsing, and local audio processing.
Part 3 (this post!): Hybrid and cloud reasoning. Explore how to use Firebase AI Logic to ground LLM answers in real-world data like Google Maps and web context.
Part 4: System integration. Integrating with the Android intelligence system using AppFunctions.
Part 5 (coming soon): In-app agentic workflows. Extend the app with an end-to-end booking assistant powered by A2UI and ADK.

Interested in more on Android Development? Follow Android Developers on YouTube or LinkedIn!

All code snippets in this blog post follow the following copyright notice:

Copyright 2026 Google LLC.
SPDX-License-Identifier: Apache-2.0

Optimize your apps for the next generation of Samsung Galaxy devices

Sat, 25 Jul 2026 10:15:16 +0200

Posted by Fahd Imtiaz, Senior Product Manager and Miguel Montemayor, Developer Relations Engineer, Android Developer Experience

Today at Galaxy Unpacked, Samsung unveiled its latest lineup of foldable and wearable devices. For developers, this means that the variety of form factors, screen sizes, and device postures your app needs to support is expanding once again.

With devices like the Galaxy Z Fold8, the ecosystem is expanding to include hardware with a landscape-first natural orientation and a wider aspect ratio in its main display state. Whether a user is unfolding a large display, flipping open a cover screen, or glancing at their wrist, users expect a flawless experience. To help you meet this moment, we’re sharing actionable guidance and new tooling updates to enable you to build adaptively proactively.

Rethink layout architecture for dynamic displays, including ultra-wide foldables

Building for the latest foldables means dropping assumptions about display orientation and size. This is especially true for the Galaxy Z Fold8, which adopts an ultra-wide display, adding to the variety of aspect ratios to account for. Devices with this landscape-first natural orientation show the limitations of hardcoded layout rules when users unfold the device. That’s why we’ve introduced dedicated guidance for building for landscape foldables and trifolds.

To build a responsive UI that handles these physics seamlessly, focus on the following core pillars:

Build fluid, adaptive layouts: Wide aspect ratios and compact vertical heights require fluid UIs that scale responsively. Our updated adaptive design guidance advises considering the window class width first to determine layout changes, then adjusting for height. To let individual components fluidly adapt to the grid, structure your layout using flexible containers that allow your content to automatically wrap, span, and reflow. For design inspiration browse our adaptive sample app and dual-screen design galleries.
Track actual app space: Your app's display space rarely matches the physical device size, especially on an ultra-wide screen during multi-window, split-screen, or multitasking states. Sometimes even the orientations differ. Leverage Window Size Classes using the Jetpack Window Manager library to calculate the exact space your app occupies.

Leverage the latest Jetpack Compose Update: Start by adopting the stable Jetpack Compose April '26 release (Compose BOM version 2026.04.01).Take advantage of the new structural layout tools to manage complex architectures. The new Grid API allows you to define dynamic tracks and column spans without the performance overhead of a lazy list. Pair Grid with the new FlexBox layout API to easily handle multi-axis alignment and dynamic item wrapping. You can also use the new MediaQuery API to adapt your UI to its environment, using conditions to detect signals like device posture, window size, and keyboard types.
Make your app fold aware: Use the Jetpack WindowManager library, which provides an API surface for foldable device window features such as folds and hinges. When your app is fold aware, it can adapt its layout to avoid placing important content in the area of folds or hinges and use folds and hinges as natural separators.
Maintain app continuity: Avoid breaking the user journey when the device configuration shifts. Retain your UI state using ViewModel to ensure smooth transitions when a user folds or unfolds their device.

Ensure seamless camera capture on foldable devices

Camera implementation on foldables brings unique hardware quirks. Moving from a compact outer display to an expanded inner display introduces distinct layout aspect ratios while device rotation remains unchanged. If an app assumes a fixed portrait relationship between the camera sensor and the device layout, the app will likely suffer from sideways, stretched, or cropped previews during these folding transitions.

When optimizing your app's media pipeline, migrate your capture experiences to CameraX using the CameraX migration skill. The library’s PreviewView automatically handles sensor orientation, device rotation, and scaling behind the scenes. This guarantees a clean, stable preview regardless of how the user holds or positions the device. If you are maintaining an existing Camera2 codebase, integrate the CameraViewfinder library to apply these complex aspect ratio and rotation transformations automatically without needing a total architecture overhaul.

Extend glanceable interactions to Wear OS 7

The opportunity to build for this new generation of devices extends right to the wrist. Launching with Wear OS 7, Wear Widgets give you a fresh surface to provide users with instant, glanceable access to their essential updates. You can build these highly expressive experiences using Jetpack Glance and RemoteCompose. Crucially, Widgets built with this framework can now populate multi-widget tiles that were previously reserved for first-party widgets.

Build intelligent features

Gemini intelligence already completes tasks on users’ behalf, and you can experiment with the intelligence system by sharing your apps capabilities.

Samsung’s new foldable devices come with Gemini Nano 4, our latest on-device model. Nano 4 provides support for over 140 languages, better multimodal understanding, and much more. Use ML Kit’s Prompt API with advanced features like structured output and thinking mode to build intelligent features on-device.

Start optimizing today

The tools and frameworks are ready to help you optimize your app for all screen sizes. Begin by exploring our guidance for building adaptive apps to learn more about core adaptive design principles.

To dive deeper, check out our comprehensive YouTube playlist. Finally, ensure your app delivers a flawless, premium experience on the newest form factors by reviewing our dedicated quality guidelines for trifolds and landscape foldables and WearOS.

Unfold the future today!

Ihr räumt euer Android-Handy auf und der Speicher ist trotzdem voll? Dann macht ihr einen Fehler, den fast jeder macht

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App lange drücken, deinstallieren, fertig – so löschen die meisten ihre Android-Apps. Das Problem: Dabei bleiben unsichtbare Datenreste zurück, die sich über Monate ansammeln und euren Speicher still und leise zumüllen. Wir zeigen euch, wie ihr Apps wirklich restlos entfernt und welchen einen Schritt ihr dabei auf keinen Fall überspringen solltet.

Decades-Old Bash Tricks Expose AI Coding Agents To Supply Chain Attacks

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Slashdot reader wiredmikey writes: AI security researchers have uncovered a structural security flaw dubbed GuardFall that allows decades-old Bash shell tricks to bypass safeguards in most open source AI coding agents. By exploiting shell behaviors such as quote removal and variable expansion, attackers can hide malicious commands in repositories, README files, Makefiles, or other content consumed by AI agents. If executed — particularly in auto-approve or CI environments—the commands can steal credentials, compromise developer systems, or enable software supply chain attacks. According to researchers at Adversa AI, the 11 popular open source AI coding agents tested, only one successfully blocked all of the Bash trick techniques.

Read more of this story at Slashdot.

Pro-Russia Hacktivists Conduct Opportunistic Attacks Against US and Global Critical Infrastructure

Sat, 25 Jul 2026 09:15:46 +0200

Summary

Note: This joint Cybersecurity Advisory is being published as an addition to the Cybersecurity and Infrastructure Security Agency (CISA) May 6, 2025, joint fact sheet Primary Mitigations to Reduce Cyber Threats to Operational Technology and European Cybercrime Centre’s (EC3) Operation Eastwood, in which CISA, Federal Bureau of Investigation (FBI), Department of Energy (DOE), Environmental Protection Agency (EPA), and EC3 shared information about cyber incidents affecting the operational technology (OT) and industrial control systems (ICS) of critical infrastructure entities in the United States and globally.

FBI, CISA, National Security Agency (NSA), and the following partners—hereafter referred to as “the authoring organizations”—are releasing this joint advisory on the targeting of critical infrastructure by pro-Russia hacktivists:

U.S. Department of Energy (DOE)
U.S. Environmental Protection Agency (EPA)
U.S. Department of Defense Cyber Crime Center (DC3)
Europol European Cybercrime Centre (EC3)
EUROJUST – European Union Agency for Criminal Justice Cooperation
Australian Signals Directorate’s Australian Cyber Security Centre (ASD’s ACSC)
Canadian Centre for Cyber Security (Cyber Centre)
Canadian Security Intelligence Service (CSIS)
Czech Republic Military Intelligence (VZ)
Czech Republic National Cyber and Information Security Agency (NÚKIB)
Czech Republic National Centre Against Terrorism, Extremism, and Cyber Crime (NCTEKK)
French National Cybercrime Unit – Gendarmerie Nationale (UNC)
French National Jurisdiction for the Fight Against Organized Crime (JUNALCO)
German Federal Office for Information Security (BSI)
Italian State Police (PS)
Latvian State Police (VP)
Lithuanian Criminal Police Bureau (LKPB)
New Zealand National Cyber Security Centre (NCSC-NZ)
Romanian National Police (PR)
Spanish Civil Guard (GC)
Spanish National Police (CNP)
Swedish Polisen (SC3)
United Kingdom National Cyber Security Centre (NCSC-UK)

The authoring organizations assess pro-Russia hacktivist groups are conducting less sophisticated, lower-impact attacks against critical infrastructure entities, compared to advanced persistent threat (APT) groups. These attacks use minimally secured, internet-facing virtual network computing (VNC) connections to infiltrate (or gain access to) OT control devices within critical infrastructure systems. Pro-Russia hacktivist groups—Cyber Army of Russia Reborn (CARR), Z-Pentest, NoName057(16), Sector16, and affiliated groups—are capitalizing on the widespread prevalence of accessible VNC devices to execute attacks against critical infrastructure entities, resulting in varying degrees of impact, including physical damage. Targeted sectors include Water and Wastewater Systems, Food and Agriculture, and Energy.

The authoring organizations encourage critical infrastructure organizations to implement the recommendations in the Mitigations section of this advisory to reduce the likelihood and impact of pro-Russia hacktivist-related incidents. For additional information on Russian state-sponsored malicious cyber activity, see CISA’s Russia Threat Overview and Advisories webpage.

Download the PDF version of this report:

Pro-Russia Hacktivists Conduct Opportunistic Attacks Against US and Global Critical Infrastructure (PDF, 1.53 MB )

Background and Development of Pro-Russia Hacktivist Groups

Over the past several years, the authoring organizations have observed pro-Russia hacktivist groups conducting cyber operations against numerous organizations and critical infrastructure sectors worldwide. The escalation of the Russia-Ukraine conflict in 2022 significantly increased the number of these pro-Russia groups. Consisting of individuals who support Russia’s agenda but lack direct governmental ties, most of these groups target Ukrainian and allied infrastructure. However, among the increasing number of groups, some appear to have associations with the Russian state through direct or indirect support.

Cyber Army of Russia Reborn

The authoring organizations assess that the Russian General Staff Main Intelligence Directorate (GRU) Main Center for Special Technologies (GTsST) military unit 74455—tracked in the cybersecurity community under several names (see Appendix B: Additional Designators Used for Cited Groups)—is likely responsible for supporting the creation of CARR —also known as “The People’s Cyber Army of Russia”—in late February or early March of 2022. Actors suspected to be from GRU unit 74455 likely funded the tools CARR threat actors used to conduct distributed denial-of-service (DDoS) attacks through at least September 2024.

In April 2022, the group began using a new Telegram channel featuring the name “CyberArmyofRussia_Reborn” to organize and plan group actions. The channel creators recruited actors to use CARR as an unattributable platform for conducting cyber activities beneath the level of an APT, aimed at deterring anti-Russia rhetoric. CARR threat actors presented themselves as a group of pro-Russia hacktivists supporting Russia’s stance on the Ukrainian conflict, and they soon began claiming responsibility for DDoS attacks against the U.S. and Europe for supporting Ukraine.

CARR documented these actions through embellished images and videos shared on their social media channels, promoting Russian ideology, disseminating talking points, and publicizing leaked information from hacks attributed to Russian state threat actors.

In late 2023, CARR expanded their operations to include attacks on industrial control systems (ICS), claiming an intrusion against a European wastewater treatment facility in October 2023. In November 2023, CARR targeted human-machine interface (HMI) devices, claiming intrusions at two U.S. dairy farms.

The authoring organizations assess that by late September 2024, CARR channel administrators became dissatisfied with the level of support and funding provided by the GRU. This dissatisfaction led CARR administrators and an administrator from another hacktivist group, NoName057(16), to create the Z-Pentest group, employing the same tactics, techniques, and procedures (TTPs) as CARR but separate from GRU involvement.

NoName057(16)

The authoring organizations assess that the Center for the Study and Network Monitoring of the Youth Environment (CISM), established on behalf of the Kremlin, created NoName057(16) as a covert project within the organization. Senior executives and employees within CISM developed and customized the NoName057(16) proprietary DDoS tool DDoSia, paid for the group’s network infrastructure, served as administrators on NoName057(16) Telegram channels, and selected DDoS targets.

Active since March 2022, NoName057(16) has conducted frequent DDoS attacks against government and private sector entities in North Atlantic Treaty Organization (NATO) member states and other European countries perceived as hostile to Russian geopolitical interests. The group operates primarily through Telegram channels and used GitHub, alongside various websites and repositories, to host DDoSia and share materials and TTPs with their followers.

In 2024, NoName057(16) began collaborating closely with other pro-Russia hacktivist groups, operating a joint chat with CARR by mid-2024. In July 2024, NoName057(16) jointly claimed responsibility with CARR for an alleged intrusion against OT assets in the U.S. The high degree of cooperation with CARR likely contributed to the formation of Z-Pentest, which is composed of actors and administrators from both teams, in September 2024.

Z-Pentest

Established in September 2024, Z-Pentest is composed of members from CARR and NoName057(16). The group specializes in OT intrusion operations targeting globally dispersed critical infrastructure entities. Additionally, the group uses “hack and leak” operations and defacement attacks to draw attention to their pro-Russia messaging. Unlike other pro-Russia hacktivist groups, Z-Pentest largely avoids DDoS activities, claiming OT intrusions as attempts to garner more attention from the media.

Shortly after Z-Pentest’s inception, the group announced alliances with CARR and NoName057(16), possibly to leverage the other groups’ subscribers to grow the new channel. In March 2025, Z-Pentest posted evidence claiming OT device intrusions to their channel using a NoName057(16) cyberattack campaign hashtag. Similarly, in April 2025, Z-Pentest shared a video purporting defacement of an HMI by changing system names to NoName057(16) and CARR references. Z-Pentest continues to create new alliances with other groups, like Sector16, to continue growing their subscriber base and incidentally propagate TTPs with new partners.

Sector16

Formed in January 2025, Sector16 is a novice pro-Russia hacktivist group that emerged through collaboration with Z-Pentest. Sector16 actively maintains an online presence, including a public Telegram channel where they share videos, statements, and claims of compromising U.S. energy infrastructure. These communications often align with pro-Russia narratives and reflect their self-proclaimed support for Russian geopolitical objectives.

Members of Sector16 may have received indirect support from the Russian government in exchange for conducting specific cyber operations that further Russian strategic goals. This aligns with broader Russian cyber strategies that involve leveraging non-state threat actors for certain cyber activities, adding a layer of deniability.

Technical Details

Note: This advisory uses the MITRE ATT&CK^® Matrix for Enterprise framework, version 18. See the MITRE ATT&CK Tactics and Techniques section of this advisory for a table of the threat actors’ activity mapped to MITRE ATT&CK tactics and techniques.

TTP Overview

Pro-Russia hacktivist groups employ easily disseminated and replicated TTPs across various entities, increasing the likelihood of widespread adoption and escalating the frequency of intrusions. These groups have limited capabilities, frequently misunderstanding the processes they aim to disrupt. Their apparent low level of technical knowledge results in haphazard attacks where actors intend to cause physical damage but cannot accurately anticipate actual impact. Despite these limitations, the authoring organizations have observed these groups willfully cause actual harm to vulnerable critical infrastructure.

Pro-Russia hacktivist groups use the TTPs in this Cybersecurity Advisory to target virtual network computing (VNC)-connected HMI devices. These groups are primarily seeking notoriety with their actions. While they have caused damage in some instances, they regularly make false or exaggerated claims about their attacks on critical infrastructure to garner more attention. They frequently misrepresent their capabilities and the impacts of their actions, portraying minor incursions as significant breaches, but such incursions can still lead to lost time and resources for operators remediating systems.

Additionally, pro-Russia hacktivists use an opportunistic targeting methodology. They leverage superficial criteria, such as victim availability and existing vulnerabilities, rather than focusing on strategically significant entities. Their lack of strategic focus can lead to a broad array of targets, ranging from water treatment facilities to oil well systems. Pro-Russia hacktivists have demonstrated a pattern of frequently taking advantage of the widespread availability of vulnerable VNC connections. While system owners typically use VNC connections for legitimate remote system access functions, threat actors can maliciously use these connections to broadly target numerous platforms and services. Consequently, these groups can indiscriminately compromise critical infrastructure entities, including those in the Water and Wastewater, Food and Agriculture, and Energy Sectors.

Pro-Russia hacktivist groups have successfully targeted supervisory control and data acquisition (SCADA) networks using basic methods, and in some cases, performed simultaneous DDoS attacks against targeted networks to facilitate SCADA intrusions. As recently as April 2025, threat actors used the following unsophisticated TTPs to access networks and conduct SCADA intrusions:

Scan for vulnerable devices on the internet [T0883] with open VNC ports [T1595.002].
Initiate temporary virtual private server (VPS) [T1583.003] to execute password brute force software.
Use VNC software to access hosts [T1021.005].
Confirm connection to the vulnerable device [T0886].
Brute force the password, if required [T1110.003].
Gain access to HMI devices [T0883], typically with default [T0812], weak, or no passwords [T0859].
Log the confirmed vulnerable device IP address, port, and password.
Using the HMI graphical interface [T0823], capture screen recordings or intermittent screenshots while conducting the following actions, intending to affect productivity and cause additional costs [T0828]:
- Modify usernames/passwords [T0892];
- Modify parameters [T0836];
- Modify device name [T0892];
- Modify instrument settings [T0831];
- Disable alarms [T0878];
- Create loss of view (a technique that mandates local hands-on operator intervention) [T0829]; and/or
- Device restart or shutdown [T0816].
Disconnect from the device, ending the VNC connection.
Research the compromised device company after the intrusion [T1591].

Propagation

To reach a wider audience, pro-Russia hacktivist groups work together, amplify each other’s posts, create additional groups to amplify their own posts, and likely share TTPs. For example, Z-Pentest jointly claimed intrusion of a U.S. system with Sector16. Sector16 later began posting additional intrusions for which the group claimed sole responsibility. It is likely that these and similar groups will continue to iterate and share these methods to disrupt critical infrastructure organizations.

Reconnaissance and Initial Access

The threat actors’ intrusion methodology is relatively unsophisticated, inexpensive to execute, and easy to replicate. These pro-Russia hacktivist groups abuse popular internet-scraping tools, such as Nmap or OPENVAS, to search for visible VNC services and use brute force password spraying tools to access devices via known default or otherwise weak credentials. Threat actors typically search for these services on the default port 5900 or other nearby ports (5901-5910). Their goal is to gain remote access to HMI devices connected to live control networks.

Once threat actors obtain access, they manipulate available settings from the graphical user interface (GUI) on the HMI devices, such as arbitrary physical parameter and setpoint changes, or conduct defacement activities. Because pro-Russia hacktivist groups seem to lack sector-specific expertise or cyber-physical engineering knowledge, they currently cannot reliably estimate the true impact of their actions. Regardless of outcome, pro-Russia hacktivist groups often post images and screen recordings to their social media platforms, boasting the compromises and exaggerating impacts to garner attention from their peers and the media.

Impact

While pro-Russia hacktivist groups currently demonstrate limited ability to consistently cause significant impact, there is a risk that their continued attacks will result in further harm or grievous physical consequences. Attacks have not yet caused injury; however, the attacks against occupied factories and community facilities demonstrate a lack of consideration for human safety.

Victim organizations reported that the most common operational impact caused by these threat actors is a temporary loss of view, necessitating manual intervention to manage processes. However, any modifications to programmatic and systematic procedures can result in damage or disruption, including substantial labor costs from hiring a programmable logic controller programmer to restore operations, costs associated with operational downtime, and potential costs for network remediation.

MITRE ATT&CK Tactics and Techniques

See Table 1 to Table 10 for all referenced threat actor tactics and techniques in this advisory. For assistance with mapping malicious cyber activity to the MITRE ATT&CK framework, see CISA and MITRE ATT&CK’s Best Practices for MITRE ATT&CK Mapping and CISA’s Decider Tool.

*Table 1. Reconnaissance*
Technique Title	ID	Use
Gather Victim Organization Information	T1591	Threat actors use information available on the internet to determine what systems they believe they have compromised and post the information on their social media. This methodology frequently leads to the threat actors misidentifying their claimed victims.
Active Scanning: Vulnerability Scanning	T1595.002	Threat actors use open source tools to look for IP addresses in target countries with visible VNC services on common ports.

*Table 2. Resource Development*
Technique Title	ID	Use
Acquire Infrastructure: Virtual Private Server	T1583.003	Threat actors use virtual infrastructure to obfuscate identifiers.

*Table 3. Initial Access*
Technique Title	ID	Use
Internet Accessible Device	T0883	Threat actors gain access through less secure HMI devices exposed to the internet.

*Table 4. Persistence*
Technique Title	ID	Use
Valid Accounts	T0859	Threat actors use password guessing tools to access legitimate accounts on the HMI devices.

*Table 5. Credential Access*
Technique Title	ID	Use
Brute Force: Password Spraying	T1110.003	Threat actors use tools to rapidly guess common or simple passwords.

*Table 6. Lateral Movement*
Technique Title	ID	Use
Default Credentials	T0812	Threat actors seek and build libraries of known default passwords for control devices to access legitimate user accounts.
Remote Services	T0886	Threat actors leverage VNC services to access system HMI devices.
Remote Services: VNC	T1021.005	Threat actors hunt VNC-enabled devices visible on the internet and connect with remote viewer software.

*Table 7. Execution*
Technique Title	ID	Use
Graphical User Interface	T0823	Threat actors interact with HMI devices via GUIs, attempting to modify control devices.

*Table 8. Inhibit Response Function*
Technique Title	ID	Use
Device Restart/Shutdown	T0816	While threat actors claim to turn off HMIs, it is possible that operators (not the threat actors) turn the devices off during incident response.
Alarm Suppression	T0878	Threat actors use HMI interfaces to clear alarms caused by their activity and alarms already present on the system at the time of their intrusion.
Change Credential	T0892	Threat actors change the usernames and passwords of HMI devices in operator lockout attempts, usually resulting in a loss of view and operators switching to manual operations.

*Table 9. Impair Process Control*
Technique Title	ID	Use
Modify Parameter	T0836	Threat actors attempt to change upper and lower limits of operational devices as available from the HMI.
Unauthorized Command Message	T0855	Threat actors attempt to send unauthorized command messages to instruct control system assets to perform actions outside of their intended functionality, causing possible impact.

*Table 10. Impact*
Technique Title	ID	Use
Loss of Productivity and Revenue	T0828	Threat actors purposefully attempt to impact productivity and create additional costs for the affected entities.
Loss of View	T0829	Threat actors change credentials on HMI devices, preventing operators from modifying processes remotely.
Manipulation of Control	T0831	Threat actors change setpoints in processes, impacting the efficiency of operations for those specific processes.

Incident Response

If organizations find exposed systems with weak or default passwords, they should assume threat actors compromised the system and begin the following incident response protocols:

Determine which hosts were compromised and isolate them by quarantining or taking them offline.
Initiate threat hunting activities to scope the intrusion. Collect and review artifacts, such as running processes/services, unusual authentications, and recent network connections.
Reimage compromised hosts.
Provision new account credentials.
Report the compromise to CISA, FBI, and/or NSA. See the Contact Information section of this advisory.
Harden the network to prevent additional malicious activity. See the Mitigations section of this advisory for guidance.

Mitigations

OT Asset Owners and Operators

The authoring organizations recommend organizations implement the mitigations below to improve your organization’s cybersecurity posture based on the threat actors’ activity. These mitigations align with the Cross-Sector Cybersecurity Performance Goals (CPGs) developed by CISA and the National Institute of Standards and Technology (NIST). The CPGs provide a minimum set of practices and protections that CISA and NIST recommend all organizations implement. CISA and NIST based the CPGs on existing cybersecurity frameworks and guidance to protect against the most common and impactful threats, tactics, techniques, and procedures. Visit CISA’s CPGs webpage for more information on the CPGs, including additional recommended baseline protections.

Reduce exposure of OT assets to the public-facing internet. When connected to the internet, OT devices are easy targets for malicious cyber threat actors. Many devices can be found by searching for open ports on public IP ranges with search engine tools to target victims with OT components [CPG 3.S].
- Asset owners should use attack surface management services and web-based search platforms to scan the internet. This mitigation can help identify if there are VNC systems exposed within the IP ranges they own, especially for connections set up by third parties.
  Note: For more information on attack surface management, see CISA’s Internet Exposure Reduction Guidance, CISA’s Cyber Hygiene Services for U.S. critical infrastructure, and NSA’s Attack Surface Management for the U.S. Defense Industrial Base.
- Implement network segmentation between IT and OT networks. Segmenting critical systems and introducing a demilitarized zone (DMZ) for passing control data to enterprise logistics reduces the potential impact of cyber threats and the risk of disruptions to essential OT operations [CPG 3.I].
- Consider implementing a firewall and/or virtual private network if exposure to the internet is necessary for controlling access to devices.
  - Consider disabling public exposure by default and implementing time-limited remote access to reduce the amount of time systems are exposed.
  - Restrict and monitor both inbound and outbound traffic at OT perimeter firewalls. Configure OT perimeter firewalls to enforce a default-deny policy for all traffic. Asset owners should explicitly permit authorized destinations and protocols based on operational requirements.
  - Implement strict egress filtering to prevent unauthorized data exfiltration or command-and-control callbacks.
  - Regularly audit firewall rulesets and monitor outbound traffic patterns for anomalies indicative of threat actor activity, such as beaconing or unexpected protocol usage.
Adopt mature asset management processes, including mapping data flows and access points. Generating a complete picture of both OT and IT assets provides visibility to operators and management, allowing organizations to monitor and assess deviations for criticality [CPG 2.A].
- Keep remote access services updated with the latest version available and ensure all systems and software are up to date with patches and necessary security updates.
  - Keep VNC systems updated with the latest version available.
- Refer to the joint Foundations for OT Cybersecurity: Asset Inventory Guidance for Owners and Operators to help with reducing cybersecurity risk by identifying which assets within their environment should be secured and protected.
Ensure OT assets use robust authentication procedures.
- Many devices lack robust authentication and authorization. Devices with weak authentication are vulnerable targets to threat actors using credential theft techniques.
- Implement MFA where possible. Where MFA is not feasible, use strong, unique passwords. Apply password standards for operator-accessible services on underlying OT assets, as well as network devices protecting those services. This is especially important for services that require internet accessibility [CPG 3.A] [CPG 3.B] [CPG 3.C] [CPG 3.F].
- Establish an allowlist that permits only authorized device IP addresses and/or media access control addresses. The allowlist can be refined to operator working hours to further obstruct malicious threat actor activity; organizations are encouraged to establish monitoring and alerting for access attempts not meeting these criteria [CPG 3.E].
- Disable any unused authentication methods, logic, or features, such as default authentication keys and default passwords. Block all unused high ephemeral ports and monitor for attempted connections using standard protocols on non-standard ports [CPG 3.R].
- Authenticate all access to field controllers before authorizing access to, or modification of, a device’s state, logic, program, or filesystems.
Enable control system security features that can separate and audit view and control functions. Limiting remotely accessible or default user accounts to “view-only” removes the potential for impact without exploiting a vulnerability [CPG 3.G].
Implement and practice business recovery/disaster recovery plans. Plans should also take into consideration redundancy, fail-safe mechanisms, islanding capabilities, backup restoration, and manual operation.
- Include scenarios that necessitate switching to manual operations. Maintaining the capability of an organization to revert to manual controls to quickly restore operations is vital in the immediate aftermath of a cyber incident [CPG 6.A].
- Create backups of the engineering logic, configurations, and firmware of HMIs to enable fast recovery. Organizations should routinely test backups and standby systems to ensure safe manual operations in the event of an incident [CPG 3.O].
Collect and monitor the traffic of OT assets and networking devices. This includes unusual logins or unexpected protocols communicating over the internet, and functions of ICS management protocols that change an asset’s operating mode or modify programs.
Review configurations for setpoint ranges or tag values to stay within safe ranges and establish alerting for deviations.
Take a proactive approach in the procurement process by following the guidance outlined in the joint guide Secure by Demand: Priority Considerations for Operational Technology Owners and Operators when Selecting Digital Products.

OT Device Manufacturers

Although critical infrastructure organizations can take steps to mitigate risks, it is ultimately the responsibility of OT device manufacturers to build products that are secure by design. The authoring organizations urge device manufacturers to take ownership of the security outcomes of their customers in line with the joint guide Shifting the Balance of Cybersecurity Risk: Principles and Approaches for Secure by Design Software.

Eliminate default credentials and require strong passwords. The use of default credentials is a top weakness threat actors exploit to gain access to systems.
Mandate MFA for privileged users. Changes to engineering logic or configurations are safety-impacting events in critical infrastructure. MFA should be available for safety critical components at no additional cost.
Practice secure by default principles. OT components were initially designed without public internet connectivity in mind. When internet connection becomes necessary, implementing additional security measures is essential to safeguard these systems. Manufacturers should recognize insecure states and promptly inform users so they can make informed risk decisions.
- Include logging at no additional charge. Change and access control logs allow operators to track safety-impacting events in their critical infrastructure. These logs should be available for no cost and use open standard logging formats.
Publish Software Bill of Materials (SBOMs). Vulnerabilities in underlying software libraries can affect a wide range of devices. Without an SBOM, it is nearly impossible for a critical infrastructure system owner to measure and mitigate the impact of a vulnerability on their existing systems. See CISA’s SBOM webpage for more information.

Additionally, see CISA’s Secure by Design Alert on how software manufacturers can shield web management interfaces from malicious cyber activity. By using secure by design tactics, software manufacturers can make their product lines secure “out of the box” without requiring customers to spend additional resources making configuration changes, purchasing tiered security software and logs, monitoring, and making routine updates.

For more information on secure by design, see CISA’s Secure by Design webpage.

Validate Security Controls

In addition to applying mitigations, the authoring organizations recommend exercising, testing, and validating your organization’s security program against the threat behaviors mapped to the MITRE ATT&CK Matrix for Enterprise framework in this advisory. The authoring organizations recommend testing your existing security controls inventory to assess how it performs against the ATT&CK techniques described in this advisory.

To start:

Select an ATT&CK technique described in this advisory (see Table 1 to Table 10).
Align your security technologies against the technique.
Test your technologies against the technique.
Analyze your detection and prevention technologies’ performance.
Repeat the process for all security technologies to obtain a set of comprehensive performance data.
Tune your security program, including people, processes, and technologies, based on the data generated by this process.

The authoring organizations recommend continually testing your security program, at scale, in a production environment to ensure optimal performance against the MITRE ATT&CK techniques identified in this advisory.

Resources

Entities requiring additional support for implementing any of the mitigations in this advisory should contact their regional CISA Cybersecurity Advisor for assistance. Key resources organizations should reference include:

CISA, EPA, NSA, FBI, ASD’s ACSC, Cyber Centre, BSI, NCSC-NL, and NCSC-NZ’s Foundations for OT Cybersecurity: Asset Inventory Guidance for Owners and Operators offers best practices to assist organizations in identifying and prioritizing which assets should be secured and protected.
CISA, FBI, NSA, EPA, DOE, USDA, FDA, MS-ISAC, Cyber Centre, and NCSC-UK’s guidance on Defending OT Operations Against Ongoing Pro-Russia Hacktivist Activity that can help organizations protect OT systems from pro-Russia hacktivist activity.
NSA and CISA’s guidance on Control System Defense: Know the Opponent helps organizations defend OT and ICS assets against malicious cyber activity.
CISA and EPA’s resource page on Water and Wastewater Cybersecurity to help organizations reduce risks posed by malicious cyber actors targeting water and wastewater systems.
- For additional guidance, see CISA, EPA, and FBI’s fact sheet on Top Cyber Actions for Securing Water Systems.
The Food and Ag-ISAC’s best practices on Food and Ag Cybersecurity: A Guide for Small & Medium Enterprises provides recommendations to help mitigate against cyber threats.
DOE and National Association of Regulatory Utility Commissioners Cybersecurity Baselines for Electric Distribution Systems and Distributed Energy (DER) webpage provides resources for state public utility commissions and utilities, as well as DER operators and aggregators to help mitigate cybersecurity risks.

Additional resources that apply to this advisory include:

EPA’s Cybersecurity for the Water Sector resource page provides organizations with guidance on implementing basic cyber hygiene practices.
CISA’s Cross-Sector Cybersecurity Performance Goals enables critical infrastructure organizations to reduce the likelihood and impact of known risks and adversary techniques.
CISA’s Require Strong Passwords webpage supports small and medium-sized businesses mitigating against malicious cyber activity that targets weak passwords.
CISA, NSA, FBI, EPA, TSA, and international partners’ guidance Secure by Demand: Priority Considerations for Operational Technology Owners and Operators when Selecting Digital Products.
DOE’s guidance on Cyber-Informed Engineering recommends considering cyber-enabled risks during the conception, design, and development phases when manufacturing physical systems.
CISA’s Cyber Hygiene Services help enable critical infrastructure organizations to reduce their exposure to threats by taking a proactive approach to monitoring and mitigating attack vectors.
CISA, NSA, FBI, and international partners’ guidance on Shifting the Balance of Cybersecurity Risk: Principles and Approaches for Secure by Design Software urges software manufacturers to provide customers with products that are safer and more secure.
- See more information in these Secure by Design Alerts: How Manufacturers Can Protect Customers by Eliminating Default Passwords and How Software Manufacturers Can Shield Web Management Interfaces From Malicious Cyber Activity.

Contact Information

U.S. organizations are encouraged to report suspicious or criminal activity related to information in this advisory to CISA, FBI, and/or NSA:

Contact CISA via CISA’s 24/7 Operations Center at contact@cisa.dhs.gov or 1-844-Say-CISA (1-844-729-2472) or your local FBI field office. When available, please include the following information regarding the incident: date, time, and location of the incident; type of activity; number of people affected; type of equipment used for the activity; the name of the submitting company or organization; and a designated point of contact.
For NSA cybersecurity guidance inquiries, contact CybersecurityReports@nsa.gov.

Australian organizations: Visit cyber.gov.au or call 1300 292 371 (1300 CYBER 1) to report cybersecurity incidents and access alerts and advisories.

Canadian organizations: Report incidents by emailing Cyber Centre at contact@cyber.gc.ca.

New Zealand organizations: Report cyber security incidents to incidents@ncsc.govt.nz or call 04 498 7654.

United Kingdom organizations: Report a significant cyber security incident: report.ncsc.gov.uk (monitored 24 hours) or, for urgent assistance, call 03000 200 973.

Disclaimer

The information in this report is being provided “as is” for informational purposes only. The authoring organizations do not endorse any commercial entity, product, company, or service, including any entities, products, or services linked within this document. Any reference to specific commercial entities, products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply endorsement, recommendation, or favoring by FBI and co-sealers.

Acknowledgements

Schneider Electric, Nozomi Networks, Eversource Energy, Electricity Information Sharing and Analysis Center, Chevron, BP, and Dragos contributed to this advisory.

Version History

December 09, 2025: Initial version.

Appendix A: Targeting Methodologies for Pro-Russia Hacktivist Groups

For further information on targeting methodologies for pro-Russia hacktivist groups, see:

CISA’s alert Unsophisticated Cyber Threat Actor(s) Targeting Operational Technology;
The joint fact sheet Primary Mitigations to Reduce Cyber Threats to Operational Technology; and
CISA’s Russia Cyber Threat webpage.

Appendix B: Additional Designators Used for Cited Groups

The cybersecurity industry and cyber actor groups often use various names to reference actor groups. While not exhaustive, the following are the most notable names used within the cybersecurity community to reference the groups in this advisory.

Note: Cybersecurity organizations have different methods of tracking and attributing cyber actors, and this may not be a 1:1 correlation to the authoring organizations’ understanding for all activity related to these groupings.

GRU military unit 74455
- Sandworm Team
- Voodoo Bear
- Seashell Blizzard
- APT44
Cyber Army of Russia Reborn (CARR)
- CyberArmy of Russia
- Народная CyberАрмия (НКА)
- People’s CyberArmy of Russia (PCA)
- Russian CyberArmy Team (RCAT)
NoName057(16)
- NoName057(16) Spain
- NoName057(16) Italy
- NoName057(16) France
Z-Pentest
- Z-Pentest Beograd
- Z-Pentest Alliance
- Z-Alliance

Iranian-Affiliated Cyber Actors Exploit Programmable Logic Controllers Across US Critical Infrastructure

Sat, 25 Jul 2026 09:12:34 +0200

Advisory at a Glance

Title	Iranian-Affiliated Cyber Actors Exploit Programmable Logic Controllers Across US Critical Infrastructure
Original Publication	April 7, 2026
Last Update	July 22, 2026
Executive Summary	The authoring agencies urgently warn U.S. organizations of ongoing Iranian-affiliated cyber targeting of internet-connected operational technology (OT) devices, including programmable logic controllers (PLCs). These actions disrupted PLCs across several U.S. critical infrastructure sectors through malicious project file interactions and manipulation of data on human machine interface (HMI) and supervisory control and data acquisition (SCADA) displays, resulting in operational disruption and financial loss.
Last Update Description	This update adds new guidance on detecting malicious changes in reusable code modules exploited within Rockwell Automation PLC programs. It also expands scope to include observed targeting of Schneider Electric, Siemens, and potentially other branded/manufactured PLCs, emphasizing the importance of restricting direct internet access and providing best practices for secure deployment.
Affected Products	Potentially all internet exposed PLCs, including Rockwell Automation/Allen-Bradley, Schneider Electric, Siemens, and other branded/manufactured PLCs.
Key Actions	Install PLCs consistent with manufacturers' guidelines and security best practices. Remove PLCs from direct internet exposure via secure gateway and firewall; work with IT/OT team members and/or integrators to perform this action. Query available logs for the provided indicators of compromise (IOCs) and check available logs for suspicious traffic on the ports associated with OT devices, including `44818`, `2222`, `102`, and `502`, especially traffic originating from foreign hosting providers. For Rockwell Automation devices, place the physical mode switch on the controller into run position. If you suspect your organization was targeted, including against other branded PLC devices, contact the authoring agencies and PLC manufacturer for guidance.
Indicators of Compromise	For a downloadable copy of July 22, 2026 IOCs, see: AA26-097A STIX XML (July 2026) (29 KB) AA26-097A STIX JSON (July 2026) (30 KB) For a downloadable copy of historical April 7, 2026 IOCs, see: AA26-097A STIX XML (36 KB) AA26-097A STIX JSON (12 KB)
Intended Audience	Organizations: Critical Infrastructure Sectors: Government Services and Facilities, Water and Wastewater Systems (WWS), and Energy Roles: Integrators, asset owners, defensive cybersecurity analysts, OT cybersecurity engineers, cybersecurity architects, secure systems developer

Introduction

Note: This advisory was originally published on April 7, 2026, to provide tactics, techniques, and procedures (TTPs) and indicators of compromise (IOCs) related to ongoing cyber exploitation of internet-connected operational technology (OT) devices by Iranian-affiliated advanced persistent threat (APT) actors. The authoring agencies updated this advisory on July 22, 2026, to add new guidance on detecting malicious changes in reusable code modules leveraged within Rockwell Automation PLC programs. It also expands the manufacturer scope to include observed targeting of Schneider Electric, Siemens, and potentially other branded/manufactured PLCs, emphasizing the importance of restricting direct internet access and providing best practice resources for secure deployment.

The Federal Bureau of Investigation (FBI), Cybersecurity and Infrastructure Security Agency (CISA), National Security Agency (NSA), Environmental Protection Agency (EPA), Department of Energy (DOE), United States Cyber Command – Cyber National Mission Force (CNMF), and Department of the Treasury (Treasury) (hereafter referred to as the “authoring agencies”) are urgently warning U.S. organizations of ongoing cyber exploitation of internet-connected OT devices—including PLCs manufactured by Rockwell Automation/Allen-Bradley, Schneider Electric, Siemens, and potentially other manufactured PLCs—across multiple U.S. critical infrastructure sectors. As a result of this activity, organizations from multiple U.S. critical infrastructure sectors experienced disruptions through malicious interactions with PLC project files¹ and the manipulation of data displayed on human machine interface (HMI) and supervisory control and data acquisition (SCADA) displays. In a few cases, this activity caused operational disruption and financial loss.

The authoring agencies assess a group of Iranian-affiliated APT actors is conducting this activity to cause disruptive effects within the United States. The group targeted devices spanning multiple U.S. critical infrastructure sectors, including Government Services and Facilities (to include local municipalities), Water and Wastewater Systems (WWS), and Energy Sectors. The authoring agencies previously reported on similar activity targeting PLCs by CyberAv3ngers (aka Shahid Kaveh Group)—a cyber threat actor affiliated with Iran’s Islamic Revolutionary Guard Corps (IRGC) Cyber Electronic Command (CEC).

Due to the widespread use of these PLCs, and the potential for additional targeting of other branded OT devices across critical infrastructure, the authoring agencies recommend U.S. organizations urgently review the TTPs and IOCs in this advisory for indications of current or historical activity on their networks, and apply the recommendations listed in the Mitigations section of this advisory to reduce the risk of compromise.

If owners and operators discover an affected internet-accessible device in their environment, additional technical measures may be necessary to evaluate the risk of compromise. Please engage your cyber incident response plans and contact the authoring agencies and applicable vendors through existing support channels available to customers and integrators (see Contact Information) to receive support, mitigation, and investigation assistance.

For more information on Iranian malicious cyber activity, see CISA’s Iran Threat Overview and Advisories webpage and the FBI’s Iran Threat and Iran Cyber Threat Overview webpages.

Download the PDF version of this report:

Iranian-Affiliated Cyber Actors Exploit Programmable Logic Controllers Across US Critical Infrastructure (PDF, 1.09 MB )

(New, July 22, 2026) For a downloadable copy of July 22, 2026 IOCs, see:

AA26-097A STIX XML (XML, 29 KB)
AA26-097A STIX JSON (JSON, 30 KB)

For a downloadable copy of historical April 7, 2026 IOCs, see:

AA26-097A.stix_.xml (XML, 35.97 KB )

AA26-097A.stix_.json (JSON, 11.87 KB )

Background Information

Similar Historical Activity Targeting Programmable Logic Controllers

During a similar campaign beginning in November 2023, the IRGC CEC-affiliated cyber threat actors known as "CyberAv3ngers” targeted U.S.-based PLCs and HMIs, causing disruptive effects. Private industry and open sources also refer to this group as Hydro Kitten, Storm-0784, APT Iran, Bauxite, Mr. Soul, Soldiers of Solomon, UNC5691, and the Shahid Kaveh Group. These attacks compromised at least 75 devices, targeting U.S.-based Unitronics PLC devices with an HMI used across multiple critical infrastructure sectors, including the WWS. APT actors developed and deployed custom ladder logic code to these devices, replacing the valid ladder logic with malicious code that continues to be observed to date.

For more information on this group’s activity, see the joint Cybersecurity Advisory IRGC-Affiliated Cyber Actors Exploit PLCs in Multiple Sectors, Including US Water and Wastewater Systems Facilities.

Ongoing Threat Actor Activity Against U.S.-Based Programmable Logic Controllers

The FBI observed Iranian-affiliated APT actors targeting internet-exposed PLCs with the intent to cause disruptions—including maliciously interacting with project files, and manipulating data displayed on HMI and SCADA displays—to U.S. critical infrastructure organizations. Iranian-affiliated APT targeting campaigns against U.S. critical infrastructure have recently escalated, likely in response to hostilities between Iran, and the United States and Israel.

(New, July 22, 2026) At one U.S. victim, the FBI observed the APT actors download a malicious project file to a targeted PLC using configuration software. Analysis indicated the project file retained ladder logic for downstream function but added logic that overrode specific instruction sets responsible for maintaining safe operating parameters in the victim’s environment.

Since at least March 2026, the authoring agencies identified (through engagements with victim organizations) an Iranian-affiliated APT group disrupted the function of PLCs. Organizations across several U.S. critical infrastructure sectors (including Government Services and Facilities, WWS, and Energy Sectors) deployed these PLCs within a wide variety of industrial automation processes. Some of the victims experienced operational disruption and financial loss.

Technical Details

Note: This advisory uses the MITRE ATT&CK^® Matrix for Enterprise framework, version 19. See the MITRE ATT&CK Tactics and Techniques section of this advisory for tables of the threat actors’ activity mapped to MITRE ATT&CK tactics and techniques.

Initial Access

(Updated, July 22, 2026) The authoring agencies observed Iranian-affiliated APT actors using several foreign-based IP addresses to access internet-facing PLCs manufactured by Rockwell Automation/Allen-Bradley, Schneider Electric, Siemens, and potentially other manufactured PLCs [T0883]. The actors used leased, third-party hosted infrastructure and manufacturers’ PLC programming software to connect to misconfigured victim PLCs. Inbound malicious traffic has been observed targeting PLC devices on the following ports: 44818, 2222, 102, and 502, as well as targeting modems on port 22. Targeted devices include:

Rockwell Automation: CompactLogix and Micro850 PLCs
Schneider Electric: BMX P34/Modicon M340 PLCs
Siemens: S7-1200 series PLCs

Command and Control

(Updated, July 22, 2026) The targeting of ports [T0885] associated with other OT vendors’ protocols suggests these actors are opportunistically targeting devices manufactured by companies other than Rockwell Automation/Allen-Bradley, including Schneider Electric and Siemens. In one reported instance, the actors utilized Dropbear Secure Shell (SSH) software on victim modems to enable them to gain remote access through port 22 [T1219].

Exfiltration

(New, July 22, 2026) The authoring agencies observed Iranian-affiliated APT actors using configuration software—such as Rockwell Automation’s Studio 5000 Logix Designer, Schneider Electric’s EcoStruxure Control Expert, and Siemens’ Totally Integrated Automation (TIA) Portal—on leased, third-party hosted infrastructure to exfiltrate device project files from PLC devices to threat-actor-controlled infrastructure [T1041].

Impact

(Updated, July 22, 2026) After the actors extracted device project files, the FBI and CISA identified the modification and deletion of project file logic, to include Add-On Instructions (AOIs) and data manipulation on HMI and SCADA displays [T1565]. Additionally, the changes disabled critical shutdown and alarm logic, allowing systems to enter unsafe conditions without notifying operators of the anomalies.

Note: An AOI is analogous to a “Function Block” or “User Defined Function Block” used in other PLC vendor programs.

Indicators of Compromise

See Table 1 and Table 2 for recent IP addresses used by the Iranian-affiliated APT actors to communicate with PLCs manufactured by Rockwell Automation/Allen-Bradley, Schneider Electric, and Siemens in the United States.

Disclaimer: The FBI observed the threat actors using the IP addresses listed below in the specified time frames. This data is being provided for customers to query against logs for indications of historical targeting by the Iranian-affiliated APT actors. The authoring agencies recommend organizations investigate or vet these IP addresses prior to taking action, such as blocking.

Table 1. Indicators of Compromise **(New, July 22, 2026)**
Indicator	Beginning of Actor Association	End of Actor Association
185.82.73[.]175	September 2025	February 2026
141.11.164[.]153	January 2026	June 2026
175.110.121[.]42	February 2026	March 2026
175.110.121[.]39	February 2026	March 2026
175.110.121[.]41	February 2026	March 2026
175.110.121[.]107	February 2026	February 2026
192.142.54[.]79	May 2026	June 2026
84.200.205[.]165	May 2026	June 2026
185.225.17[.]225	June 2026	July 2026
79.133.46[.]209	July 2026	July 2026
88.80.150[.]199	July 2026	July 2026
88.80.150[.]200	July 2026	July 2026
88.80.150[.]202	July 2026	July 2026

Table 2. Indicators of Compromise
Indicator	Beginning of Actor Association	End of Actor Association
185.82.73[.]162	January 2025	March 2026
185.82.73[.]164	January 2025	March 2026
185.82.73[.]165	January 2025	March 2026
185.82.73[.]167	January 2025	March 2026
185.82.73[.]168	January 2025	March 2026
185.82.73[.]170	January 2025	March 2026
185.82.73[.]171	January 2025	March 2026
135.136.1[.]133	March 2026	March 2026

MITRE ATT&CK Tactics and Techniques

See Table 3 to Table 6 for all referenced threat actor tactics and techniques in this advisory. The authoring agencies recommend organizations review historical TTPs for similar Iranian-affiliated cyber actor activity in IRGC-Affiliated Cyber Actors Exploit PLCs in Multiple Sectors, Including US Water and Wastewater Systems Facilities. For assistance with mapping malicious cyber activity to the MITRE ATT&CK framework, see CISA and MITRE ATT&CK’s Best Practices for MITRE ATT&CK Mapping and CISA’s Decider Tool.

Table 3. Initial Access
Technique Title	ID	Use
Internet Accessible Device	T0883	The actors accessed and interacted with publicly exposed, internet-accessible PLCs that lacked sufficient network and/or hardening security controls.

Table 4. Command and Control
Technique Title	ID	Use
Commonly Used Port	T0885	The actors leveraged commonly used OT ports to communicate with PLCs.
Remote Access Tools	T1219	The actors deployed Dropbear SSH software on victim modems to enable them to gain remote access through port `22`.

Table 5. Exfiltration **(New, July 22, 2026)**
Technique Title	ID	Use
Exfiltration Over C2 Channel	T1041	The actors used remote, third-party hosted infrastructure as a C2 channel to transfer device project files out of victim environments.

Table 6. Impact
Technique Title	ID	Use
Data Manipulation	T1565	The actors maliciously interacted with project files, including modifying and deleting project file logic, and altered data displayed on HMI and SCADA displays.

Mitigations

The authoring agencies recommend organizations implement the mitigations below to improve your organization’s cybersecurity posture on the basis of the threat actors’ activity. These mitigations align with the Cross-Sector Cybersecurity Performance Goals (CPGs) developed by CISA and the National Institute of Standards and Technology (NIST). The CPGs provide a minimum set of practices and protections that CISA and NIST recommend all organizations implement. CISA and NIST based the CPGs on existing cybersecurity frameworks and guidance to protect against the most common and impactful threats and TTPs. Visit CISA’s CPGs webpage for more information on the CPGs, including additional recommended baseline protections.

Network Defenders

The cyber threat actors accessed PLCs manufactured by Rockwell Automation/Allen-Bradley, Schneider Electric, Siemens, and potentially other branded/manufactured PLCs to cause disruptions to victim systems. To safeguard against this threat and threats to other types of PLCs, the authoring agencies urge organizations to consider the following mitigations.

(Updated, July 22, 2026) In addition to contacting the authoring agencies, organizations and integrators operating PLCs from the manufacturers mentioned in this advisory should review the previously issued guidance to strengthen the security of their OT deployments:

Rockwell Automation: Contact the Rockwell Automation Product Security Incident Response Team (PSIRT) at PSIRT@rockwellautomation.com for questions regarding this guidance, or to report cyber incidents related to Rockwell Automation products.
- Refer to Rockwell Automation Security Advisory SD1771 for recommended PLC hardening measures and configuration guidance.
Schneider Electric: Contact the Schneider Electric Corporate Product Cyber Emergency Response Team (CPCERT) at cpcert@se.com for questions regarding this guidance, or to report cyber incidents related to Schneider Electric products.
- Refer to Schneider Electric’s Recommended Cybersecurity Best Practices and Cybersecurity User Guide for Modicon Controller Platform for guidance on securing and configuring PLCs.
Siemens: Contact Siemens ProductCERT at productcert@siemens.com for questions regarding this guidance, or to report cyber incidents and vulnerabilities related to Siemens products.
- Refer to Siemens Security Bulletin 104599 for a list of security measures to harden PLCs and in-depth configuration guides.
- Siemens users should review the Cybersecurity for Industry Operational Guidelines and implement defense-in-depth controls within their automation systems.

Immediate steps to prevent the attack:

Disconnect the PLC from the public-facing internet [CPG 3.S]. Follow the joint guidance Secure connectivity principles for OT to safely allow remote access. Specifically, “remove inbound port exposure,” so the OT system is never directly exposed to the internet or external networks, and to ensure all access is mediated, monitored, and controlled. Do this through a secure gateway (jump host) that brokers the connection.
- Ensure cellular modems, used for remote field connectivity and access, are secured with strong authentication and updated.
- Enable logs for connected modems and regularly review for suspicious activity to detect intrusions and improve incident response speed.
- (New, July 22, 2026) To mitigate unauthorized access to OT via cellular modems, organizations should consider implementing isolated architectures, such as private Access Point Name (APN), 5G Public Network Integrated Non-Public Network (PNI-NPN), cellular Software-Defined Wide Area Network (SD-WAN), Zero Trust Network Access (ZTNA), or a site-to-site virtual private network (VPN).
(New, July 22, 2026) Strictly control network access to PLC devices.
- Configure firewall rules or access control list (ACL) security features on PLCs or programmable controllers to allow only authorized communications between expected control system devices. Block access from unauthorized or threat actor-controlled IP addresses, such as those associated with hosting providers.
For controllers with a physical mode switch, place the physical mode switch into run position to prevent remote modification. Devices should only be in the program or remote position when updating or downloading software online and immediately switched back to the run position when complete. (See Rockwell Automation’s²System Security Design Guidelines for manufacturer’s instructions.)
- (New, July 22, 2026) Prior to switching the device to run mode, review and validate project files, as changing modes will lock in the current project file downloaded to the device.
For devices that allow software key switching, enable programming protection in PLC configuration software (S7 TIA Portal) to limit who can modify PLCs remotely. (See Siemens’ Cybersecurity for Industry Operational Guidelines for the manufacturer’s instructions.)

Follow-up steps to strengthen security posture:

(New, July 22, 2026) Review project files running on PLCs for unauthorized changes. Use vendor-provided integrity checking tools and visually compare the running program to known good logic. Ensure reusable logic and input/output configurations are valid. For Rockwell Automation PLCs listed in the Customer Guidance to Disconnect Devices from the Internet, check the AOIs for any anomalous modifications.
- If restoring from backups, verify the backup does not contain malicious logic before deployment.
- Review logs and configurations on all connected devices, including modems, HMIs, and workstations, to assess potential lateral movement by threat actors. If it appears the actors connected to additional devices, reimage these devices to remove any potential malicious changes or access tools.
(New, July 22, 2026) Ensure device passwords are changed from their default and are configured to use complex, unique combinations of letters, numbers, and symbols that are not easily guessable. Implementing robust password practices remains a critical security measure that can help prevent unauthorized access and strengthen the overall security posture of OT devices.
(New, July 22, 2026) Take defensive measures to minimize the risk of exploitation. Conduct comprehensive impact analysis and risk assessments prior to deploying defensive measures.
Create and test strong backups of the logic and configurations of PLCs. Store backup files offline and secure the physical removal media to enable fast recovery.
Implement multifactor authentication (MFA) [CPG 3.F] for access to the OT network from an external network.
If remote access is required, implement a network proxy, gateway, firewall, and/or VPN in front of the PLC to control network access.
- A VPN or gateway device can enable MFA for remote access even if the PLC does not support MFA. Implement security rules on these higher-level network security mechanisms to prevent the type of repeated and sustained login attempts seen during a brute force attack. When possible, implement a device control list for workstations sending messages or connecting to OT components.
- Use the device control list to monitor for logon activity for unexpected or unusual access to devices from the internet.
Keep PLC devices updated with the latest software patches issued by the manufacturer. Use established downtime windows to install patches. Known Exploited Vulnerabilities may need to be prioritized outside a downtime window.
Configure external and internal firewalls to block traffic using common ports associated with network protocols that are unnecessary for the particular network segment.
Disable any unused authentication methods, logic, or features, such as default authentication keys and passwords, as well as unused or needed services such as Teletype Network (Telnet), File Transfer Protocol (FTP), Remote Desktop Protocol (RDP), Virtual Network Computing (VNC), and web services.
Monitor asset management systems for device configuration changes, which can be used to understand expected parameter settings.
Monitor the content of network traffic for the following:
- Unusual logins to internet-connected devices or unexpected protocols to/from the internet.
- Functions of industrial control systems management protocols that change an asset’s operating mode or modify programs.
(New, July 22, 2026) Ensure service providers are informed of active threats targeting internet-connected PLC devices. Owners and operators should communicate directly with service providers to address risks, especially when remote monitoring or maintenance is involved. Some service providers may rely on internet connectivity essential to monitor and maintain OT/ICS operations but may not be fully aware of active threats.

In addition, the authoring agencies recommend network defenders apply the following mitigations to limit potential adversarial use of common system and network discovery techniques, as well as reduce the impact and risk of compromise by cyber threat actors:

Reduce risk exposure. CISA offers a range of services at no cost, including scanning and testing, to help organizations reduce exposure to threats via mitigating attack vectors. CISA’s Cyber Hygiene Services can help provide additional review of organizations’ internet-accessible assets.

Device Manufacturers

Note: The following guidance is general in nature and not specific to any OT vendor. Some of the features, settings, and practices may already be offered by certain vendors. The inclusion of this guidance should not be interpreted as an assertion that vendors referenced do not offer such security features. Also, this advisory is not highlighting a new vulnerability in the identified products, but instead discusses opportunistic targeting. Device manufacturers can make opportunistic attacks more difficult at scale by encouraging more secure behavior by default and in operations, as discussed below.

Although critical infrastructure organizations using PLC devices can take steps to mitigate the risks, it is ultimately the responsibility of the device manufacturer to build products secured by design and default. The authoring agencies urge device manufacturers to take ownership of their customers’ security outcomes by following the principles in the joint guide Secure by Demand: Priority Considerations for OT Owners and Operators when Selecting Digital Products, primarily:

Change the manufacturers’ default settings to prevent exposing administrative interfaces to the internet.
Do not charge additional fees for basic security features needed to operate the product securely.
Support MFA, including via phishing-resistant methods.

By using secure by design tactics, software manufacturers can make product lines secure “out of the box” without requiring customers to spend additional resources making configuration changes, purchasing tiered security software and logs, monitoring, and making routine updates.

For more information on common misconfigurations and guidance on reducing their prevalence, see joint advisory NSA and CISA Red and Blue Teams Share Top Ten Cybersecurity Misconfigurations. For more information on secure by design, see CISA’s Secure by Design webpage and joint guide.

Validate Security Controls

In addition to applying mitigations, the authoring agencies recommend exercising, testing, and validating your organization's security program against the threat behaviors mapped to the MITRE ATT&CK for Enterprise framework in this advisory. The authoring agencies recommend testing your existing security controls inventory to assess how they perform against the ATT&CK techniques described in this advisory.

To get started:

Select an ATT&CK technique described in this advisory (see Table 3 to Table 6).
Align your security technologies against the technique.
Test your technologies against the technique.
Analyze your detection and prevention technologies’ performance.
Repeat the process for all security technologies to obtain a set of comprehensive performance data.
Tune your security program, including people, processes, and technologies, based on the data generated by this process.

The authoring agencies recommend continually testing your security program, at scale, in a production environment to ensure optimal performance against the ATT&CK techniques identified in this advisory.

Resources

Authoring Agencies: IRGC-Affiliated Cyber Actors Exploit PLCs in Multiple Sectors, Including US Water and Wastewater Systems Facilities
CISA: Bulletproof Defense: Mitigating Risks From Bulletproof Hosting Providers
EPA: Cybersecurity for the Water Sector
CISA: Water and Wastewater Cybersecurity
CISA: Exploitation of Unitronics PLCs used in Water and Wastewater Systems
CISA: Iran Threat Overview and Advisories
FBI: The Iran Threat and Cyber Threat Overview: Iran
CISA, MITRE: Best Practices for MITRE ATT&CK Mapping
CISA: Decider Tool
CISA: Cross-Sector Cybersecurity Performance Goals 2.0
CISA: No-Cost Cybersecurity Services and Tools
CISA: Secure by Demand: Priority Considerations for OT Owners and Operators when Selecting Digital Products
NSA, CISA: NSA and CISA Red and Blue Teams Share Top Ten Cybersecurity Misconfigurations
CISA: Secure by Design
FBI, CISA: Primary Mitigations to Reduce Cyber Threats to Operational Technology
United Kingdom National Cyber Security Centre: Secure connectivity principles for operational technology

Contact Information

U.S. organizations are encouraged to report suspicious or criminal activity related to information in this advisory to CISA, the FBI, and/or NSA:

Contact CISA via CISA’s 24/7 Operations Center at contact@cisa.dhs.gov or 1-844-Say-CISA (1-844-729-2472). File a claim with FBI’s Internet Crime Complaint Center (IC3) or contact your local FBI field office. When available, please include the following information regarding the incident:
- Date, time, and location of the incident;
- Type of activity;
- Number of people affected;
- Type of equipment used for the activity; and
- Name of the submitting company or organization, and a designated point of contact.
For NSA cybersecurity guidance inquiries, contact CybersecurityReports@nsa.gov.
Entities required to report incidents to DOE should follow established reporting requirements, as appropriate. For other energy sector inquiries, contact EnergySRMA@hq.doe.gov.
Contact the Rockwell Automation PSIRT for questions regarding their guidance or for reporting cyber incidents related to Rockwell Automation products at PSIRT@rockwellautomation.com.
Contact the Schneider Electric CPCERT at cpcert@se.com for questions regarding this guidance, or to report cyber incidents related to Schneider Electric products.
Contact Siemens ProductCERT for up-to-date information about the security of Siemens products or to report cybersecurity vulnerabilities at productcert@siemens.com. For support with increasing the security of installed Siemens PLCs, contact Siemens Industrial Cybersecurity Services at services.automation@siemens.com. See Siemens ProductCERT and Siemens CERT for more information.

Disclaimer

The information in this report is being provided “as is” for informational purposes only. CISA and the authoring agencies do not endorse any commercial entity, product, company, or service, including any entities, products, or services linked within this document. Any reference to specific commercial entities, products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply endorsement, recommendation, or favoring by CISA and the authoring agencies.

Version History

April 7, 2026: Initial version.

July 22, 2026: Update includes new guidance on detecting malicious activity, expanded scope of observed targeting, and best practices for secure PLCs deployment.

Notes

¹Project file refers to the software file that contains ladder logic and configuration settings. On Rockwell Automation devices, it is referred to as an .ACD file.

²See CompactLogix 5370 Controllers (Chapter 5: “Select the Operating Mode of the Controller”) for more information on functions available for the switch.

Android 17: Googles neues Update-System überholt sich schon wieder selbst – warum eigentlich? (Meinung)

Sat, 25 Jul 2026 08:54:32 +0200

Seit einiger Zeit haben es die Android-Entwickler sehr eilig und auch unter Android 17 saust Google durch die Versionen, dass einem fast schon schwindlig werden kann. In dieser Woche hat man das nächste Kapitel eröffnet, bevor das vorherige überhaupt zu Ende gebracht worden ist. Man muss sich wirklich fragen, was mit dem neuen QPR-System bezweckt wird, das sich immer wieder selbst überholt.

Mehr lesen: Android 17: Googles neues Update-System überholt sich schon wieder selbst – warum eigentlich? (Meinung)

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Firefox Nightly: Backup for a Rainy Day – These Weeks in Firefox: Issue 202

Sat, 25 Jul 2026 08:37:35 +0200

Highlights

The profile backup mechanism has been enabled by default for all desktop platforms in Nightly, as well as Beta! The current plan is to have this ride out to Firefox 151 for Windows, macOS and Linux on May 18th!
- This feature, when enabled, will create a copy of your profile data in the background and store it in a single file on your file system that you can restore from.
- You will be able to manage this feature in Settings under Sync (for now)
- You can read more about the feature here
As followups to the recent addition to the WebExtension tabs API to support the new SplitView tabs feature, tabs.group() and tabs.ungroup() have been fixed to work correctly with split view tabs, and fixed split views being prepended instead of appended to tab groups when adopted into a new window – Bug 2029099 / Bug 2029534
Adaptive autofill has been enabled on Nightly.
- Previously, autofill only completed domains (e.g. typing red autofilled reddit.com). Now it can also complete full URLs for pages you visit often (e.g. red → reddit.com/r/firefox), learning from what you actually click in the address bar. If a suggestion isn’t helpful, you can now dismiss it so autofill learns what not to show you too.
  - If you run into issues or have feedback, you can file a bug here!
Markus Stange [:mstange] implemented dynamic toolbar on top in RDM (#1978145), but also implemented some static skeleton UI so it’s closer to what we actually have in Firefox for Android
- dynamic toolbar is behind a pref: devtools.responsive.dynamicToolbar.enabled
- it can be put on top by setting devtools.responsive.dynamicToolbar.onTop, otherwise it’s at the bottom

Friends of the Firefox team

Resolved bugs (excluding employees)

Script to find new contributors from bug list

Volunteers that fixed more than one bug

Amin Amir
aoia7rz7l
Chukwuka Rosemary
DrSeed
Frédéric Wang Nélar
japandi
John Iweh
jonathancabera
Josh Aas
Keji Bakare
kofoworola shonuyi
konyhéa
liz
Mathew Hodson
Okhuomon Ajayi
Oluwatobi
ROSHAAN
Sam Johnson

New contributors (🌟 = first patch)

Project Updates

Add-ons / Web Extensions

Addon Manager & about:addons

In preparation for the Project Nova restyling of the about:addons page, we have refactored about:addons into separate per-component ES modules, splitting the monolithic aboutaddons.js and aboutaddons.html into 16 dedicated component files under components/ (with no behavior or UI changes) – Bug 2032014
- NOTE: if you have working on patches with changes to about:addons internals it is very likely you’ll need to rebase and solve merge conflicts hit on top of this refactoring, the internals are still largely the same as before but don’t hesitate to reach out to the Addons team if you have doubts / questions or need help to figure out how to adapt your patch of top of these changes

WebExtensions Framework

Fixed exportFunction to preserve the constructibility of the wrapped function instead of unconditionally making all exported functions implicitly as constructors – Bug 2033173
- Thanks to Gregory Pappas for contributing this improvement to the Content Scripts’ Xray Wrappers helpers!
Fixed a Firefox 151 regression where extension content scripts accessing location.ancestorOrigins caused subsequent page script reads of the same property to fail with “Permission denied”, breaking sites like Gmail – Bug 2034329
- Thanks to Simon Farre for promptly investigating and fixing this recent regression!

WebExtension APIs

Updated sessions.getRecentlyClosed() to remove the hardcoded cap when maxResults is omitted – Bug 1392125
- Shoutout to Amine Zroual for contributing this enhancement to the sessions WebExtensions API!

DevTools

Artem Manushenkov fixed an issue where autosuggestion popup was removing overridden indicators from properties in the Inspector (#1983408)
Andrea Marchesini [:baku] fix DevTools cookie header serialization for long cookies, which could lead to cookies not being visible in Netmonitor (#2031299)
Julian Descottes [:jdescottes] fixed a toolbox crash that was happening we couldn’t find a localization file (e.g. when using a language pack on Nightly) (#2028930)
Nicolas Chevobbe [:nchevobbe] improved @container tooltip so it show the value of variables used in style()(#2030239), has enough contrast in dark mode (#2033782) and contains a link to select the container (#2031688)
Hubert Boma Manilla (:bomsy) is making good progress on migrating the Console to CodeMirror 6 (#2032758, #2026569)

Fluent

We’re now at over 72% of our strings being Fluent! Got a component still using .properties? Convert when you can!

Migration Improvements

Thanks to dao for fixing a recent alignment issue in the migration wizard dropdown
Thanks to volunteer contributor Anthony Mclamb for his patch that disables the legacy EdgeHTML Edge migrator! Once that finishes rolling out, presuming no surprises, we’ll go ahead and remove the migrator entirely.

New Tab Page

Nova for New Tab has ridden the trains to Beta! It will be enabled by default, globally, when Firefox 151 goes out to release on May 19th
- It’s possible that we’ll do a train-hop coupled with an experiment to enable HNT Nova for a few clients a bit earlier.
Maxx Crawford enabled Nova designs for New Tab, rolling out the updated layout, widgets, and customization panel behind HNT Nova flags.
Maxx Crawford fixed the Nova content feed to render the intended four‑column layout by correcting CSS grid breakpoints.
Maxx Crawford resolved a first‑load failure in the Weather widget by fixing init order and fetch timing, eliminating the “Oops” error.
Maxx Crawford synchronized the Weather toggle between about:preferences#home and the panel via the shared showWeather pref to prevent desync.
Maxx Crawford updated Nova grid focus order to align tab flow with visual order for keyboard users.
Maxx Crawford fixed critical UI issues in Lists and Timer widgets covering overflow, controls, and layout stability.
Maxx Crawford guarded document.dir access in Nova render paths to avoid startup cache worker errors and improve startup stability.
Rolf added a new normalization method for the inferred interest vector to stabilize topic relevance across sessions.
Rolf prevented unnecessary content refreshes during Pocket New Tab experiments, reducing jank and bandwidth.
Sameeksha defined the Customize button’s expanded/collapsed state programmatically using aria-expanded for better a11y.
liz clarified follow/unfollow/blocked button names with topic context so screen readers announce clear actions.
Vector marked the Delete from History control as opening a dialog via aria-haspopup=dialog for assistive tech.
Scott Downe fixed a regression that flipped the Wallpapers pref off, restoring user selections.
Irene Ni corrected privacy link color and focus styles for contrast and keyboard visibility.
Reem Hamoui added a wallpaper toggle reset in the Nova customization panel so users can quickly restore default wallpapers without extra steps.
Reem Hamoui fixed the Customize pencil button to match the Nova spec, aligning placement and iconography for visual consistency.
Dre updated the ‘Fresh new’ wallpapers copy to a clearer, localized message for better comprehension.
Irene Ni fixed Nova privacy link color and focus styles to meet contrast and focus ring guidelines, improving accessibility on New Tab.
Irene Ni adjusted Sponsored tile character limits to prevent truncation/overflow, yielding cleaner titles across grid and wide tiles.
Scott Downe fixed a regression that flipped the Wallpapers user pref to false, restoring wallpapers for affected users and preventing unintended disablement.
Reem Hamoui hooked the wallpaper check into the new toggle logic so the Customization Panel accurately reflects wallpaper availability and state.
Irene Ni landed Nova UI updates for the Daily Briefing 3-pack card, improving spacing, type scale, and tap targets.
Reem Hamoui added a wallpaper toggle reset in the Nova customization panel so users can quickly restore default wallpapers without extra steps.
Reem Hamoui fixed the Customize pencil button to match the Nova spec, aligning placement and iconography for visual consistency.
Dre updated the ‘Fresh new’ wallpapers copy to a clearer, localized message for better comprehension.
Irene Ni fixed Nova privacy link color and focus styles to meet contrast and focus ring guidelines, improving accessibility on New Tab.
Irene Ni adjusted Sponsored tile character limits to prevent truncation/overflow, yielding cleaner titles across grid and wide tiles.
Scott Downe fixed a regression that flipped the Wallpapers user pref to false, restoring wallpapers for affected users and preventing unintended disablement.
Reem Hamoui hooked the wallpaper check into the new toggle logic so the Customization Panel accurately reflects wallpaper availability and state.
Irene Ni landed Nova UI updates for the Daily Briefing 3-pack card, improving spacing, type scale, and tap targets.

Search and Urlbar

Marco has fixed a couple of issues with the places databases to try and improve stability. This should help with avoiding users losing bookmarks or favicons.
Work continues on the new separate search bar to improve the functionality, e.g. allowing middle click to perform a search in a new tab, avoiding performing a search when adding a search engine.
Work also continues on the new Nova layouts.

Smart Window

uplifted 10 bugs to 150.0.1 dot release addressing initial user feedback from diary study and Connect
- jump to bottom of conversation 2028692
- stop streaming button 2029204
- back/forward navigation from assistant 2029229
- dark mode for various chips 2024499
search engine switching from smart bar 2021973
Nova styling within smart window 2026794

Storybook/Reusable Components/Acorn Design System

Dustin converted moz-breadcrumb-group variables into JSON design tokens Bug 2029181 – Convert moz-breadcrumb-group variables into JSON design tokens
Dustin converted moz-box-* variables into JSON design tokens Bug 2029180 – Convert moz-box-* variables into JSON design tokens
Dustin converted moz-promo variables to JSON design tokens Bug 2029190 – Convert moz-promo variables into JSON design tokens
Dustin converted moz-reorderable-list variables to JSON design tokens Bug 2029191 – Convert moz-reorderable-list variables into JSON design tokens
Dustin converted moz-visual-picker variables to JSON design tokens Bug 2029193 – Convert moz-visual-picker-item variables into JSON design tokens
Dustin updated browser-shared.css so it passes use-design-tokens Bug 2022985 – Update browser-shared.css so it passes use-design-tokens
Dustin updated popup.css so it passes use-design-tokens Bug 2022979 – Update popup.css so it passes use-design-tokens
Jon added opacity tokens and added opacity to use-design-tokens stylelint rule Bug 1955325 – Create opacity tokens
Jon converted toolbar design tokens to JSON Bug 2017970 – Convert toolbar design tokens to json
Anna fixed moz-select with panel-list drop-down size inconsistency Bug 2032365 – Applications Action drop-down menus sometimes have a different size when opened
Anna fixed issue with the disabled state of moz-radio component Bug 2027123 – moz-radio disabled state cannot be changed while the moz-radio-group is disabled
Anna updated moz-button and moz-box-button components to prevent label corruption when accesskeys are present and the label changes. Bug 2022326 – moz-button with accesskey label becomes corrupted when l10nId updates dynamically

UX Fundamentals

The error pages shown when a server sends back an invalid response header or an unsupported content encoding now display accurate, context-specific messages. The invalid response header page also gained a helpful list of next steps. – 2027209
In progress: The error page illustrations are being replaced with new artwork, and the system now supports per-illustration size configuration, giving each image the ability to define its own appropriate dimensions. – 2031837

Settings Redesign

Tim converted settings related to Accessibility page to config-based pane Bug 1968116 – Convert settings related to Accessibility page to config-based settings
Benjamin converted Privacy & Security page to the config-based pane Bug 1968112 – Convert settings related to Privacy & Security page to config-based settings
Finn integrated Firefox Labs page into setting-pane config Bug 2021047 – Integrate Firefox Labs page into setting-pane config
Anna converted Firefox Updates section to config-based prefs Bug 1990961 – Convert Firefox Updates section to config-based prefs
Mark Kennedy added moz-promo, that is welcoming users to the redesigned settings Bug 2015093 – Add a moz-promo to welcome users to the redesign
Anna added possibility to search for actions in the redesigned “Applications” section Bug 2020370 – It’s no longer possible to search for actions in the new “Applications” section
Anna fixed the Settings navbar layout breakage

Hacks.Mozilla.Org: PACT: Anonymous Credentials for the Web

Sat, 25 Jul 2026 08:37:27 +0200

This is the technical companion to our update on Distilled, “Keeping the web open and private in the bot era.” Here we take a deeper look at the problem space, the design we’re proposing, and the problems still left to solve.

Bots (and privacy-preserving browsers) not welcome

Browse a news site in a private window. Shop at a major retailer with a VPN. Visit a video streaming platform with anti-fingerprinting defenses tuned up. You’ll see the same responses: registration walls, block pages, and endless CAPTCHAs. The message is clear: if we think you might be a bot, you’re not welcome.

Websites have valid reasons for wanting to block bots. Bots enable volumetric abuse, abuse that wouldn’t otherwise be feasible if they had to be carried out by humans. For example: SEO comment spam, credential stuffing and DDoSing. Consequently many sites employ dedicated anti-abuse tooling which aims to keep the bots out whilst minimizing friction for human visitors.

Unfortunately, that tooling is increasingly failing at both tasks. Browser privacy protections are dismantling the passive signals that anti-abuse systems depended on to identify and distinguish visitors. Meanwhile advances in generative AI have rendered CAPTCHAs ineffective: bots now solve them faster and more reliably than humans.

Many sites are switching to more invasive mechanisms and now ask visitors to disclose identifying information, e.g. an email address, a federated login or disabling their VPN. This means greater friction for users, since providing these details on a first visit takes time. It also compromises their privacy, since these details enable the same kinds of cross-site tracking that browser privacy protections were intended to mitigate.

This leaves users with a dilemma. The more effectively they protect their privacy, the harder it is for websites to distinguish them from bots and the worse the treatment they receive. Website operators are also suffering. The additional friction they inflict upon well-behaved visitors harms their site, but many are willing to pay the costs if it mitigates volumetric abuse.

Browser-based AI agents make this tension more acute. Sites may want to allow agents which are acting on behalf of individual users while blocking agents engaged in volumetric abuse. However, with no effective mechanisms to distinguish the two, websites are opting to block both. That hurts users, who should be free to choose the user agent they use to access the web; it hurts new browsers and agents, which struggle to interoperate; and it hurts sites, which lose legitimate visitors.

The consequence is that the web gets worse for everyone. Users get more friction or less privacy or both. Website operators see more volumetric abuse and the friction they add drives away users who would otherwise want to consume their content or services. New user agents struggle to access the same content as conventional browsers.

The Costs of Convenient Solutions

Some large ecosystem players have put forward solutions that leverage their control of the dominant operating systems and their deep integration with consumer hardware. These rely on device attestation: identifiers and privileged code baked into devices at the hardware level, which let manufacturers prove what software is running on a user’s device. Exposing this functionality to the web means attesting to sites that the user is running approved software with trusted hardware and therefore isn’t a bot. There have been two substantive proposals.

Google’s Web Environment Integrity, abandoned in 2023, was the blunt version. It attested to the user agent itself, as well as the operating system and device in use. Users would have lost control in two ways: once to the attester, which would decide which operating systems and devices could be blessed, and again to the website, which would decide which software to accept. If sites had adopted allow-lists of approved user agents, building a new browser would have become virtually impossible, and sites could have withdrawn access from any user agent they chose.

Apple’s Private Access Tokens, deployed across their ecosystem in 2022, have more subtle issues. Built on the Privacy Pass protocol standardized at the IETF, they get a lot right: a user receives a renewed, limited batch of one-time tokens that can be presented to websites without linking their visits together. This provides privacy for users and has shown rate limits to be an effective tool for sites – both points we’ll return to later in this post.

However, Private Access Tokens rely on device attestation, requiring that the hardware manufacturer be in overall control of the user’s device. Presenting a PAT tells a website you are locked into Apple’s rules for what counts as acceptable software. Due to PAT’s technical design^[1], there’s no way to open the system to other sources of scarcity without compromising the system’s privacy properties, meaning that if more widely deployed, access to the web would become tied to having bought expensive hardware from a small, hard to change set of vendors.

Both approaches are ultimately hostile to users and to the openness of the web. Both are premised on parts of a user’s device that sit within the manufacturer’s control and beyond the user’s own. Were they widely deployed, the web would become just another walled garden with centralized gatekeepers controlling acceptable hardware, operating systems and software. As convenient as these solutions are for the players who already dominate the ecosystem, we think there’s a better path.

A Better Path Forward

Bots’ harms arise from their ability to operate beyond human scale. For sites to prevent volumetric abuse they don’t actually need to know the user’s identity or receive cryptographic proof that they’re running approved software. If sites knew their visitors were restricted to a rate limit set by a site, that would be enough.

Rate limits only make sense if they’re tied to something scarce; something an attacker can’t cheaply replicate to evade the limit. Without anchoring to a scarce resource, like the trusted hardware used in Private Access Tokens, attackers can generate as many fresh identities as they need to bypass the rate limit.

However, hardware is just one option for scarcity. Anything a user already has that an attacker can’t trivially spin up at scale will work: email addresses and phone numbers are naturally scarce. A paid subscription costs an attacker the same as a real user. Even maintaining an account on a free service requires some non-trivial work.

What if we could use these scarce signals across the web? We could build an open ecosystem with many parties offering scarcity signals, each site choosing which to accept. By opening up who can provide a signal, and letting sites choose which to accept, we can avoid transferring control to device manufacturers and the resulting harms.

As a concrete example of who might be well positioned to provide such a signal, we can consider VPN providers acting as a subscription service. Sites routinely block VPN users indiscriminately, whether through a deliberate policy choice or through an indirect consequence of rate limiting visitors per IP address. But a VPN subscription is a perfect source of scarcity. If the VPN provider could vouch for its users so that sites could rate limit each user individually – then users would be able to browse the web with less friction and without giving up their VPN usage.

The catch is that building a system that can enable this on the open web whilst maintaining user’s privacy is genuinely difficult. It requires that we take information from one site — that this user holds some scarce thing — and expose it to other sites so that they can use that as the basis for their rate limiting. Letting one site verify a signal from another is the sort of information flow that privacy-preserving browsers have spent the last decade locking down to prevent cross-site tracking.

Our goal would be that no more than the minimum information gets through: a single bit communicating whether the user is below the rate limit set by the site. Leaking anything more – like the source of the scarcity that the rate limit is anchored to – would be unacceptable. Enabling a new cross-site information flow might feel like compromising privacy to gain better access, but reality is more nuanced. If a new system moves sites away from demanding that visitors be identifiable (whether through fingerprinting or login forms), it can be a win for both privacy and access.

The Foundations

The good news is that the cryptographic foundations for a privacy preserving approach already exist. The Privacy Pass protocol, originally developed in 2018 to reduce the friction of Cloudflare CAPTCHAs for Tor users, introduced the core primitive: a token that is unlinkable between issuance and redemption. You prove something to an issuer (e.g. by solving a CAPTCHA), receive some tokens, and later present a token to a website. The website can verify the token is legitimate, but can’t link it to the user it was issued to.

Figure 1: In Privacy Pass, a CAPTCHA provider can issue tokens to a client which can then be used to bypass challenges for future site visits. Even if the CAPTCHA provider and sites collude, they can’t use the tokens to identify the user or their browsing history.

Privacy Pass has gone on to be successfully deployed in systems where the issuer and verifier have a prior trust relationship: Apple uses it to authenticate users of Private Cloud Compute and Private Relay without linking their activity to their identity, Chrome uses it for two-hop IP protection, and Kagi uses it to provide private search. These deployments work in part because a small number of parties have agreed in advance on who issues tokens and who accepts them.

Applying this approach to an open system where any site can act as an issuer brings real challenges. Firstly, even though tokens are unlinkable, knowing a user has access to a specific issuer is a privacy leak on its own, because you can infer that the user meets the relevant issuance criteria. If one site can learn that you have a token from another site, that reveals that you have been to that site, which can be a major privacy problem. This compounds if sites can learn the set of issuers you have visited, since it becomes a fingerprint which can be used to identify you.

Generic techniques exist for proving a statement in zero knowledge: we can prove that a client has a token from a set of acceptable issuers without revealing which specific issuer it is. We’ll call this issuer blinding. The generic approach is often slow, but bespoke approaches tailored to the underlying cryptography can improve this considerably.

Another challenge is how sites using rate limits decide who to trust to issue tokens. If an issuer misbehaves then the site’s rate limits become ineffective, enabling volumetric abuse. However, if we need to prevent the site from learning which issuers a user has access to, the site is only going to know that one of its trusted issuers was used, not which one. This makes mistakes or misbehaviour by an issuer difficult to detect, and makes it hard for sites to evaluate new issuers. Solving this challenge is essential for openness. Without adequate information, sites are likely to lean towards conservative issuer selection. That could lead to less choice between Anchors, which in turn could lead to a new form of gatekeeper being created.

To solve this, sites at least need a way to calculate an aggregate score for each issuer they use. This should roughly correspond to how much of the traffic it considers abusive to have come from users using that particular issuer. Mozilla has long invested in systems like Prio which use multiparty computation (MPC) to protect user privacy whilst enabling aggregate measurements of system behaviour.

Privacy Pass also struggles to handle dynamic adjustments to rate limits. Once tokens have been issued, they’re difficult to invalidate without either revoking all active tokens or risking attacks which can compromise the privacy of users. It’s also beneficial if sites can adjust rate limits on a per client basis, for example by increasing rate limits where they become more confident the client is benign and withdrawing access when abuse is detected.

Anonymous Credit Tokens offer a useful building block to solve this problem. Conventional Privacy Pass schemes rely on issuing a bucket of tokens but ACT works differently by enabling the use of a credential with state. For example, an ACT credential can hold an internal counter. When the credential is presented, the site can check the counter is over some threshold and mutate it, increasing or decreasing the counter whenever the site’s perception of the holder has improved or worsened. Critically, the exact value is never leaked to the site, preventing the site from tracking the holder and ensuring successive presentations of the same credential can’t be linked.

Putting it together

So how can we combine these techniques to build a system which can enable privacy-preserving rate limiting on the open web? In May 2026, we participated in a W3C CG Meeting in collaboration with Cloudflare, Chrome and other web stakeholders in which we started sketching out a design we’re calling PACT – Private Access Control Tokens.

Rate limits need a starting point, a source of scarcity to anchor on. We’ll call an entity that provides such a source an Anchor. To a user who meets the Anchor’s criteria, like having a subscription, an account in good standing, or a verified phone number, an Anchor issues a batch of Endorsement tokens, following the Privacy Pass model. In practice, Anchors could be any website which has access to this kind of signal. An Endorsement conveys scarcity to other sites.

That’s enough for a simple system where access is either granted or denied. But as we discussed earlier, we also want the ability to increase access where a visitor behaves benignly and decrease it where they don’t. The state needed to enforce a rate limit can’t live in the Endorsement, because Endorsements cross trust boundaries between unrelated sites. We need a second object that can hold that state, scoped to the party that maintains it.

We’ll call that the party that handles rate limiting for a site a Moderator and the stateful object a Credential. A Credential is specific to a Moderator and, unlike endorsements, we limit each site to nominating a single Moderator. In the common case the site itself plays the Moderator role, so there’s no new entity or trust boundary. A Moderator can also be a third-party service shared across many sites, allowing those sites to cooperatively share a rate limit.

In the terminology of the previous section, the Anchor is the issuer of Endorsements, and the Moderator both verifies Endorsements and issues Credentials. A Moderator manages rate-limit policy: it decides which Anchors it trusts, accepts their Endorsements, and issues a Credential in return.

Figure 2: (1) Clients acquire Endorsements from Anchors in the course of normal browsing to sites they have relationships with. (2) Clients can exchange Endorsements for a stateful Credential from a Moderator. (3) Credentials can be used to access sites which use that Moderator. Credentials can be updated over time.

Directly revealing which Anchor backed an Endorsement would leak a lot of information about the user. The issuer blinding techniques from the previous section solve this: when an Endorsement is redeemed, the Moderator only learns that it came from one of the Anchors it trusts, but not which one.

When a Moderator covers more than one site, we let Credentials be presented across all of them but partition cookies and storage as we would for any other third party site. The unlinkability of Credential presentations keeps this from creating a new cross-site identifier. The benefit is that good behaviour on one site improves access on every site the Moderator covers, and bad behaviour cuts it everywhere. Websites can already build the same capability with a shared account system, so this doesn’t create a new way to lock users out, but it does provide a new way to grant access without requiring users to give up their privacy.

Enabling Moderators that cover many sites carries a centralisation risk, similar to the concentration we see today in anti-abuse providers. The mitigation is that the choice of Moderator stays with each site, and the choice of trusted Anchors stays with each Moderator. This can’t reverse the centralisation pressure the web already faces, but it ensures this system won’t lead to additional lock-in: a new Anchor or a new Moderator can be adopted without coordinating with a dominant vendor.

The system then has three flows. First, the user receives Endorsements from an Anchor in the course of normal interaction, based on the Anchor’s positive view of the user. This is a relatively rare operation for any given user and Anchor. After all, as our source of scarcity, Endorsements should not be too easy to accumulate.

Figure 3: In the course of normal browsing, clients browse to websites they have a relationship with. These sites can act as Anchors by issuing Endorsements to clients.

Second, when the user arrives at a site that works with a Moderator, the browser spends an Endorsement from an Anchor the Moderator trusts and receives a Credential in return. The presentation hides which Anchor was used, and neither the Anchor nor the Moderator can trace the Endorsement back to where it was issued. The Moderator decides what initial balance the Credential starts with. If the user has no Endorsements from suitable Anchors at all, existing mechanisms (CAPTCHAs, account creation, federated login) could be used to bootstrap a Credential the same way, so the system degrades to today’s experience rather than locking the user out.

Figure 4: When the client browses to a site, it can prompt the client for a Credential from the Moderator it uses. If the Client doesn’t have a suitable Credential, but does have a suitable Endorsement, it can exchange it for a Credential with the Moderator. In practice, the Moderator and the Site might be the same server.

Third, as the user browses, the browser presents the Credential and the Moderator updates the internal state of the Credential. The Moderator can reward behaviour that looks benign and penalize suspicious activity, but can’t track the use of the Credential or identify it if it’s used on other sites the Moderator covers. Revocation falls out of the same mechanism: a Moderator can refuse to return an updated Credential.

Figure 5: The Client can present the Credential on sites which use the matching Moderator. Sites can check if the Credential is in good standing. The sites can then adjust the access the Credential has in response to behaviour. E.g. increasing it when they gain confidence in the client or reducing it in response to malicious behaviour.

In practice, all of this would happen transparently to the user through a WebAPI that sites acting as Anchors or Moderators would call from JavaScript. In an ideal ecosystem, users would accumulate Endorsements through normal browsing, just by virtue of the sites they already visit, and the rest of the flow would happen in the background as they move around the web, leaving users with meaningfully less friction.

AI agents acting on behalf of a user slot into the same flow. An agent can carry its user’s Credentials, in which case the user remains accountable for how the agent behaves. Sites would not need to grant any more access than they would to the user themselves. Alternatively, the operator of an agent can run its own Anchor and vouch for its agents the way other Anchors vouch for human users. Sites retain control over which Anchors they accept, so they can choose how to treat agent traffic without needing a separate detection mechanism.

Several mechanisms combine to keep the information about a user that flows out close to a single bit. Cryptographic unlinkability ensures successive Credential presentations cannot be tied to each other or to the original issuance, so a user’s visits cannot be joined into a history. Each site is bound to a single Moderator, so the set of Moderators a user has Credentials with never becomes a cross-site fingerprint. The Anchor-to-Credential exchange happens in an isolated browsing context, so during ordinary browsing the only thing the site or its Moderator ever observes is a Credential presentation: the site only learns if the user has a valid Credential below the rate limit, or nothing. When the Moderator updates a Credential, it adjusts the credentials state without learning what it is.

The additional privacy given to users from Issuer blinding makes participating in the system more challenging for Moderators. Because the Moderator can’t see which Anchor backed a Credential at issuance, it can’t give a Credential from a strong Anchor more access than one from a weak Anchor: doing so would itself leak which Anchor was used. The initial access has to be uniform across the Moderator’s whole pool of Anchors, which in practice means setting it at the strength of the weakest. However, this is only relevant for that initial access, the Moderator can update credentials according to the holder’s behavior, enabling Credential’s to accrue access over time.

Building an open ecosystem also requires that sites can make effective decisions about the Anchors they choose to trust. Multiparty computation systems like Prio enable aggregate scoring without compromising privacy. When users present Credentials, they can provide an encrypted share which identifies the anchor they used and can be privately aggregated to compute the quality of an issuer.

Next Steps

We think the architecture we’ve sketched for PACT has the right shape, but many of the details still need to be worked out and the entire system needs rigorous privacy and security analysis.

We want to do that work in the open. The IETF is the natural venue for the cryptographic protocols underneath, and the W3C for the WebAPI surface that sits on top. We’ll be bringing draft specifications to these bodies as soon as they’re ready, and we welcome collaborators from across the ecosystem: browser vendors, site operators, anti-abuse providers, and the cryptography community.

If successful, we think we can provide a system which will keep the web open and private, while still giving sites the rate-limiting signal they need.

Acknowledgements

The ideas described here are the result of collaboration and conversations with many people, including: Watson Ladd, Thibault Meunier, Michele Orrù, Trevor Perrin, Eric Rescorla, Samuel Schlesinger, Martin Thomson, Eric Trouton, Benjamin Vandersloot & Cathie Yun.

[1] PAT requires that the source of scarcity and an independent issuer be trusted not to collude. If they do, they can track users as they interact with the system. This is not suitable in the context of an open system where any party could play those two roles.

The post PACT: Anonymous Credentials for the Web appeared first on Mozilla Hacks - the Web developer blog.

The Rust Programming Language Blog: The many journeys of learning Rust

Sat, 25 Jul 2026 08:37:24 +0200

This is another post in our series covering what we learned through the Vision Doc process. We previously described the overall approach and what we learned about doing user research, we explored what people love about Rust, dug into what it takes to ship safety-crticial Rust, and described some of the major challenges that people face when using Rust.

In this post we walk through what folks have found on their journey to learn the Rust programming language with ups and downs covered.

As a disclaimer, LLMs (Large Language Models) come up in this post because our interviewees brought them up. We're scoping discussion to their use as a learning tool, covering research and example generation, not broader questions about AI (Artificial Intelligence) in software development.

Many paths to needing Rust

The interviews surfaced several different paths into Rust: curiosity, embedded work, job-market pressure, organizational adoption, and reassignment after a team or company chose Rust. That last path matters because many learners are not evaluating Rust from a blank slate; they are trying to become productive after Rust has already arrived in their work.

"Funny enough, I've advocated for more niche languages than Rust in the past. Rust has pretty much stopped being as much of a niche language as it was, but it's not Java." -- Fractional CTO

Rust learning resources

Likely as expected, the folks that we talked to reach for a range of resources to learn Rust. Some reach for official documentation, such as The Rust Programming Language Book and find that sufficient to build on what the compiler was already showing them.

"I started with the official Rust documentation because there are a lot of great examples of how features like the borrow checker work." -- Software engineer at an Automotive supplier

Others needed more passes and more formats, sometimes reaching for resources the community maintains, such as Rustlings, The Little Book of Rust Macros, and Learn Rust With Entirely Too Many Linked Lists.

"The first time I went through the chapter in [The Rust Programming Language] on borrow checking, I was like, what is this? I read it again, then I watched a YouTube video of someone explaining the chapter." -- Rust freelance consultant

"Rust book, Rustlings, Zero to Production in Rust, Jon Gjengset tutorials. A bunch of books. It's not a one-pass reading. Can't say how many times I've gone through it." -- Software engineer working on video streaming and storage

These resources have brought up an entire generation of Rust programmers. But, to some, there is a perception that these resources have trouble keeping pace with the language.

"We'd like to use [The Rust Programming Language/'the book'], but we've found that it's out of date, unfortunately. We've looked at the GitHub repo and found it's got a lot of unresolved issues and unmerged PRs" -- Principal Software Engineering work on Rust adoption in a regulated industry

Whether or not this is factually true, Rust's growth has nonetheless put more scrutiny on these materials. Companies evaluating adoption and engineers getting reassigned to Rust teams are looking at them with fresh eyes and finding the gaps that affect their own evaluation.

Beginner stumblings and unlearning habits

It's pretty typical for Rust to be the 2nd, 3rd or Nth programming language that someone picks up. They'd end up writing their most familiar language in Rust, whether C++ patterns, Java patterns, or whatever they knew, for months or even years. Eventually they got comfortable enough to start writing idiomatic Rust.

"There's a bit of a drop in productivity compared to C if you're already familiar with it just because you're learning new rules, new syntax." -- Principal Firmware Engineer (mobile robotics)

"In the beginning it was more poking around the code and adding and removing some ampersands and asterisks to try to make sense of mut and not mut and whatever." -- Senior engineer with 20 years of Java experience in cloud and IoT

We also spoke with someone who found that not having much of a programming background seemed to benefit people picking up Rust. Not having worn-in grooves from other languages may play a role here, and it's worth investigating further.

"I had someone who had never programmed much before start working on the internals of [our Rust project]. She was just fine with getting into Rust. It's more of the senior people that struggle as they need to unlearn practices which may work in other languages, but it's not the 'Rust' way." -- Researcher, Automotive OEM R&D Lab

Learning to work with the borrow checker

We heard a lot about learning to work with the borrow checker instead of against it. People get there through different paths, but a few patterns came up repeatedly.

The compiler as teacher

Rust's diagnostics did the teaching on their own, especially around lifetimes.

"If you mess up the lifetimes in a piece of code that you've written by hand, I usually find that Rust's diagnostics are very helpful" -- Researcher working on static analysis of Rust programs

"Whatever's missing, the compiler usually fills in: it tells me 'you need to declare the lifetime of this reference', so I know and can figure it out. That all generally works pretty well." -- Senior Software Engineer

Learning by doing

Others felt like they only really internalized the borrow checker after writing a lot of Rust. It took projects, coding challenges, prototyping and so on until at some point it clicked.

"I actually did not understand the borrow checker until I spent a lot of time writing Rust" -- Founder of a startup built on Rust

"Besides the prototyping work, I also did coding-challenge-type stuff to get familiar with Rust for Advent of Code. [..] It eventually clicked to the point where I wasn't fighting with Rust, it was working for me. I had that experience other people describe: when I managed to get my program to fit with Rust, it worked. I didn't spend time debugging." -- Principal Software Engineer, large SaaS provider

Letting go of "clone guilt"

Some learners arrive with the assumption that good Rust means zero clones, zero copies, lifetimes threaded through everything. They set the bar at optimal before they've learned how to write idiomatic Rust, and it makes the borrow checker feel harder than it needs to be at the outset.

"On one of my first projects, I was like, 'I don't ever want to copy or clone anything,' so I carefully wove through all the lifetimes and got myself into a bit of a bind. Then I saw someone else just cloning the struct I was working with, and it was super cheap. Sometimes you can just clone and it's going to be okay." -- Researcher at a university

The experienced Rust developers we spoke with consistently said the same thing: clone freely while you're learning, then optimize when you understand the problem. Rust's reputation for performance and correctness feeds this. Newcomers assume anything less than optimal is wrong before they've written a first working program, and clone guilt is how that shows up.

We think it could be an interesting area of future study to check into the patterns Rust programmers employ at different levels of experience and under which circumstances. One member of the Rust Vision doc team that's very experienced with Rust noted that there's kind of an "expected shape" they understand as passing the compiler. This knowledge influences how they approach writing code which wouldn't take that shape and they naturally find themselves understanding when to use so-called workarounds, such as passing around indices into arrays or Vecs.

Multi-paradigm, but not the OOP some are used to

The Rust programming language is multi-paradigm, and how that lands depends on what you're coming from. We heard some that came from a functional background were delighted with digging into learning how much Rust inherits from that lineage. Some others noted that they and others on their teams struggled to unlearn the object-oriented style they'd come to use heavily in other languages like C++ and Java.

"Developers coming from C++ tend to think object-oriented. I think that's a difference between C++ and Rust." -- Architect at Automotive OEM

"I had exactly that thing, where I would apply all my years of Java and JS thinking, where I could just create some object, not care about it, return it, have it sloshing around between various functions. Found myself reaching for these patterns and then being told 'no, you cannot do that'." -- Principal Engineer at a SaaS company

Developers coming from functional programming had less to unlearn: strong typing, pattern matching, and an expression-oriented style were already familiar.

"My background has been more functional programming, strong typing. That originated for me as a Lisper: once a Lisper, always a Lisper." -- Principal Software Engineer working on Rust tooling for safety-regulated industries

"The languages I primarily used before Rust were things like OCaml. Way back, I came from C and C++, the classic languages, and then I spent quite a long time doing primarily pure functional stuff. These days I've ended up back in what I like to think of as a pragmatic center ground [with Rust]." -- Fractional CTO

Teaching Rust in academia

We spoke with a university professor that's been teaching Rust generally. In the academic environment, they were able to use proxies for some things such as "traits are like interfaces in Java" because the students had already gone through a set of courses in their first and second years that taught them Java. They introduced concepts slowly throughout the course, choosing to deal with some more complex topics like generics later. The outcome generally was that students had no problem picking up Rust in this setting.

"I couldn't see any big difference on the embedded side. We also teach an embedded class, and we did an experiment. Half of the students' feedback was worse on the Rust class, mostly because they needed to build the project themselves. The C students just got one from [an LLM], absolutely no problem." -- University Professor, on teaching Rust

The C cohort leaned on LLMs for the project in ways the Rust cohort couldn't. We don't yet have a clear answer for why.

What did come through clearly was the Rust cohort's experience with the community. Some students needed to figure out which drivers to use for the embedded project and how to use them. Their professor encouraged them to open issues and ask questions directly on GitHub, and the maintainers responded. Students who had never contributed to open source before were getting answers from the people who wrote the code.

Learning using LLMs

Some experienced folks shared that they saw LLMs as a tool that can help someone come up to speed quickly, either as a research tool or for generating example Rust code to understand concepts.

"I'm optimistic that there's a way to work [LLMs] in that will cut down that learning curve. One of the big things these tools bring is reducing the learning curve in general; these are very good tools to help you navigate a space that you don't know yet." -- Maintainer of large open source Rust crate

"I try [LLMs] out once a month, usually for generating an example or something like this. Just like with Stack Overflow: when you read an example, you should read it carefully and try to understand it. Not copy and paste it, but type it in your own words in code and then check it, because that's where the teeny tiny little mistakes are." -- Founder of startup built on Rust

For some learners, an LLM is just another way to find answers, no different than a search engine.

"So for the most part, picking up Rust - how do I learn? I'll [use web search for] things, I'll ask [an LLM], I'll just poke around and read the code." -- Senior Software Engineer working in a regulated space

One founder went further and claimed that LLMs change who can become a Rust developer. One consulting company founder described hiring high school graduates with no systems programming background and training them as Rust developers, with LLMs filling in the learning gaps that would previously have required years of experience.

"At the beginning, I was worried, but now that we have [LLMs] supporting development, the difficulty of the language doesn't matter. I'm seeing a huge opportunity behind strong runtime languages like Rust. [..] In [Developing Country] we hire 20-25 high school graduates, train them to be Rust programmers, then they enhance our workforce worldwide." -- Founder of a consulting company

We heard this from one organization. This is a claim that the combination of Rust's compiler and LLM tooling can dramatically shorten the path from beginner to working developer. Whether it generalizes depends on questions we can't answer from a single interview: how long these developers stay, what kind of code they can maintain independently, and whether this training/learning model works outside this company's particular structure. If it holds up, the pool of people who can become Rust developers is much larger than the usual hiring profile suggests.

Organizational considerations for Rust learners

We spoke with a number of folks on teams that are using Rust in larger organizations. Teams wanted to know that everyone would end up at roughly the same level of competence, which led a good number to invest in training courses to get there. Some leaders found that staff was able to ramp well enough by reading The Rust Programming Language, going through Rustlings, and then picking up lower risk and priority tickets to work on. Having a sense of community was also important within companies; it helps people know they are not alone when they are asked to work on Rust after, say, a reorganization happens.

"[..] the idea with the class as opposed to 'just read the Rust book on your own' was that this gives everyone kind of the same baseline going in." -- Principal Firmware Engineer (mobile robotics)

"So typically we're going to have people work through Rustlings, work through The Rust Programming Language. We have them then start to pick up lower risk tickets to work on." -- Principal Engineer at a large SaaS provider

"We've got an internal Slack channel for Rust learning where people can drop questions and others will come in and answer them. That helps build up understanding and community." -- Software Engineer at a large corporation

Some organizations found that while the person they'd hire would need to learn Rust, it was still preferable to the alternative of hiring someone for a critical piece of software written in another language.

"They needed to grow and maintain this C++ codebase. They had a C++ wizard, and they tried for about two years to find someone with the same level of expertise. They ended up hiring people that didn't know Rust and ramping them up, creating FFI bindings from the C++ side so they could work in Rust. And you can feel it: the borrow checker is teaching these people the right way to handle their systems." -- Principal Engineer at an Automotive OEM

The community and helping each other aspect seems to grow bonds as organizations mature.

"Our team is [all about] mentorship. I've mentored people coming up to speed on Rust, and people help each other hugely." -- Principal Software Engineer at a large SaaS company

Silent attrition

We identified some cases where people have approached Rust and bounced off of it, for one reason or another. In the below case, someone with a background in a language with fewer guardrails found themselves frustrated enough with Rust to walk away.

"All of that means that that embedded ecosystem is very frustrating to somebody who comes from C and is like, why can't I just get a pointer to this peripheral and then write into the registers. What are you doing to me? [..] My friend never got over that. He looked at it and said, I'm not going to deal with this and walked away." -– A second University Professor

There may be language features that for a particular domain are not seen as comfortable or usable yet, such as async Rust usage in a safety domain. We'd like to map which language features feel off-limits in which domains; async in safety-critical work probably isn't the only case.

"We're not fully sure how async [Rust] will work out in the long run in our domain. [..] People don't feel comfortable yet since C++14 doesn't provide such concepts. [..] It's the chicken-and-egg problem again: we probably need to gain some experience to see whether we can actually benefit from these new concepts in the automotive and safety domains." -- Team Lead at Automotive Supplier (ASIL D target)

We heard in at least one case, that while the language was challenging and there was a near bounce, the tooling helped keep them coming back and trying.

"Well, I think my early impressions of Rust - one is I find C++ so intimidating, and I think a big part of why I was able to succeed at [..] learning Rust is the tooling. I mean, all this makes sense [..] but it's like, for me, getting started with Rust, the language was challenging, but the tooling was incredibly easy." -- Founder of another startup built on Rust

While it might be considered more of a community concern, if there are interactions online and in spaces that point to learners having so-called "skill issues" this feeds into the narrative that Rust must be hard to learn. We may be unintentionally turning away Rust Project contributors and maintainers due to the vibes being put out when new learners show up in certain spaces.

"People are very helpful, but generally the attitude is: if your program is very complicated, it's mostly a skill issue. There's not that much empathy when people get stuck learning, and a lot of people are just pushed away by it. There's probably a huge number of people who silently stop wanting to write Rust, because at some point it gets complicated and the feedback they get is 'you just need to be a better programmer, obviously'." -- Software Engineer at a SaaS Provider

Feedback on near-bounces from survey

We found a few interesting perspectives collected in the Rust Vision doc survey which we administered with examples of bouncing and coming back:

"I started before 1.0, got stuck very soon when trying to translate patterns from C++ to Rust (due to borrow checking). I tried again after 1.0 and it stuck. [..]" -- Survey Respondent A

Survey Respondent A went on to share in a more detailed response about a perceived weakness in Rust learning materials related to lifetimes and the borrow checker are explained. There was an observation that it's fairly easy to run into more complex situations with lifetimes and the borrow checker. They felt that the current state of this sort of material and tutorials is fairly superficial and can leave learners stuck when they run into those more complex situations.

One respondent that bounced once and came back shared challenges around usage of async. In concert with Rust's memory-safety and the borrow checker, they found some of the nitty-gritty details of async were difficult to learn. While we're aware of the Rust Project's continuous efforts to improve Rust's async story, this is another data point of a user that faced challenges.

Another survey respondent shared how they had multiple times bounced in trying to learn Rust. They returned after a year or so and found Rustlings to be highly motivating. We note that having multiple pathways for folks to learn Rust opens up more possibilities for those that nearly bounced, just like this person.

Need more focused work on silent attritrion

The thing that stood out most to us was the lack of real, first-hand knowledge of having bounced when learning Rust. While this is an obvious effect of soliciting answers to our survey and opportunities to interview through Rust channels and our networks, this cohort is good future candidate where interviews could start.

Conclusions

Across these conversations, the experience of learning Rust depended heavily on context. Why someone was learning and what support they had mattered as much as the borrow checker. The same kinds of examples kept coming up: a training course that got a team to a shared baseline, a maintainer answering a student's first GitHub issue, and a colleague whose code showed that cloning was okay.

That context is largely something the community has a hand in. With that in mind, here is what we take away from what we heard, and what we still don't know.

What seems worth trying

Learning materials aimed at unlearning. Syntax barely came up when people described their struggles. People struggled with unlearning habits from previous languages, whether OOP structuring from C++ and Java or the instinct to grab a raw pointer to a peripheral. Most of our learning materials teach Rust from first principles, and that works. What we didn't come across is much written for, say, the engineer with ten years of Java who lands on a Rust team after a reorg: material that names the patterns they'll reach for that won't transfer, and shows what to do instead. The professor we spoke with did a version of this in the classroom, leaning on "traits are like interfaces in Java" and saving generics for later in the course, and the students did fine. Something similar could work outside the classroom too.

Put the "clone freely while you're learning" advice somewhere official. Every experienced developer we spoke with gave the same advice, but learners seem to mostly pick it up by accident, like the researcher who happened to see someone else cloning the struct they had been carefully threading lifetimes through. Saying it early in official materials would take some of the steepness out of the curve. The broader version belongs there too: idiomatic Rust doesn't have to mean optimal Rust, especially on a first project.

Diagnostics are already a primary learning resource: several people told us the compiler taught them lifetimes before any documentation did. Diagnostics reach learners right at the moment they're stuck. When writing new ones, it seems worth keeping the confused newcomer in mind alongside the expert, because for a lot of people this is where the learning happens.

Is "the book" actually out of date? Whether or not The Rust Programming Language or other materials are actually behind, a team evaluating Rust looked at its repository, saw unresolved issues and unmerged PRs, and moved on. As more companies evaluate adoption, more people will look at these materials with the same fresh eyes. Visible issue triage and some communication about what's current and what's planned would address the perception, separately from whatever content work may or may not be needed.

How stuck learners get treated is shaping who stays. We heard about students getting answers on GitHub from the maintainers who wrote the code, and we heard about learners being told their struggles were a skill issue. The first group came away with a lasting good impression of Rust. Some of the second group walked away entirely, and because they leave quietly, it's easy to underestimate how many of them there are. The welcoming side of the community came up unprompted as a reason people stayed, so we know it makes a difference when we get this right.

Every organization we spoke with described essentially the same ramp-up for bringing a team to Rust. Teams that brought groups of developers to Rust described roughly the same approach: get everyone to a shared baseline with a training course or with The Rust Programming Language and Rustlings, start people on lower-risk tickets, and give them somewhere internal to ask questions. Several organizations also found that hiring developers without Rust experience and ramping them up worked out better than continuing to search for rare expertise in another language. None of this is complicated, and teams weighing adoption don't need to invent a training program from scratch.

What we still don't know

The biggest gap is the people we didn't reach. Nearly everyone we spoke with stuck with Rust long enough to be reachable through Rust channels, so the stories of bouncing off came to us second-hand: a friend who walked away from embedded Rust, colleagues who quietly stopped after the responses they got. As we wrote in our first post, finding people who decided against Rust takes targeted outreach. If the proposed User Research team comes together, talking with learners who bounced would make a good early project, and learning is probably the area where that research would teach us the most.

We also don't know what to make of LLMs as a learning tool yet. They came up as a search engine, as an example generator, and in one organization's case as something that makes training high school graduates into working Rust developers possible. We saw a classroom where the C cohort leaned on LLMs in ways the Rust cohort couldn't, and we don't have an explanation for it. All of this comes from a handful of conversations, so we treat it as a set of leads to follow up on. Given how quickly the tools are changing, it seems better to study this deliberately than to wait and see what folklore develops.

The folks we spoke with showed that people do get there: with enough passes through the materials and enough code written, it eventually clicks. The opportunities above are mostly about making it work for the people who didn't pick Rust on purpose, and for the ones who would have stuck around if their early experience had gone a little differently.

Matrix 2.0 — How we're making Matrix go voom!

Sat, 25 Jul 2026 08:36:49 +0200

Author: Matrixdotorg - Bewertung: 395x - Views:15995

NOTE: we'll update the FOSDEM 2023 playlist as we gather all the talks. Stay tuned for more Matrix content.

This video was recorded during FOSDEM 2023, and can also be found here: https://fosdem.org/2023/schedule/event/matrix20/

Matrix is an open standard for secure, decentralised communication, which may be familiar from powering the online editions of FOSDEM in 2021 and 2022 (and hybrid-FOSDEM this year!).

In this talk we will explain the fundamental changes which are landing in Matrix 2.0, which speeds up Matrix to be at least as snappy as the fastest proprietary messaging apps - all while handling thousands of rooms spanning millions of users.

During 2022 we've been on a mission to completely rework the slowest bits of Matrix, aiming that nobody can ever complain about Matrix being sluggish again. In practice this means fundamental changes in:

- How Matrix syncs data - "sliding sync", where servers only sync the bare minimum data to clients required to render the UI, providing instant login and instant sync (MSC3575)

- How room joins work over federation - "faster joins", where servers only sync the bare minimum data such that clients can start participating in the room as soon as possible (MSC3902)

- How auth works - switching Matrix to use OIDC natively for all authentication, registration and account management (MSC3861)

- How VoIP works - switching Matrix to natively support multiparty decentralised E2EE VoIP as the primary calling mechanism (MSC3401 and MSC3898)

The end result is transformational, and by far the biggest change to Matrix since the project began in 2014. So, we're calling it Matrix 2.0, and this talk will give a guided tour of everything that's changed - and show off the new reference matrix-rust-sdk client SDK, which powers the new flagship mobile Matrix client, codenamed Element X.

00:00 Hello!
00:41 Matrix and its ecosystem
06:33 Spec update
08:10 Matrix is taking over the world
09:26 Demo: Element X is blazing fast
13:25 Behind the scenes of the demo
15:02 How sliding sync works
21:20 One matrix-rust-sdk to rule them all
23:45 Faster remote room joins
25:12 MatrixRTC, Element Call, Matryoshka Widgets
26:37 Demo: Element Call!
28:34 Demo: raiding Element Call with waterfall — Selective Forwarding Units for Matrix
31:02 OpenID Connect: authentication done right
32:04 The future: Digital Markets Act, MIMI, MLS, P2P Matrix
36:32 Demo: P2P Matrix
40:31 Demo: ThirdRoom — Matrix beyond chat and voice
47:16 What's next?

Support Matrix!
🪙 Donorbox for individuals - https://donorbox.org/keep-matrix-exciting
💶 Matrix Foundation (paid) membership for orgs - https://forms.gle/Yy345QkB5pifJJNy6

(This Week in) Matrix
🐘 https://mastodon.matrix.org/@matrix
🐦️ https://twitter.com/matrixdotorg
[m] https://matrix.to/#/#thisweekinmatrix:matrix.org

Understanding different types of OLED TVs and displays

Sat, 25 Jul 2026 08:36:42 +0200

Author: Techquickie - Bewertung: 5027x - Views:93409

Play War Thunder for FREE on PC, PlayStation, Xbox, and mobile using the links below! New to the game, or returning after six months? You'll get a massive bonus pack on PC and consoles packed with vehicles, boosters, and more. Claim your bonus and start playing today!

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Remember when OLED was simple? You paid a premium, you got perfect blacks, and you called it a day. Fast forward to 2026, and we’re drowning in an alphabet soup of WOLED, QD-OLED, and "Penta-Tandem" stacks. Today we’re breaking it all down and we’ve got the answers.
Special thanks to Ron Mertens, CEO of Metalgrass and founder of OLED-info, for joining us and sharing over 20 years of expertise in the display industry.

Leave a reply with your requests for future episodes.

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Ist die GTX 1080 Ti die beste Grafikkarte aller Zeiten?

Sat, 25 Jul 2026 08:35:40 +0200

Author: PC-WELT - Bewertung: 484x - Views:13584

Beste Grafikkarte aller Zeiten? 👑

Die NVIDIA GeForce GTX 1080 Ti kam 2017 für 819 € – mit 11 GB VRAM, 3.584 CUDA-Kernen und mehr Leistung als die teurere Titan X. Fast 10 Jahre später läuft die Pascal-Legende noch erstaunlich gut in 1080p: kein DLSS, kein Raytracing – einfach rohe GPU-Power.

Welche Grafikkarte verdient für dich Legendenstatus? 👇
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#GTX1080Ti #NVIDIA #Grafikkarte #PCGaming #gaminghardware

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"Ritt auf Rasierklinge": Sicherheitsforscher warnen Windows-10-Nutzer

Sat, 25 Jul 2026 08:21:38 +0200

Das Support-Ende von Windows 10 war im Oktober 2025, doch fast die Hälfte der deutschen Privat-PCs nutzt das System weiter. Trotz der ESU-Gnadenfrist warnen Sicherheitsforscher vor steigenden Risiken und raten dringend zur baldigen Migration. (Weiter lesen)

Studie belegt: Solaranlagen fallen bei Extremhitze fast komplett aus

Sat, 25 Jul 2026 07:21:36 +0200

Der Beitrag Studie belegt: Solaranlagen fallen bei Extremhitze fast komplett aus erschien zuerst beim Online-Magazin BASIC thinking. Über unseren Newsletter UPDATE startest du jeden Morgen bestens informiert in den Tag.

Der Klimawandel gefährdet zunehmend die europäische Stromversorgung, da extreme Sommertemperaturen nachhaltige Infrastruktur belasten. Eine neue wissenschaftliche Untersuchung zeigt, dass Solaranlagen bei Hitze massive Leistungseinbrüche von bis zu 90 Prozent erleiden können. Ein Forscherteam der Universität Évora in Portugal hat die Auswirkungen extremer Hitzephasen auf Solaranlagen auf der Iberischen Halbinsel in den Jahren 2024 und 2025 […]

Der Beitrag Studie belegt: Solaranlagen fallen bei Extremhitze fast komplett aus erschien zuerst auf BASIC thinking. Folge uns auch auf Google News und Flipboard oder abonniere unseren Newsletter UPDATE.

IT leaders: Leading-edge AI insights await at TechCrunch Disrupt

Sat, 25 Jul 2026 05:51:14 +0200

For CIOs, learning from the startup ecosystem has never been more critical.

As pressure mounts to transform business operations with AI and agentic systems, IT leaders should be looking to those on the AI vanguard for insights into the strategic and technical decisions necessary to launch, grow, and thrive in today’s AI-disrupted business environment.

So why not immerse yourself in Silicon Valley’s most famous firehose of hyper-accelerated fail-fast and dream-big culture by registering for TechCrunch Disrupt 2026?

Three packed days of 200-plus sessions across six stages will spark new ideas for reshaping your AI strategy, provide fresh perspectives on the architectural, workflow, and resource decisions involved in moving AI from pilots to scale, and give you a sneak peek of business disruptions to come.

Get 10% off your TechCrunch Disrupt pass with the exclusive code CIO10.

This year’s TechCrunch Disrupt, held Oct. 13-15 at San Francisco’s Moscone West, will feature big-picture conversations on what’s next in AI; discussions on how AI agents are rewriting SaaS, enterprise workflows, software pricing, and security; and demonstrations of AI’s future across robotics, manufacturing, defense, and industrial operations; and more.

Over 10,000 attendees will hear from 250-plus startup founders, technology executives, and enterprise IT leaders about how the future of programming is being rewritten, what enterprise AI security requires, how startups are orchestrating workloads across models while managing cost and reliability at scale, why creating a safety culture is essential for AI deployment, and how startups are deciding what work humans should own versus what should be delegated to AI as they work to build hybrid teams without losing speed, accountability, or culture.

And of course, the rising tide of enterprise-focused startups will be there seeking to bring agentic systems to your business workflows, as well as vendors familiar to your enterprise IT portfolios, such as AWS, Google, and Databricks, and enterprise IT colleagues creating mutually beneficial partnerships with the startup community, such as American Express.

That’s not to mention TechCrunch Disrupt’s signature Startup Battlefield, in which 200 standout companies showcase their innovations to compete for a $100K equity-free prize. The battlefield will give CIOs a rapid-fire, broad view of what’s possible — and a possible early look at the next big enterprise player. After all, Dropbox, Trello, and Cloudflare, among others, roamed that same battlefield before the world knew their names.

And with M&A now an early-stage startup strategy for many from day one, TechCrunch Disrupt’s exhibition floor provides IT leaders not just an opportunity to discuss the nuts and bolts of innovation architecture or how an upstart product can enhance your workflows, but a chance to find your next innovation partner, or more.

Leading-edge startups are figuring out how to make AI work at scale. Shouldn’t you be?

Don’t miss your chance to experience TechCrunch Disrupt 2026. Book your pass today and use the exclusive code CIO10 to save 10% before prices increase.

5 Minuten Vorbereitung, Hunderte Euro gespart: Warum eine Reise-eSIM auf jede Urlaubs-Checkliste gehört

Sat, 25 Jul 2026 05:14:33 +0200

Der Sommerurlaub steht vor der Tür, die Koffer sind fast gepackt – doch eine Sorge bleibt: Wie bleibt ihr im Ausland online, ohne von einer horrenden Handy-Rechnung oder langen Suche nach einer SIM-Karte überrascht zu werden? Es gibt eine smarte und überraschend einfache Lösung, die euch jede Menge Geld und Ärger spart: die Reise-eSIM.

5 endpoint blind spots your EDR/XDR was never built to see

Sat, 25 Jul 2026 00:18:11 +0200

In August 2025, 126 malicious packages landed in the npm registry. Even after the community caught the initial wave, 80 of these hidden backdoors remained actively listed.

#1. Gain real-time visibility into shadow AI & extensions

#2. Distinguish between human and autonomous agent behavior

#3. Establish guardrails for automated package updates on endpoints

#4. Enforce principle of least privilege for AI agents

#5. Maintain continuous endpoint posture management

Summary

Ready to secure the future of your software stack? See how Koi Agentic Endpoint Security delivers complete visibility, risk scoring, and real-time prevention across every endpoint in your enterprise.

Where do you install software from? Standard repos, sources, Flatpak, AppImage, etc

Sat, 25 Jul 2026 00:11:41 +0200

Do you install only flatpaks? Or only native RPM/DEB/etc? Maybe you're religios about it.

Or maybe you don't care and just use what's convenient, which happens to be... what channel usually for you?

Curious to know as a developer where this ship is moving recently

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T-Mobile 5G Home Internet Gets You Online Fast with No Technicians or Contracts

Fri, 24 Jul 2026 22:51:34 +0200

With T-Mobile 5G Home Internet, you can be online in 15 minutes, with simple pricing that won’t change for 5 years.

Rakuten TV: Neue FAST-Channels mit Sony Pictures gestartet

Fri, 24 Jul 2026 21:34:52 +0200

Rakuten TV baut sein kostenloses, werbefinanziertes Streaming-Angebot (FAST) massiv aus. Durch einen frischen Deal mit Sony Pictures Entertainment (SPE) bringt die Plattform ab sofort europaweit 20...

Zum Beitrag: Rakuten TV: Neue FAST-Channels mit Sony Pictures gestartet

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Anthropic's New Opus 5 Model Rivals Fable 5 For Half the Price

Fri, 24 Jul 2026 21:08:38 +0200

Anthropic has released Opus 5, a new Claude model that it says comes close to its higher-end Fable 5 model at half the price while improving on Opus 4.8 in knowledge work, coding, and scientific research tasks. "At the same time, Anthropic says it has managed to make the model more resistant to being tricked," notes Engadget. Additionally, the company says Opus 5 "exhibits the lowest rates of deceptive behavior." From the report: One important distinction between Opus 5 and Mythos, which is currently only available to a limited number of vetted organizations through Anthropic's Project Glasswing initiative, is that the company has specifically avoided training the new model on cyber-related tasks. Due to more its powerful capabilities, Opus 5 is broadly better at those tasks than its predecessor, making it more useful for finding cybersecurity vulnerabilities, but the company says Opus 5 is "substantially behind" its flagship model at exploiting those vulnerabilities. [...] Anthropic says "Claude Opus 5's safeguards are designed to allow beneficial uses of the model in both cybersecurity and biology." The company has strengthened some of the model's cyber-related guardrails, but notes it did so along a "narrow range" of specific tasks. "Based on our testing, we expect the classifiers to intervene around 85 percent less often than they do for Fable 5," Anthropic said. With Opus 5, Anthropic also isn't including it in its recently announced 30-day data retention policy, which the company introduced alongside Fable and Mythos 5. As for pricing. Anthropic says API costs for Opus 5 will remain at $5 per one million input tokens and $25 per one million output tokens. Anthropic has also added an "effort" menu for Opus that users can tweak to tell the model whether they want it to be more thorough or fast and efficient to conserve tokens.

Read more of this story at Slashdot.

Anthropic launches Claude Opus 5, a cheaper AI model for coding, agents and enterprise workflows

Fri, 24 Jul 2026 20:10:11 +0200

Anthropic released Claude Opus 5 on Friday, a model the company says delivers nearly all the intelligence of its top-of-the-line Claude Fable 5 at half the cost — a launch that signals how the AI race is shifting from raw capability to the economics of daily use.

The model, available immediately on all of Anthropic's platforms, is priced at $5 per million input tokens and $25 per million output tokens, unchanged from its predecessor, Opus 4.8. It becomes the new default model on Claude Max, Anthropic's premium consumer tier, and the strongest model available on Claude Pro.

The positioning is deliberate. Anthropic is not claiming Opus 5 is its smartest model — that distinction still belongs to Fable 5, and rival systems retain an edge in certain domains. Instead, the company is making a subtler argument that may matter more to enterprise buyers: that the most economically important AI work happens in a middle band of difficulty, where near-frontier intelligence delivered efficiently and cheaply beats frontier intelligence delivered expensively.

"Opus 5 as your daily driver, the model you hand complex work to and review when it's done," an Anthropic spokesperson said in an interview with VentureBeat, describing how the company's lineup now stratifies. "Fable 5 for your most ambitious work, the days-long autonomous projects nothing could take on before... Sonnet 5 for work you run at scale, where speed and cost per call decide what ships. Haiku 4.5 for subagents and instant answers."

How Claude Opus 5 benchmark results stack up against Fable 5 and rival AI models

On paper, the results are striking. Anthropic says Opus 5 sets new state-of-the-art marks on coding and knowledge-work evaluations including Frontier-Bench and GDPval-AA. On Frontier-Bench v0.1, an agentic terminal coding benchmark, Opus 5 scores 43.3 percent — more than double Opus 4.8's 18.7 percent and well ahead of Fable 5's 33.7 percent — at a lower cost per task, according to the company. On ARC-AGI 3, an evaluation of novel problem-solving, Anthropic reports Opus 5 scored three times as high as the next best model. On OSWorld 2.0, a computer-use benchmark, the company says the model surpasses Fable 5's best result at just over a third of the cost.

The numbers come with honest caveats that are themselves notable in an industry prone to superlatives. Anthropic acknowledges Opus 5 remains behind Mythos 5, a competing model, on cybersecurity tasks and biology research, and an OpenAI-family model still leads on one agentic coding benchmark.

The more revealing caveat came from Anthropic itself, when asked where Opus 5 still falls short of Fable 5. The spokesperson's answer amounted to a candid admission about what benchmarks do and don't capture.

"The evals where Opus 5 wins are bounded tasks with a specific outcome, which is where it's strongest. What those evals don't measure is duration," the spokesperson told VentureBeat. "One way to put it: Opus 5 is the best tool for the jobs benchmarks can see, and Fable 5 is what you reach for when the job outruns the benchmark."

Fable 5, by contrast, "is for the longest, most autonomous jobs, where the model has to stay coherent across many connected steps over hours or days with dense source material," the spokesperson said, advising customers to "run both on a representative workload, one bounded task and one long-horizon job." That framing — bounded tasks versus long-horizon autonomy — may become the defining axis of model differentiation in 2026, as benchmarks saturate and the hardest remaining problems involve sustained, multi-day agentic work rather than discrete puzzles.

Why token efficiency is becoming the real battleground for enterprise AI spending

Threaded through the launch is a theme Anthropic clearly wants buyers to absorb: Opus 5 doesn't just score well, it scores well per dollar. The model ships with an adjustable "effort" setting that lets customers trade intelligence for speed and token savings, and Anthropic's charts emphasize performance at a given cost rather than peak performance alone.

Early customers echoed the point with unusual specificity. Harvey, the legal AI company, said Opus 5 achieved similar performance to Opus 4.8's maximum-reasoning mode "while generating 26% fewer tokens on average," according to Niko Grupen, its head of applied research. Richard Pham of Fundamental Research Lab said that on hard financial-modeling tasks, the model averaged nine percentage points higher accuracy "while using roughly one-third fewer turns and tool calls and 60% less time."

Wade Foster, chief executive of Zapier, said Opus 5 topped his company's AutomationBench leaderboard "without spending more tokens than prior Claude models," running a full churn-prevention workflow from start to finish. "Previous models didn't pass; Opus 5 hit 100%," he said. Scott Wu, chief executive of Cognition, the company behind the Devin coding agent, said that on FrontierCode 1.1, "Claude Opus 5 approaches Fable-level performance at half the cost," with particular strength in debugging and root-cause analysis.

The efficiency emphasis reflects commercial reality. Enterprise AI spending is no longer experimental, and inference costs — the price of actually running these models at scale — have become a board-level line item.

Anthropic's business skews heavily toward API and enterprise usage; according to a February 2026 analysis by Contrary Research, Claude held roughly 40 percent of the enterprise large language model market by usage as of late 2025, and Claude Code alone had reached about $1 billion in annualized revenue. For a company whose customers pay by the token, a model that does more with fewer tokens is not a nice-to-have. It is the product.

Self-verifying AI agents and what they mean for the hidden costs of automation

Beyond the numbers, Anthropic is selling a behavioral story: that Opus 5 verifies its work and iterates until it succeeds. The company offered several examples from testing that read like small parables of machine stubbornness.

In one Frontier-Bench task, the model was asked to reconstruct a machine part as a 3D CAD model from a drawing it was intentionally given no way to view. Rather than fail, Anthropic says, Opus 5 wrote its own computer vision pipeline to extract the geometry from raw pixels — and did so repeatedly, while no competing model solved the task in five attempts. In another case, given a real bug in a popular open-source package manager, the model found the root cause and fixed an edge case the community's own patch had missed; a competing model patched only the symptom and declared victory. An engineer at a trading firm, the company says, used Opus 5 to build a market data feed for a new exchange in a single session and, finding no live feed to validate against, watched the model build its own test harness to check its parsing code.

Customers described similar behavior in the wild. Cristian Rivera, a staff software engineer at Stripe, said he gave the model "a chief-of-staff role over my dev environments" for a weekend: "it built its own monitor, drove each box, and pulled me in only for the judgment calls."

This is the capability enterprises actually care about, and it is worth dwelling on why. The gap between a model that produces plausible output and one that verifies its output is the gap between a demo and a deployable system. Most of the hidden cost of enterprise AI today is human review — engineers checking the machine's work. A model that reliably checks its own work compresses that cost, which is precisely why customers keep citing fewer turns, fewer passes, and less time rather than higher raw scores.

Inside Anthropic's safety strategy: capability gaps, classifiers, and model fallbacks

The launch also showcases Anthropic's increasingly intricate approach to safety — one that now involves deliberately not teaching its models certain skills. The company says its automated behavioral audit found Opus 5 to be its most aligned model to date, scoring 2.3 on overall misaligned behavior, lower than Opus 4.8, Sonnet 5, or Fable 5, with the lowest rates of deceptive behavior and the least susceptibility to being tricked into misuse.

On the capability side, Anthropic says it intentionally avoided training Opus 5 on cyber tasks, as it did with Opus 4.8. The model improved on them anyway — a side effect of general capability gains — and now nearly matches Mythos 5 at finding software vulnerabilities. But it remains far behind at exploiting them: on Anthropic's OSS-Fuzz evaluation, Opus 5 identified vulnerabilities at a 79.4 percent rate, close to Mythos 5's 80 percent, but succeeded at developing exploits in only 4 challenges versus Mythos 5's 13. That asymmetry — strong at defense-relevant discovery, weak at offense-relevant exploitation — appears to be by design, and the safeguards follow the same logic. Anthropic expects Opus 5's cyber classifiers to intervene about 85 percent less often than Fable 5's.

When a classifier does trigger, requests in Claude.ai, Claude Code, and Claude Cowork fall back to Opus 4.8 by default — raising an obvious question: if a request is too risky for one model, why is it acceptable for another? "The model it falls back to has lower capability levels making the risk of harmful use lower as well," the spokesperson said, adding that "there is a message that lets the user know when this occurs and is visible in the chat."

The logic is defensible, but it reveals how AI safety actually works in 2026: risk is not a property of the question alone, but of the question multiplied by the capability of the system answering it. On biology, the calculus runs the other way. Opus 5 is now Anthropic's most capable generally available model for scientific research — scoring 10.2 percentage points higher than Opus 4.8 on the company's internal chemistry benchmark — though the spokesperson acknowledged that "Mythos 5 remains the stronger model for long-horizon, open-ended work like autonomous drug design campaigns."

The business stakes behind the launch: a $380 billion valuation and massive compute bets

The launch lands at a moment of extraordinary commercial momentum — and extraordinary obligations — for Anthropic. Reuters reported in February that the company was valued at roughly $380 billion in its latest funding round, following a period in which, per Contrary Research's analysis, its annualized revenue climbed from about $1 billion at the end of 2024 to a projected $9 billion by the end of 2025, with internal targets reportedly reaching $20 to $26 billion for 2026. Those targets are underwritten by enormous infrastructure commitments, including a reported $30 billion Azure compute deal alongside arrangements with Google Cloud and Nvidia — spending that only pencils out if enterprises keep expanding usage.

That is the context in which Opus 5's pricing strategy makes sense. Holding the price at Opus 4.8 levels while roughly doubling performance on key agentic benchmarks is effectively a steep price cut per unit of capability, designed to widen the funnel of workloads that are economical to automate. Every task that was marginal at Opus 4.8's cost-per-success becomes viable at Opus 5's — and every viable task is recurring token revenue.

The regulatory backdrop has grown more complex as well. A U.S. judge gave final approval this week to Anthropic's $1.5 billion copyright settlement with book authors, Reuters reported, closing a chapter of litigation over the company's early training data. And in June, Reuters, citing Axios, reported that the U.S. government had moved to block foreign access to Anthropic's most advanced models — a reminder that frontier AI is now entangled with export policy in ways that shape which customers can buy what.

Also shipping Friday: a Fast mode running at roughly 2.5 times default speed at twice the base price, automatic fallback routing on the API, and mid-conversation tool changes that no longer invalidate the prompt cache — a small feature that agent developers may appreciate more than any benchmark. Consistent with prior Opus models, Opus 5 carries no data retention requirements for general access, a point the spokesperson flagged unprompted for customers with "a hard zero data retention requirement." Developers can access the model as claude-opus-5 on the Claude API starting today.

Two questions will determine whether the bet pays off: whether Opus 5's efficiency claims survive contact with production workloads at scale, and whether enterprises embrace a world where safety classifiers, not users, sometimes decide which model answers. But the deeper message of Friday's launch is that the AI industry's center of gravity has moved. For three years, the labs competed on what their best model could do on its best day. With Opus 5, Anthropic is competing on something less glamorous and far more lucrative: what a very good model can do every day, for half the price. In a market where the frontier keeps moving, Anthropic is wagering that the real fortune lies just behind it.

IT leaders: Leading-edge AI insights await at TechCrunch Disrupt

Fri, 24 Jul 2026 19:56:29 +0200

For CIOs, learning from the startup ecosystem has never been more critical.

So why not immerse yourself in Silicon Valley’s most famous firehose of hyper-accelerated fail-fast and dream-big culture by registering for TechCrunch Disrupt 2026?

Get 10% off your TechCrunch Disrupt pass with the exclusive code CIO10.

Leading-edge startups are figuring out how to make AI work at scale. Shouldn’t you be?

Don’t miss your chance to experience TechCrunch Disrupt 2026. Book your pass today and use the exclusive code CIO10 to save 10% before prices increase.

Apple’s $250 Million Siri Settlement Approved, iPhone Users Could Get Up to $95

Fri, 24 Jul 2026 19:20:12 +0200

Apple’s $250 million settlement over the delayed launch of its new Siri features has received preliminary court approval, bringing eligible iPhone owners closer to a possible payout of up to $95. The settlement website has not launched yet, so customers do not need to submit any forms or take action at this stage. The lawsuit followed Apple’s decision to delay several Siri features that it had promoted alongside Apple Intelligence in June 2024. These features included personal context awareness, on-screen understanding, and deeper controls inside supported apps. Apple used product presentations, its website, and a television advertisement featuring Bella Ramsey to promote the new Siri experience. However, the company delayed the personalised Siri upgrade in March 2025, which led customers to file a class action lawsuit over alleged false advertising. Who qualifies for the Apple Siri settlement? Eligible users must live in the United States and must have purchased one of the following iPhone models between June 10, 2024, and March 29, 2025: iPhone 15 Pro iPhone 15 Pro Max iPhone 16 iPhone 16e iPhone 16 Plus iPhone 16 Pro iPhone 16 Pro Max Customers will likely need to provide purchase details, a serial number, or other device information when submitting a claim. The final requirements will appear on the official settlement website once it goes live. Each approved claim will receive an estimated payment of $25 per eligible device. However, the payout can rise to as much as $95 if fewer people submit valid claims than expected. According to the July 17 court document, eligible customers should receive email notices within about 45 days of preliminary approval. Customers who do not receive an email can still submit a claim if they meet the eligibility requirements. Apple announced its revamped Siri system at WWDC 2026, and the assistant is now available through the iOS 27 developer beta. Apple plans to release iOS 27 publicly in September for the iPhone 15 Pro and newer models.

v2.1.219

Fri, 24 Jul 2026 19:18:36 +0200

What's changed

Added Claude Opus 5 (claude-opus-5), now the default Opus model — 1M context, fast mode at $10/$50 per Mtok
Added sandbox.network.strictAllowlist setting to deny non-allowlisted hosts for sandboxed commands without prompting
Added DirectoryAdded hook that fires after /add-dir or the SDK register_repo_root control request registers a new working directory mid-session
Added mcp_server_errors to the headless stream-json init event, listing --mcp-config entries skipped by config validation; terminal runs print a startup warning
Added the workflowSizeGuideline settings key so the advisory Dynamic workflow size guideline can be set from any settings file; the /config row is hidden while one does
Added nested subagent forwarding in stream-json: subagents spawned at depth-2+ now appear when --forward-subagent-text is set, keyed by their spawning Agent tool_use id
Fixed claude -p text output dropping the answer already produced when a turn dies on a mid-stream API error
Added HTTP status and error text to claude mcp list and /mcp when a server fails to connect, and a warning for MCP config values with hidden leading or trailing whitespace
Fixed a permission you approved while a self-hosted runner was restarting being dropped when the session resumed, so the approved action now runs
Fixed the Fable model row showing "Requires usage credits" for plans that include it, when a stale cache had baked the label in
Fixed a SIGTERM arriving while a self-hosted runner was starting up leaving a stale active row until the lease expired; it now deregisters cleanly
Added structured failure categories to self-hosted runner spawn and session failures, so hook errors, runner crashes and config errors can be told apart
Fixed the /model picker showing the merged Opus row as plain "Opus" instead of "Opus (1M context)"
Fixed copy-on-select inside GNU screen printing base64 into the terminal instead of copying the selection
Fixed Remote Control clients keeping a stale fast-mode status after a model switch, reconnect, or failed org check
Fixed CLAUDE_CODE_GIT_BASH_PATH on Windows exiting or being used as bash when the path isn't a bash/sh binary; it's now ignored with a warning
Fixed Vim mode: pressing ← on an empty prompt now returns to the agent view from NORMAL mode, not just INSERT
Fixed screen-reader mode rewriting the entire input line on every keystroke instead of echoing only the typed character
Improved the "Remote Control is only available via api.anthropic.com" error to name the specific setting that caused it
Improved claude --teleport to show which repo your current checkout points at when it doesn't match the session's repo
Changed dynamic workflows to default to a medium size guideline (aim for fewer than 15 agents); pick another size or unrestricted with Dynamic workflow size in /config
Changed managed MCP allowlist/denylist ${VAR} entries to resolve from the startup environment and managed-settings env instead of settings-file env
Changed the /model picker to highlight only the newest model's name, so the highlight marks the new release rather than an arbitrary subset of the list
Added the current default workflow size to the running-workflow status line, with a pointer to /config for changing it
Removed Opus 4.7 from fast mode; /fast now applies to Opus 5 and Opus 4.8
Updated the claude-api skill to default to Claude Opus 5, with a migration path from Opus 4.8
Subagents can now spawn nested subagents up to depth 3 by default (was 1); set CLAUDE_CODE_MAX_SUBAGENT_SPAWN_DEPTH=1 to disable nesting

11 Dinge, die fast alle im Internet machen – und die strafbar sind

Fri, 24 Jul 2026 17:49:16 +0200

Das Internet – grenzenlose Freiheit? Mitnichten, auch im World Wide Web gelten Gesetze. Doch selbst wenn ihr glaubt, euch an die Regeln zu halten, gibt es Dinge, mit denen man sich strafbar macht, ohne es zu wissen.

Model Context Protocol is going stateless to make scaling simpler

Fri, 24 Jul 2026 17:40:37 +0200

Model Context Protocol (MCP), the emerging standard for connecting AI models to external tools and enterprise data, is undergoing its biggest architectural overhaul yet.

“The session-based model made sense when MCP servers were local processes on a developer’s laptop. In production, it became an operational tax,” said Muskan Bandta, cloud associate at ZopDev.

It should also make AI workflows more portable, resilient, and easier to orchestrate across distributed environments, he said.

MCP’s new features

Other changes to MCP include the addition of a Multi Round-Trip Requests (MRTR) mechanism that changes how AI agents request additional information they need to complete a task.

Routable transport headers, another addition, enable API gateways and other networking infrastructure to identify and route MCP requests without inspecting their contents.

They reduce processing overhead, lower latency, and let enterprise teams enforce routing, rate-limiting and security policies more efficiently using existing API management infrastructure, Jena said.

Rebuilding the trust boundary

The deprecation of Sampling is likely to have the biggest impact because it changes who is responsible for interacting with foundation models, said Jena.

Updated MCP SDKs

That backward compatibility should make the transition largely incremental, except for enterprises that built custom infrastructure around MCP’s earlier session-based architecture, Bandta said.

Identifying and auditing those session dependencies may not be easy, Jena warned.

Model Context Protocol is going stateless to make scaling simpler

Fri, 24 Jul 2026 17:38:35 +0200

Model Context Protocol (MCP), the emerging standard for connecting AI models to external tools and enterprise data, is undergoing its biggest architectural overhaul yet.

“The session-based model made sense when MCP servers were local processes on a developer’s laptop. In production, it became an operational tax,” said Muskan Bandta, cloud associate at ZopDev.

It should also make AI workflows more portable, resilient, and easier to orchestrate across distributed environments, he said.

MCP’s new features

Other changes to MCP include the addition of a Multi Round-Trip Requests (MRTR) mechanism that changes how AI agents request additional information they need to complete a task.

Routable transport headers, another addition, enable API gateways and other networking infrastructure to identify and route MCP requests without inspecting their contents.

They reduce processing overhead, lower latency, and let enterprise teams enforce routing, rate-limiting and security policies more efficiently using existing API management infrastructure, Jena said.

Rebuilding the trust boundary

The deprecation of Sampling is likely to have the biggest impact because it changes who is responsible for interacting with foundation models, said Jena.

Updated MCP SDKs

That backward compatibility should make the transition largely incremental, except for enterprises that built custom infrastructure around MCP’s earlier session-based architecture, Bandta said.

Identifying and auditing those session dependencies may not be easy, Jena warned.

This article first appeared on InfoWorld.

Offline und Online: Final Fantasy XIV Fan Festival in Berlin

Fri, 24 Jul 2026 16:08:43 +0200

Die Diskussion, ob Videospiele ein Nischenphänomen sind oder längst ein fester Teil unserer Gesellschaft geworden sind, brauchen wir zum Glück nicht mehr führen. So spielen in der Altersgruppe zwischen 16 und 49 Jahren fast vier Fünftel der Deutschen regelmäßig Videospiele. Und selbst bei den Über-60-jährigen ist es zumindest fast jeder Fünfte. Was für ein wichtiger […]

reMarkable Paper Pure im Test: Fast wie Schreiben auf Papier

Fri, 24 Jul 2026 15:57:10 +0200

Nettes Gadget oder sinnvoller Alltagsbegleiter? COMPUTER BILD hat das E-Ink-Tablet reMarkable Paper Pure in einem Praxis-Test genau unter die Lupe genommen.

2TB Crucial P310 SSD is on sale, bringing fast Gen4 performance to laptops, desktops, and workstations

Fri, 24 Jul 2026 15:33:52 +0200

2TB Crucial P310 NVMe SSD is reduced in price, making it a smart upgrade for everyday PCs and laptops.

Fast alle Unternehmen haben einen Cyber-Notfallplan - elektroniknet

Fri, 24 Jul 2026 14:58:34 +0200

Für die vierte Ausgabe der Studie Cybersicherheit in Zahlen wertete Statista mehr als 300 Statistiken aus und befragte im Rahmen einer repräsentativen ...

Fast drei Jahre nach Habecks Heizungsgesetz: Karlsruhe fällt klares Urteil – „unzulässig“

Fri, 24 Jul 2026 14:52:42 +0200

Karlsruhe hat eine Organklage gegen Heizungsgesetz-Verfahren für unzulässig erklärt. Der CDU-Politiker Thomas Heilmann ist mit seiner Klage gegen das Gesetzgebungsverfahren zum Heizungsgesetz gescheitert.

Netto erhält weltweit ersten Supermarkt aus dem 3D-Drucker

Fri, 24 Jul 2026 14:39:34 +0200

Im Schwarzwald entsteht der weltweit erste Supermarkt aus dem 3D-Drucker.

Aleksej Keksel

In der beschaulichen Gemeinde Neubulach im Nordschwarzwald nähe Calw wird derzeit Bau- und Digitalgeschichte geschrieben. Denn hier findet eine Weltpremiere statt: Es entsteht der weltweit erste Supermarkt aus dem 3D-Betondrucker. Mieter wird der Discounter Netto.

Mit einer Grundfläche von rund 1.700 Quadratmetern und einer gedruckten Wandfläche von über 1.300 Quadratmetern setzt das Projekt neue Maßstäbe für den industriellen Einsatz automatisierter Bauverfahren. Wo früher Maurer Stein auf Stein setzten, ziehen heute zwei mobile Instatiq P1 Roboter (Progress One) präzise ihre Bahnen.

Roboter statt Kelle und Mörtel

Die Maschinen spritzen den Beton Schicht für Schicht übereinander, bis Wände mit einer Höhe von bis zu sieben Metern entstehen. Dabei können die Roboter auf der Baustelle flexibel umgesetzt und neu positioniert werden. Das ermöglicht den Bau großflächiger Gewerbeimmobilien in Rekordzeit.

So wurde der gesamte Wandrohbau inklusive aller baulichen Schnittstellen in nur etwa vier Wochen realisiert. „Mit dem ersten gedruckten Supermarkt zeigen wir, dass 3D-Druck im realen Gewerbebau angekommen ist“, erklärt Markus Schilling von Instatiq. Gleichzeitig zeigt das Projekt, dass der 3D-Druck nicht länger nur für kleine Pilotprojekte oder Wohnhäuser reserviert ist. Für Optimisten stellt er gar eine wirtschaftlich relevante Alternative für den großflächigen Handel dar.

Grüner Beton

Während die Druckroboter die Hauptarbeit an den Wänden leisten, wird die Konstruktion durch konventionelle Bauteile wie Stützen und Ringbalken ergänzt. Diese Verzahnung von digitaler Bauvorbereitung und klassischem Rohbau war eine der Herausforderungen, die durch die Zusammenarbeit von Firmen wie Nelcon und der Köhler Bauunternehmung gemeistert wurde.

Schicht für Schicht werden die Wände aus fast CO₂-neutralem Beton gedruckt.

Aleksej Keksel

Doch nicht nur die Technik ist revolutionär, sondern auch das Material. Zum ersten Mal wird bei einem solchen Großprojekt der Near-Zero-Zement evoZero von Heidelberg Materials eingesetzt. Dieser Zement basiert auf der sogenannten CCS-Technologie (Carbon Capture and Storage). Dabei wird das CO₂ direkt im Werk im norwegischen Brevik abgeschieden und anschließend dauerhaft im Meeresboden gespeichert.

Nachhaltiger Gewerbebau

Das Ergebnis ist ein 3D-Druckbeton, der signifikant weniger CO₂ verursacht, ohne dass die Rezeptur oder die technischen Eigenschaften – wie Pumpfähigkeit und Formstabilität – verändert wurden. Matthias Fischer von Heidelberg Materials betont denn auch: „Hier kommen zwei zukunftsweisende Ansätze unter realen Bedingungen zusammen: innovative Materialien und neue Bauverfahren.“

Hinter dem Projekt steht ein breites Partnernetzwerk. Bauherr ist die Bäckerei Sehne, die das Gebäude nach Fertigstellung an den Lebensmittelhändler Netto Marken-Discount vermieten wird. Für Netto ist die Filiale in Neubulach ein klares Bekenntnis zu Innovation und Ressourcenschonung. So heißt es bei dem Discounter: „Der Einsatz von 3D-Druck zeigt, wie sich innovative Technologien bereits heute effizient und nachhaltiger im Filialbau einsetzen lassen.“

Unter dem Strich ist der Supermarkt in Neubulach mehr als nur ein Gebäude. Er ist schlicht ein Beweis dafür, dass automatisiertes, schnelles und nachhaltigeres Bauen keine Zukunftsmusik mehr ist. Mit rund 292 Kubikmetern Druckbeton ist das Projekt laut Instatiq derzeit das weltweit größte realisierte 3D-gedruckte Gebäude.

Getting a grip on shadow tokens and AI blowouts

Fri, 24 Jul 2026 14:04:46 +0200

Going forward, CIOs need to thread the AI needle between governance that encourages transparency and reasonable spend without stifling innovation.

When shadow tokens result in real costs

Shifting the culture from usage to yield

Stopping token waste before an AI blowout

This article is published as part of the Foundry Expert Contributor Network.
Want to join?

007 First Light has Denuvo Anti-Tamper DRM removed

Fri, 24 Jul 2026 13:30:06 +0200

That was fast? 007 First Light only released back at the end of May, and it appears that Denuvo Anti-Tamper DRM has now been removed.

Read the full article on GamingOnLinux.

Zehn Jahre Streit beendet: .web-TLD kann jetzt endlich starten

Fri, 24 Jul 2026 12:39:26 +0200

Nach fast zehn Jahren juristischer Auseinandersetzungen und regulatorischer Hürden ist die neue Top-Level-Domain .web einen entscheidenden Schritt näher an den Markt gerückt: Sie wurde offiziell in die Root-Zone des Domain Name Systems (DNS) aufgenommen. (Weiter lesen)

Critical IntelliJ IDEA Path Traversal Flaw Enables Code Execution

Fri, 24 Jul 2026 12:09:07 +0200

JetBrains has patched a critical path traversal vulnerability in IntelliJ IDEA that allows attackers to achieve arbitrary code execution simply by tricking a developer into opening a maliciously crafted project. Tracked as CVE-2026-59792, the flaw carries a CVSS score of 9.6 and was disclosed on July 10, 2026, alongside a broader batch of security fixes […]

The post Critical IntelliJ IDEA Path Traversal Flaw Enables Code Execution appeared first on Cyber Security News.

Golden Chickens TAG-195 Launches TinyEgg and ChonkyChicken Modular Malware

Fri, 24 Jul 2026 12:09:05 +0200

TAG-195, also known as Golden Chickens or Venom Spider, is a financially motivated MaaS developer long linked to credential theft and remote access tooling for multiple criminal operators. Insikt Group recently identified four new malware families in this ecosystem, naming two of them TinyEgg and ChonkyChicken, alongside a modularized ChonkyChicken variant and a Chrome-focused helper […]

The post Golden Chickens TAG-195 Launches TinyEgg and ChonkyChicken Modular Malware appeared first on Cyber Security News.

„Fast & Furious“-Star rechnet mit der Action-Reihe ab – und gibt den Fans damit recht

Fri, 24 Jul 2026 12:03:04 +0200

Die „Fast & Furious“-Filme haben sich über die Jahre stark von ihren Wurzeln in der Street-Racing-Szene entfernt – das kritisiert jetzt auch ein Star der Reihe.

The Microsoft agent framework wars are over. The real architecture decision starts now

Fri, 24 Jul 2026 11:04:58 +0200

Over the past year, I had the same conversation with almost every team starting an AI initiative. Should we build on Semantic Kernel, AutoGen or Foundry?

At first it felt like the most important architectural decision we’d make. Each framework had its own philosophy, each promised to be the foundation for enterprise AI, and picking the wrong one felt like an expensive mistake. I spent a lot of time helping teams weigh the trade-offs.

Looking back, I think we were asking the wrong question. I certainly was.

I watched teams spend months debating SDKs while the decisions that actually decided whether their applications survived production went unexamined. Some built elaborate orchestration layers for workflows that a few deterministic functions would have handled. Others avoided agent frameworks entirely and later found they’d designed themselves into a corner.

Then Microsoft settled it for us. It introduced the unified Agent Framework, quietly moved Semantic Kernel and AutoGen into maintenance mode, and the debate I’d spent months refereeing was suddenly over. Turns out the answer to “which of the three” was “none of the three, here’s a fourth.” The framework hit version 1.0 and general availability in April 2026, stable across .NET and Python.

What surprised me wasn’t the decision. It was how fast a debate that had eaten so much of our attention stopped mattering. Microsoft changed the menu.

It didn’t change the meal.

The framework was never the hard part

Framework selection dominated almost every early conversation I had about enterprise agents. Which SDK do we standardize on? Which orchestration model gives us the most flexibility? Which one is Microsoft actually betting on?

Fair questions. But after a year of watching these projects play out, I’ve slowly come around to a different view. Those weren’t the questions that decided anything.

The first question I ask now is much smaller. Does this thing actually need an agent?

It sounds obvious, and I still get it wrong sometimes. But it’s the mistake I see most. On one project, a team spent weeks designing a multi-agent workflow for a process that ran the same four steps every time: read a document, validate it, call an API, send a notification. The diagrams looked great. The system in production didn’t. A few well-tested functions would have been easier to build, easier to maintain and a lot easier to trust.

Part of this is just that “agent” has become the word everyone reaches for. Sometimes it’s the right call. Sometimes it’s a workflow we already knew how to build, wearing a newer label. An agent earns its complexity when it genuinely has to decide things you can’t predetermine, choosing between tools, adapting to what it finds, working out its own next step. If you already know every step, you have a workflow, and a workflow is usually the better engineering choice. The consolidation didn’t change that. It just made it easier to see.

What building production agents actually taught me

Once I stopped fixating on frameworks, the same three problems kept showing up. None of them had anything to do with the SDK.

Context beats model choice

Early on I spent a lot of time comparing models, the way you’d agonize over a restaurant menu and then order what you always order. Now I spend most of it thinking about context, which is far less fun and far more useful.

I’ve watched good models fail because they were handed too much, not too little. One team I worked with gave the model access to nearly every internal document they had on the theory that more information meant better answers. It went the other way. Responses got slower, less consistent and sometimes skipped right past the thing that actually mattered. When we cut the context down to only what the task needed, the quality jumped almost immediately. I didn’t predict that. It taught me to be suspicious of “just give it everything.”

The best agent systems I’ve worked on weren’t the ones with the biggest context windows. They were the ones careful about what reached the model, and when. That’s not something the framework hands you.

Failure is where the real work is

Most agent demos look great because they’re built around the happy path. Production doesn’t extend that courtesy.

I remember a project where everything held up in testing. Then a downstream API timed out after the agent had already completed several earlier steps. We couldn’t just restart, because part of the business process had already gone through. We ended up spending far more time on recovery logic than we ever spent on prompts. That project changed how I think about this work. The hard part was never getting the model to make a decision. It was making sure the system didn’t fall apart when reality refused to follow the script.

Tool calls fail partway through. APIs return inconsistent data. Models call the same tool over and over because the last answer wasn’t what they wanted. That’s not the exception; that’s a normal Tuesday. Whether you retry, roll back, pause for a human or push on with partial results is a judgment call, and no framework is going to make it for you.

Identity is the real security boundary

This one surprised me most. The moment an agent stops being a chatbot and starts touching real business systems, identity matters more than orchestration.

Every project gets to the same question eventually. Who is this agent actually acting as? The developer’s credentials? A service account? The user who asked? Get it wrong and you’ve built something autonomous running with more access than any single person should have, which is exactly the kind of thing that looks fine until an audit. The Agent Framework, like most modern tooling, makes it easier to wire agents to tools through standards like the Model Context Protocol. That helps. But where human approval belongs, what needs extra authorization, how much rope to give the thing, those are still yours to decide.

The surprises weren’t technical

Here’s what I didn’t see coming. The hardest part of last year wasn’t technical at all. It was organizational. The moment a team heard “agent,” expectations shifted under everyone’s feet. Business stakeholders started expecting full autonomy. Developers assumed the thing could reason its way through anything. People started designing for flexibility before we’d even agreed on what problem we were solving. The word did damage before any code did. I found myself spending as much time resetting expectations as I did discussing architecture.

Build for change, not for today’s winner

I don’t think the teams that struggled last year picked the wrong framework. Semantic Kernel was reasonable. AutoGen was reasonable. Foundry made sense for plenty of cases. I’d have signed off on any of them.

The ones that got hurt put all their eggs in one framework, treating it as the foundation of the whole system instead of as one more dependency. Microsoft provided a migration path. But teams that had tightly coupled their applications to framework-specific abstractions discovered that migrating and rewriting are not the same thing. That wasn’t Microsoft’s doing. It was their own architecture’s. The teams that moved easily had kept their business logic, prompts and orchestration loose enough to evolve independently of any one SDK. For them, the change was a manageable project, not a teardown.

For what it’s worth, nobody I work with is treating this as an emergency. Most are moving the smaller workloads first, watching how they behave and leaving the production-critical systems alone until they actually understand the new abstractions. That’s the right instinct. And I doubt this is the last consolidation we’ll see, the ecosystem is still young, frameworks will keep absorbing each other and over time the differences between them will be operational more than architectural.

I don’t regret the framework debates, honestly. They were reasonable at the time. What changed wasn’t Microsoft’s roadmap.

It was mine. Watching these systems run in production taught me that the framework is the easiest piece to swap out. Recovery logic, context management, security boundaries, the business workflow itself, those stay with you long after today’s SDK gets replaced by tomorrow’s.

So, Microsoft made one decision easier by turning three frameworks into one. Good. Five years from now we’ll be on different tools, and we’ll still be asking the same handful of questions.

Does this actually need an agent? Does it have the right context? Can it recover when something breaks, because something will? Is it acting as the right person?

Those questions outlast every rewrite. That’s where I’ve learned to put my effort.

Frameworks come and go. Good architecture has to survive all of them.

This article is published as part of the Foundry Expert Contributor Network.
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Samsung's 4TB 990 EVO Plus internal SSD is down to $599.99, combining massive storage with ultra-fast 7,250MB/s performance

Fri, 24 Jul 2026 11:03:52 +0200

Samsung's 4TB 990 EVO Plus internal SSD is down to $599.99, combining massive storage with ultra-fast 7,250MB/s performance.

Archäologen entdecken fast 2.000 Jahre alte Überreste – „weitreichende Bedeutung“

Fri, 24 Jul 2026 10:51:03 +0200

Archäologische Entdeckungen helfen dabei, vergangene Kulturen und historische Ereignisse besser zu verstehen. Neue Funde liefern wertvolle Hinweise auf das Leben früherer Gesellschaften.

Phishing-Fallen: KI-Mails, QR-Codes, falsche Warnungen – so schützen Sie sich

Fri, 24 Jul 2026 10:47:37 +0200

Zwei große Änderungen hat es in den vergangenen Jahren bei Phishing-Angriffen gegeben: Die Kriminellen erstellen mithilfe von generativer KI sprachlich fast perfekte Mails, die in Stil, Struktur und Tonalität kaum noch von legitimen Nachrichten zu unterscheiden sind. Wo früher holpriges Deutsch sofort Misstrauen geweckt hat, liest sich heute eine Phishing-Mail wie eine echte Mitteilung von Microsoft, einer Bank oder einem Paketdienst.

Auch das Design wirkt meist höchst professionell. Zum anderen sind auch die technischen Tricks beim Datendiebstahl heute höher entwickelt. Einige Maschen umgehen sogar eine Zwei-Faktor-Authentifizierung. Das Ziel der Angreifer bleiben vor allem Zugangsdaten, Session-Tokens und persönliche Infos.

1. Microsoft-365-Log-in-Falle trickst Zwei-Faktor-Anmeldung aus

Eine neue Angriffsmethode verwendet den originalen Microsoft-Anmeldedialog und kommt entsprechend fast ohne gefälschte Webseiten aus. Die Kriminellen nutzen dafür den Oauth-Device-Code-Flow. Das ist ein Anmeldeverfahren für Geräte oder Programme, die keinen brauchbaren Browser oder keine komfortable Texteingabe bieten, etwa Smart-TVs, IoT-Geräte, Drucker oder CLI-Tools.

Offiziell heißt er „OAuth 2.0 Device Authorization Grant“. Mit der Methode lassen sich auch Konten übernehmen, die mit einer Zwei-Faktor-Authentifizierung geschützt sind.

Die Kriminellen schicken an ihre Opfer eine Phishing-Nachricht und geben vor, das Gerät der Opfer müsste für den Log-in ins Microsoft-365-Konto neu autorisiert werden. Die Nachrichten beginnen meist harmlos, etwa mit „Ihre Sitzung ist abgelaufen“, und bieten einen Link zur Neuanmeldung. Wenn das Opfer dem Link in der Nachricht folgt, landet es zunächst auf einer gefälschten Website, schließlich aber beim offiziellen Microsoft-Authentifizierungsverfahren für Geräte und Anwendungen (Oauth-Device-Code-Flow).

Proofpoint

Es handelt sich um echte Microsoft-Meldungen und Webseiten. Allerdings autorisiert das Opfer nicht den Zugang zu seinem eigenen PC oder Smartphone, sondern eine Anwendung der Kriminellen. Diese bekommen nach der Freigabe durch das getäuschte Opfer einen Access-Token. Damit kann die feindliche Anwendung per API auf das Microsoft-Konto zugreifen, ohne dass noch einmal ein Passwort eingegeben werden muss.

Übrigens: Die meisten dieser Angriffe verstecken den Link zur gefälschten Website in einem QR-Code. Dieser entgeht den Spam-Filtern eher als ein üblicher Link, und es lässt die meisten Opfer vom PC auf das Smartphone wechseln.

Auf diesem ist es wegen des kleineren Bildschirms und oft fehlender Sicherheits-Software noch wahrscheinlicher, dass das Opfer die Täuschung nicht bemerkt. Eine ausführliche Analyse der Angriffe auf Microsoft-365-Konten haben die Sicherheitsexperten von Proofpoint veröffentlicht.

2. Support-Masche: Ihr Computer ist gesperrt & Co.

Die Support-Masche ist zwar nicht neu, funktioniert aber nach wie vor: Noch immer fallen zahlreiche Menschen auf die perfide Betrugsstrategie herein. Zu den prominenten Opfern zählt Bundestagspräsidentin Julia Klöckner.

Mutmaßlich staatlich organisierte Angreifer kontaktierten sie über den Messenger-Dienst Signal und gaben sich als vermeintliche Signal-Support-Mitarbeiter aus. Unter einem Vorwand forderten sie Klöckner und weitere Politiker auf, ihre PIN einzugeben. Dadurch erlangten die Angreifer Zugriff auf die Signal-Konten der Betroffenen – und damit auf private Chats und Kontakte.

Das Bundesamt für Verfassungsschutz und das Bundesamt für Sicherheit in der Informationstechnik (BSI) haben gemeinsam einen Leitfaden veröffentlicht, der potenziellen Opfern hilft zu prüfen, ob ihr Signal-Konto übernommen wurde.

Bundesnetzagentur

Ebenfalls weiterhin verbreitet sind Angriffe durch angebliche Microsoft-Support-Mitarbeiter. Die Betrüger kontaktieren ihre Opfer per Telefon, E-Mail oder über gefälschte Pop-up-Warnungen im Browser.

Dabei behaupten sie, der Windows-PC habe ein Sicherheitsproblem – etwa sei der Computer gesperrt oder mit Schadsoftware infiziert. Anschließend versuchen sie, die Betroffenen zur Installation einer Fernwartungssoftware oder eines vermeintlichen Sicherheitstools zu bewegen. Tatsächlich erhalten die Angreifer dadurch oft vollständigen Zugriff auf den Rechner.

3. Gefälschter Microsoft Defender warnt

Der Microsoft Defender ist ein Windows-Bordmittel und schützt PCs gegen alle bekannten PC-Viren. Entsprechend alarmierend ist für viele Nutzer eine Warnung dieses Antiviren-Tools. Eine gefälschte Form dieser Warnung erscheint mal per E-Mail, mal als Pop-up im Browser. In diesen Nachrichten wird behauptet, der Schutz des Defenders müsse kostenpflichtig erneuert werden. In der Folge werden die Nutzer auf gefälschte Shop-Webseiten geleitet, die eine Zahlung für einen Virenschutz verlangen.

Grundsätzlich gilt: Der Microsoft Defender ist auf Privat-PCs ein Bordmittel und kostenlos in Windows enthalten. Eine Zahlung ist nicht nötig. Sollte die Warnung per Mail bei Ihnen landen, löschen Sie diese einfach. Schlägt sie als Pop-up im Browser auf, schließen Sie einfach das Browser-Fenster, notfalls mit der Tastenkombination „Alt+F4”.

Der Antivirenspezialist Norton hat eine Anleitung veröffentlicht, die erklärt, wie sich solche Pop-up-Warnungen im Browser beseitigen lassen, falls sie sich im System festgesetzt haben.

4. Microsoft-Onedrive: Cloud-Phishing über Freigaben

Cloud-Dienste wie Onedrive von Microsoft nutzen viele Windows-Nutzer mehrmals täglich. Genau deshalb sind sie ein attraktives Ziel für Phishing. Statt klassischer E-Mails mit Dateianhängen erhalten die Nutzer Freigabe-Benachrichtigungen mit einem Betreff wie „Dokument wurde mit Ihnen geteilt“. Der Inhalt wirkt meist harmlos und oft beruflich relevant: Rechnungen, Projektpläne, Gehaltslisten oder interne Dokumente.

Besonders tückisch ist die Kombination aus echten und gefälschten Elementen. Manche Angriffe nutzen tatsächlich legitime Cloud-Plattformen, bieten dort aber manipulierte Dokumente an. Das Ziel dieser Angriffe sind mehrheitlich die Log-in-Daten der Opfer zu Ihren Cloud- und Mail-Konten. Diese werden dann von den Angreifern übernommen und etwa für neue Phishing-Attacken genutzt.

5. Lieferdienste, Lieferdienste und noch mal Lieferdienste

Phishing im Namen von Paketdiensten gehört zu den stabilsten Angriffsmustern überhaupt und wird gleichzeitig immer ausgefeilter. Der Grund ist die hohe Alltagstauglichkeit: Fast jeder erwartet regelmäßig Lieferungen und ist deshalb kaum misstrauisch, wenn eine Mail, SMS oder Whatsapp zum Thema Paketversand eintrudelt. Moderne Varianten enthalten nicht nur einfache Textlinks, sondern vollständige Tracking-Systeme.

Arne Arnold

Diese Seiten sind dynamisch aufgebaut und simulieren echte Logistikprozesse. Beim Sendungsverlauf heißt es dann etwa: „Zustellung fehlgeschlagen – bitte Adresse bestätigen“ oder „Letzte Möglichkeit zur Terminänderung“. Besonders kritisch ist die Kombination aus Zeitdruck und Kontext. Wer Opfer eines solchen Angriffs wird, gibt meist seine Log-in-Daten für Shopping- oder Zahlungsdienste preis. Oder er überweist den Angreifern direkt Geld, da angeblich Steuern, Bearbeitungsgebühren oder ein Expresszuschlag fällig sind.

Arne Arnold

Übrigens: Ab dem 1. Juli 2026 gibt es zusätzliche Abgaben auf Sendungen aus Nicht-EU-Ländern. Für Waren unter 150 Euro sind dann pauschal 3 Euro Zollgebühr und eine Einfuhrumsatzsteuer fällig. Einige Kurierdienste verlangen zusätzlich eine Servicepauschale für diese Zollanmeldung. Über die genauen Kosten informiert eine Seite der Verbraucherzentrale NRW. Es lohnt sich, die tatsächlichen Kosten zu kennen, denn es ist wahrscheinlich, dass zu diesem Termin vermehrt Phishing-Mails zu diesem Thema versendet werden.

6. Phishing zu Online-Banking gibt es immer

Phishing zum Online-Banking gibt es fast schon so lange wie das Online-Banking selbst. Die Bedrohungslage ist aber so angespannt wie nie, denn die Angriffe sind nun wirklich zahlreich. Über die neuesten Phishing-Fallen informieren unter anderem die Verbraucherzentralen.

Beispiele aus dem Mai 2026 lauten etwa so: „Bestätigung Ihrer Mobilfunknummer erforderlich“. Absender ist vorgeblich die Easybank. Eine Fälschung von Commerzbank-Mails warnt vor einem fälligen „Photo-TAN Update“, bei dem ein „einmaliger Abgleich der Zugangsdaten“ nötig ist.

Andere Phishing-Mails geben vor, von der Deutschen Bank zu sein, und fordern eine Reaktivierung des „photoTAN-Sicherheitszertifikats“. Auch Kunden der DKB erhielten im Mai Phishing-Mails.

Verbraucherzentrale

Sollten Sie eine Mail von Ihrer Bank bekommen, klicken Sie auf keinen Fall auf einen der Links in dieser Mail. Wenn Sie sich unsicher sind, ob Sie reagieren sollen, rufen Sie die Website Ihrer Bank über Ihren Browser auf.

Sollte es tatsächlich ein Anliegen der Bank geben, wird es Ihnen nach dem Einloggen in Ihr Online-Konto angezeigt. Oder Sie rufen Ihre Bank einfach per Telefon an und fragen, ob Informationen von Ihnen benötigt werden.

7. Phishing per Post: Kreditbetrug per Postident-Verfahren

Diese Phishing-Angriffe erreichen Sie per Post in Ihrem echten Briefkasten. Die Briefe geben vor, von Ihrer Bank zu stammen, und fordern Sie auf, Ihre Daten erneut per Postident zu bestätigen. Postident ist ein Verfahren der Post, mit dem Sie Ihre Identität gegenüber anderen, etwa einer neuen Bank oder einem Kreditinstitut, bestätigen können. Wer das beigefügte Schreiben nutzt, legitimiert in der Regel einen hohen Kredit bei einer anderen Bank.

Schäden von 15.000 bis 25.000 Euro sind hier keine Seltenheit. Vorangegangen ist meist ein Diebstahl Ihrer genauen Daten (Postadresse, Hausbank, Arbeitgeber, Verdienst), den die Angreifer dann nutzen. An die Daten kommen die Kriminellen etwa über gefälschte Wohnungsinserate bei Immoscout24 oder ähnlichen Portalen. Wer sich auf eine Wohnung oder ein Haus mit Gehaltszetteln und weiteren Angaben bewirbt, hat bereits alle wichtigen Daten für den Postident-Betrug verraten. Seien Sie beim Postident-Verfahren stets besonders vorsichtig. Konkrete Tipps lesen Sie hier.

Sicherheitstipps: Phishing erkennen und blockieren

An diesen Merkmalen erkennen Sie betrügerische Nachrichten:

Unverlangter Kontakt: Sie erhalten eine E-Mail, Whatsapp oder SMS über eine Gutschrift, eine Lastschrift oder andere finanzielle Ansprüche, obwohl Sie aktuell keine Buchung storniert oder reklamiert haben.
Zeitdruck: Die Nachricht suggeriert dringenden Handlungsbedarf und fordert zur schnellen Reaktion auf.
Verdächtige Links: Die Links in der Nachricht sind hinter einem QR-Code maskiert, führen zu unpassenden Domains oder sind ungewöhnlich lang.
Aufforderung zur Dateneingabe: Seriöse Unternehmen fordern in Nachrichten oder Mails nur äußerst selten zur Eingabe sensibler Daten auf.
Unpersönliche Anrede: Oft fehlt die namentliche Ansprache oder es werden generische Formulierungen verwendet.

Diese Maßnahmen schützen vor Phishing-Fallen:

E-Mail, SMS und Whatsapp & Co. sind keine geschlossenen Nachrichtenkanäle: Sie müssen damit rechnen, auch betrügerische Nachrichten zu erhalten.
Misstrauen Sie Links in Nachrichten: Klicken Sie keine Links an und scannen Sie keine QR-Codes, wenn Log-in-, Zahlungs- oder Sicherheitsaufforderungen in der Mail stehen. Öffnen Sie den jeweiligen Dienst im Browser über die manuelle Eingabe der Adresse.
Nutzen Sie Browser und Passwortmanager als Frühwarnsystem: Wenn Ihr Passwortmanager Ihre Log-in-Daten auf einer Webseite nicht einfügen möchte, dann ist die Domain vermutlich gefälscht. Achten Sie zudem auf die Warnungen Ihres Browsers.
MFA aktivieren: Nutzen Sie immer eine Zwei- oder Multifaktor-Authentifizierung, wenn diese angeboten wird. Vor allem Passkeys erhöhen die Sicherheit.
Remote-Support misstrauen: Installieren Sie keine Fernwartungs-Tools, nachdem Sie unaufgefordert kontaktiert wurden.
Freigaben hinterfragen: Wenn Sie Freigaben für Dateien in Cloud-Speichern erhalten, kontaktieren Sie zunächst den Absender, idealerweise telefonisch.

Infos zu aktuellen Angriffen: Informieren Sie sich über Phishing-Kampagnen etwa bei der Verbraucherzentrale NRW.

Als letzte Verteidigungslinie lassen sich Antivirenprogramme, Browserschutz und Spezial-Tools einsetzen:

Antivirus: Große Sicherheits-Suiten wie G Data Internet Security filtern Phishing-Mails heraus, bevor sie diese Nachrichten öffnen.
Browser-Schutz: Browser von Sicherheitsanbietern blockieren viele aktuelle Phishing-Seiten, etwa der KI-Browser Norton Neo.
Spezial-Tools: KI-Chatbots wie Scamio von Bitdefender begutachten verdächtige Nachrichten und warnen vor gefährlichen Inhalten.

Arne Arnold

European Commission Fines Google €890 Million for DMA Breaches

Fri, 24 Jul 2026 10:23:58 +0200

Google fined €890 million by the European Commission for breaching the Digital Markets Act (DMA) over its practices on Google Search and Google Play. The Commission issued two separate fines of €460 million and €430 million, finding that Google had failed to comply with the DMA's rules on self-preferencing and steering. The decisions concern how Google ranks its own services in search results and how app developers can communicate alternative offers to users through Google Play.

Google Fined Over Self-preferencing on Google Search

The Commission found that Google breached the DMA by giving preferential treatment to its own services, including shopping, hotels, transport and sports results, compared with third-party services appearing in Google Search. Under the DMA, designated gatekeepers are required to treat their own services and third-party services fairly and without discrimination in search rankings. According to the Commission, Google gives its own services greater prominence by placing them at the top of search results or displaying them with enhanced visuals and filters. The Commission said similar third-party services do not receive the same level of prominence. The Commission's decision requires Google to treat third-party services featured in its search results in a fair and non-discriminatory manner compared with its own services. The Commission also noted that Google has proposed and started testing changes to the way it presents its own services on Google Search, including free services covering shopping, hotels and flights. The Commission said these changes represent substantial progress towards compliance and will be monitored. Google has also proposed and started testing changes involving shopping ads and content-related services, including sports. The Commission is assessing these changes and will continue discussions with the company. The dialogue will also cover Google's proposals for applying the principles of the decision to AI Overviews and AI Mode.

Google Play Restrictions Lead to Second Fine

The second decision concerns Google's anti-steering practices on Google Play. Under the DMA, app developers distributing apps through Google Play must be able to inform customers about alternative, often cheaper offers at no cost. Developers should also be able to direct users to make purchases through other channels, including websites and alternative app stores. The Commission found that Google failed to meet these requirements. It said Google restricted app developers from freely communicating and promoting offers and from concluding contracts with users through distribution channels of their choice, including third-party app stores. The Commission acknowledged that Google can charge a fee for facilitating the initial acquisition of a new customer by an app developer through Google Play. However, it found that the level of Google's steering-related fees and the length of time those fees were charged went beyond what is considered compliant with the DMA. Google has since rolled out changes related to its steering terms. The Commission said these changes represent good progress towards compliance but will be assessed in light of the cease and desist order issued as part of the decision.

Digital Markets Act Enforcement Brings Compliance Deadline

As part of the two decisions, the Commission has ordered Google to end the identified non-compliance. The company must implement measures addressing both search rankings and its anti-steering rules. Google is required to comply with the Commission's decisions within 60 days. If it fails to do so, it could face periodic penalty payments of up to 5% of its total worldwide turnover. The fines take into account the gravity and duration of the non-compliance. The Commission said it also considered the recurrence of the breaches and concluded that the fines were proportionate and appropriate. Google may appeal the decisions.

Commission Investigations Began in 2024

Google was designated as a gatekeeper in September 2023 for its online search engine, Google Search. On 25 March 2024, the Commission opened non-compliance investigations into Google's measures addressing self-preferencing and its steering rules. On 19 March 2025, the Commission informed Google of its preliminary view that the company was in breach of the DMA. Google subsequently exercised its rights of defence by reviewing the documents in the Commission's investigation files and responding in writing to the preliminary findings. The two decisions followed a detailed investigation that included feedback from market participants and extensive dialogue with Google. The Commission said the decisions demonstrate its continued enforcement of the DMA and its focus on protecting fairness, business opportunities, consumer choice and innovation in digital markets.

EU Fines Google €890m For Competition Breaches

Fri, 24 Jul 2026 10:12:15 +0200

Latest DMA fines target alleged self-preferencing in Google Search, developer restrictions on Android app store This article has been indexed from Silicon UK Read the original article: EU Fines Google €890m For Competition Breaches

The post EU Fines Google €890m For Competition Breaches appeared first on IT Security News.

13 clevere USB-C-Gadgets, die viel mehr können, als nur Ihr Handy aufzuladen

Fri, 24 Jul 2026 10:03:52 +0200

Ich weiß nicht, wie es in Ihren Schubladen aussieht, aber meine sind voll mit Kabeln, die ich wahrscheinlich nie wieder benutzen werde, weil die Technologie sich ziemlich weiterentwickelt hat. Zum Glück scheint sich USB-C durchzusetzen, denn es gibt keinen falschen Weg, diese Dinger anzuschließen. Aber nicht nur Smartphones, Tablets und Laptops verwenden diese Anschlüsse, sondern auch eine lange Liste von Gadgets.

Wir haben den Markt nach versteckten Perlen durchforstet: Gadgets, bei denen Sie denken werden: “Wow, ich wusste gar nicht, dass ich das brauche, aber es wird mein Leben so viel einfacher machen!” Schauen wir uns also an, mit welchen coolen Geräten Sie Ihre Typ-C-Kabel und -Anschlüsse nutzen können.

heat it Insektenstichheiler

PCWelt/heat it/Amazon

Wer im Sommer oder Urlaub oft von Mücken geradezu heimgesucht wird, wird sich besonders über dieses geniale Gadget freuen: Dieses winzige Gerät, das sich einfach per USB-C mit Ihrem Smartphone verbinden lässt, kann mit gezielter Wärme den Juckreiz von Stichen und Insektenbissen deutlich reduzieren.

Ähnlich wie größere Varianten, etwa diesen hier von Beurer, muss man den kleinen Stichheiler von heat it einfach nur per App aufladen und dann auf den Stich halten. Die Hitze erledigt dann den Rest und zersetzt die Proteine, die im Mückenstich dafür sorgen, dass die Stelle anschwillt, juckt und schmerzt. Ein echtes Must-have für den Sommer, und es kostet nicht mal 25 Euro.

Mini-Ventilator

KIMMOO / Amazon / PC-WELT

Ziemlich genial sind auch diese kleinen Ventilatoren, die man im Sommer einfach in der Hosentasche mitnehmen und bei Bedarf schnell einsetzen kann. Wer schon einmal bei 30 Grad in einer vollen S-Bahn saß und sich zumindest ein wenig frische Luft gewünscht hat, der wird dieses Gadget lieben.

Der Mini-Ventilator für unter 20 Euro mit USB-C-Ladeanschluss kann ganz einfach über Powerbanks, Computer, Laptops oder USB-Ladegeräte betrieben werden. Es gibt aber noch kleinere und günstigere Ventilatoren, die man direkt ans Handy anschließen kann, und gerade mal 2-3 Euro pro Stück kosten. Diese sorgen aber auch für einen weniger starken Luftstrom, daher würden wir eher die erste Variante empfehlen.

USB-C zu HDMI Adapter

Anker

Wenn Ihr Laptop über einen USB-C-Anschluss verfügt, Sie Ihren Monitor aber schon seit Ewigkeiten nicht mehr geupgradet haben, verfügt Ihr Bildschirm vermutlich nicht über einen Typ-C-Anschluss. Dieser Adapter von Anker löst dieses Problem für Sie, sodass Sie das normale HDMI-Kabel, das Sie an Ihren Monitor anschließen, mit diesem Gerät verbinden können.

Der Adapter unterstützt Auflösungen von bis zu 4K bei 60 Hz, was ziemlich beeindruckend ist. Dies ist auch eine raffinierte Möglichkeit, Ihr Smartphone oder Tablet an Ihren Monitor oder Fernseher anzuschließen. Das Gerät kostet circa 13 Euro, aber wir haben es schon für nur 13 Euro bei Amazon gesehen.

Mini-Schraubendreher

Amazon

Wenn Sie häufig an Ihrem Computer oder anderen Geräten herumschrauben müssen, ist dieser elektrische Mini-Schraubendreher vielleicht genau das Richtige für Sie. Dieser stiftförmige Schraubendreher verfügt über vier LED-Leuchten, damit Sie besser sehen können, woran Sie gerade arbeiten, und dreht sich 200 Mal pro Minute, sodass Sie die Arbeit schneller erledigen können.

Der Schraubendreher verfügt über 64 verschiedene Aufsätze, es sollte also für so gut wie jeden Einsatzzweck ein passender dabei sein. Er kann über USB-C aufgeladen werden und hält bis zu drei Stunden lang. Der Elektroschraubendreher kostet normalerweise 41 Euro, ist aber auch für 32 Euro im Angebot erhältlich.

USB-C microSD-Kartenleser

Ugreen / Amazon

Die meisten Laptops verfügen heutzutage nicht mehr über einen Kartenleser, daher muss man andere Wege finden, um Daten von diesen kleinen Dingern zu übertragen. Egal, ob Sie Daten von der Karte Ihrer Dashcam oder Ihrer Kamera übertragen möchten, dieser winzige Ugreen USB-C microSD-Kartenleser ist dafür bestens geeignet.

Der Kartenleser ist so klein, dass Sie ihn wahrscheinlich an einem Schlüsselbund befestigen können. Wenn Sie eine etwas vielseitigere Version bevorzugen, bietet Ugreen ein Modell an, das auch mit SD-Karten kompatibel ist und sowohl über USB-C- als auch USB-A-Anschlüsse verfügt. Alle diese Modelle kosten weniger als 10 Euro und sind daher eine lohnende Investition.

Anker Nano Power Bank

Anker

Eine Sache, die Sie unbedingt in Ihrer Tasche, Reisetasche oder in Ihrem Rucksack haben müssen, ist eine Powerbank. Denn man weiß schließlich nie, wann das Telefon einen mal im Stich lässt und nach einer Aufladung schreit. Die Anker Nano Power Bank ist zum Glück so klein, dass sie fast überall hinpasst.

Sie verfügt über einen faltbaren USB-C-Anschluss und einen Anschluss an der Seite, sodass Sie bei Bedarf zwei Geräte gleichzeitig aufladen können. Die Kapazität von 5.000 mAh reicht gerade aus, um Ihr Handy einmal vollständig aufzuladen, was in der Not entscheidend sein kann. Außerdem kostet sie gerade mal 26 Euro, ist aber immer wieder mal reduziert.

Endoskopkamera mit Licht

Ennovor

Auch wenn Sie diese Endoskopkamera von Ennovor nicht jeden Tag benutzen werden, kann sie doch sehr nützlich sein. Sie schließen sie einfach an Ihr Telefon an, installieren eine App und sehen alles, was Ihre Kamera macht. Mein Kollege schwört darauf und verwendet sie, um alles zu finden, was so hinter dem Schreibtisch verloren geht.

Gerade dann, wenn Sie am Auto arbeiten oder nach undichten Leitungen suchen, ist das Teil super nützlich. Da sie die Schutzklasse IP67 hat, können Sie sie sogar in Ihr Aquarium stellen. Die Kamera wird mit einem circa 5 Meter langen, halbstarren Kabel und diversem Zubehör geliefert, darunter ein Haken, ein Magnet und ein Spiegel. Sie können diese Kamera im Moment für um die 22 Euro kaufen.

Blukar Taschenlampe

Blukar

Egal, wer Sie sind oder wo Sie so unterwegs sind: Sie brauchen eine Taschenlampe. Je kleiner, desto besser, denn so können Sie sie in jede Tasche stecken. Dieses Modell von Blukar verfügt über einen eingebauten 1800mAh-Akku, den Sie mit einem der vielen Typ-C-Kabel aufladen, die Sie so herumliegen haben.

Mit einer einzigen Ladung können Sie bis zu 16 Stunden arbeiten, was ziemlich gut ist. Sie können zwischen vier verschiedenen Blitzmodi wählen, darunter auch einer, der Ihnen hilft, Hilfe zu signalisieren. Außerdem kostet die kleine Taschenlampe weniger als 10 Euro. Es gibt also keine Ausrede, sich diesen Tipp entgehen zu lassen.

Samsung Flash-Laufwerk

Samsung

Die allermeisten Flash-Laufwerke haben einen USB-A-Anschluss, aber dieses von Samsung besitzt einen Typ-C-Anschluss. Mit Übertragungsgeschwindigkeiten von bis zu 400 MB/s können Sie Dateien im Handumdrehen verschieben. Das Coole an diesem USB-Stick ist, dass Sie ihn sogar in Ihr Smartphone stecken können, um 4K-Videos direkt darauf aufzunehmen.

Das Samsung Type-C Flash-Laufwerk ist in verschiedenen Speicheroptionen erhältlich, angefangen bei 64 GB bis hin zu 512 GB.

Mini Luftpumpe

PCWelt/Cycplus/Amazon

Ebenfalls perfekt für den Sommer geeignet ist diese kleine Luftpumpe von Cycplus, die unterwegs in jede Tasche passt. Sie kann nicht nur einen Fahrradreifen innerhalb von zwei Minuten auf Knopfdruck wieder aufpumpen, sondern wird auch per USB aufgeladen. Sie eignet sich laut Hersteller für Mountainbikes, Rennräder, Motorräder und sogar Autos!

Neben der Luftpumpe selbst lässt sich dieses praktische Gadget auch als Taschenlampe oder Powerbank für unterwegs einsetzen. Alles Dinge, die man auf einer Fahrradtour sehr gut gebrauchen kann. Und für den Preis von nur 45,99 Euro wirklich empfehlenswert, wenn Sie schnelle Hilfe bei platten Reifen benötigen. Im Angebot kostet sie sogar nur 37 Euro.

Leselampe mit Buchklemme

Gritin / Amazon

Wer auch gerne und viel liest, und das teilweise bis spät in die Nacht hinein, wird sich über dieses kleine Teil hier freuen: eine Leselampe, die Sie einfach an Ihr Buch klemmen können. Das ist jetzt vielleicht nichts bahnbrechend Neues, doch mit insgesamt drei Farbtemperaturen und fünf verschiedenen Lichtmodi können Sie individuell anpassen, wie viel Licht Sie zum Lesen brauchen.

Die Leselampe von Gritin kostet bei Amazon gerade mal 13 Euro und besitzt einen 1200-mAh-Akku, der je nach Nutzung bis zu 80 Stunden durchhält. Danach können Sie ihn einfach per USB wieder aufladen. Den Hals der Lampe können Sie nach Belieben hin- und herschwenken, und es gibt sogar eine kleine Ladeanzeige. Was will man mehr?

Externes DVD-Laufwerk

Amicool

Wenn Ihr Laptop wie viele heutzutage kein DVD-Laufwerk besitzt, dann wird Ihnen dieses kleine Gerät sehr nützlich sein. Dieses externe DVD-Laufwerk von Amicool wird über USB-C (oder USB-A) an Ihren Laptop angeschlossen und bietet Ihnen das optische Laufwerk, das Sie manchmal benötigen.

Es kann DVDs und CDs problemlos lesen und brennen, sodass Sie Software installieren, Dateien kopieren, Daten sichern, Spiele spielen und vieles mehr können. Sie müssen nicht einmal Treiber installieren, da dieses Gerät Plug-and-Play-fähig ist. Normalerweise kostet es 28 Euro, aber oft ist es schon für etwa 20 Euro zu haben, was ein absolutes Schnäppchen ist.

Wiederaufladbare Handwärmer

Amazon / PC-WELT

Wenn es langsam wieder kälter wird, wünscht man sich oft nichts sehnlicher, als seine Hände etwas schneller aufwärmen zu können. Das geht natürlich auch mit Handschuhen oder einer Tasse Tee, doch diese Handwärmer für 20 Euro sind nicht nur wiederaufladbar, sondern halten Ihre Finger mit einer einzigen Ladung auch bis zu 24 Stunden lang warm.

Die beiden Gadgets verfügen über einen Temperatursensor-Chip, mit dem Sie eine von drei Temperaturen für eine präzise Steuerung auswählen können. Außerdem stehen verschiedene Farben zur Auswahl, wodurch sie sich auch bestens als Geschenk eignen.

Die wichtigsten News der Kalenderwoche 30/2026: KI, Data und mehr

Fri, 24 Jul 2026 09:03:37 +0200

Was hat sich in der vergangenen Woche für Developer und IT-Pros getan? Was waren die wichtigsten Ankündigungen? Gab es neue Lernressourcen? Unser TechWiese-Team hat jede Menge Links für dich zusammengestellt.

Ryzen AI Embedded X100: AMDs Embedded-Sparte geht mit Strix Halo All‑In

Fri, 24 Jul 2026 09:03:36 +0200

Strix Halo lebt 2026 und darüber hinaus weiter. Als AMD Ryzen AI Embedded X100 werden die großen APUs noch Jahre verfügbar sein, genauer gesagt bis zu 10 Jahre. Denn so lange gibt AMD den neuen X100-Lösungen eine Lebensgarantie im 24/7-Betrieb. Das Ziel sind Robotik-Lösungen fast komplett aus AMD-Hand.

Fast alle Unternehmen haben einen Cyber-Notfallplan - elektroniknet

Fri, 24 Jul 2026 08:58:03 +0200

Mehr als 5000 Beschäftigte befragt. Für die vierte Ausgabe der Studie Cybersicherheit in Zahlen wertete Statista mehr als 300 Statistiken aus und ...

Malicious RubyGems Turn Developer Machines Into Monero Miners and Spread Through SSH

Fri, 24 Jul 2026 08:41:01 +0200

A malicious RubyGems campaign has turned seemingly useful developer packages into tools for hidden cryptocurrency mining. The poisoned packages can consume a machine’s computing power, slow down development work, and quietly generate Monero for the attackers. The campaign also goes…

The post Malicious RubyGems Turn Developer Machines Into Monero Miners and Spread Through SSH appeared first on IT Security News.

Klotzen statt kleckern: Alphabet pumpt 45 Milliarden Dollar in KI

Fri, 24 Jul 2026 08:19:07 +0200

Alphabet hat im zweiten Quartal 2026 knapp 45 Milliarden US-Dollar in Sachanlagen investiert – fast doppelt so viel wie ein Jahr zuvor. Der massive Ausbau der KI- und Rechenzentrumsinfrastruktur verschlang damit mehr Geld, als das laufende Geschäft in diesem Zeitraum einbrachte.

Malicious RubyGems Turn Developer Machines Into Monero Miners and Spread Through SSH

Fri, 24 Jul 2026 08:09:51 +0200

The post Malicious RubyGems Turn Developer Machines Into Monero Miners and Spread Through SSH appeared first on Cyber Security News.

Überstunden: Diese Erwerbsgruppe kennt kaum normale Arbeitszeiten

Fri, 24 Jul 2026 07:35:17 +0200

Fast jede*r Zweite Selbstständige mit Beschäftigten arbeitet 49 Stunden pro Woche oder mehr. Unter Angestellten trifft das nur auf 4,1 Prozent zu.

Hier erodiert die KI-Produktivität

Fri, 24 Jul 2026 06:08:03 +0200

Ob aus individuellen KI-Produktivitätsgewinnen auch eine performantere Organisation entsteht, hängt maßgeblich von der Gestaltung durch das Management ab.

Gorodenkoff | shutterstock.com

Die Debatte über (generative) künstliche Intelligenz (KI) fokussiert sich meist auf die Produktivität des Einzelnen – und die Zahlen hierzu sind bemerkenswert. So zeigte ein großangelegtes Feldexperiment der Harvard Business School in Zusammenarbeit mit der Boston Consulting Group, dass der Einsatz generativer KI bei Wissensaufgaben sowohl die Ergebnisqualität als auch die Bearbeitungsgeschwindigkeit signifikant verbessert. Dabei ist der Nivellierungseffekt besonders beachtlich:

Während erfahrene Fachkräfte ihre Leistung um rund 17 Prozent steigerten,
lagen die Verbesserungen bei den schwächeren Teilnehmern bei über 40 Prozent.

Diese individuellen Produktivitätsgewinne übertragen sich jedoch nicht automatisch in gleichem Maße auf die Organisationsebene. Unternehmen schaffen nicht bloß durch die isolierte Performance Einzelner Wert, sondern vor allem durch die Fähigkeit, Wissen zu bündeln, komplexe Entscheidungen zu treffen und gemeinsam Innovationen hervorzubringen.

Wenn jedes Teammitglied zunehmend mit seinem eigenen KI-Assistenten interagiert, kann dies die Kernmechanismen erfolgreicher Zusammenarbeit beeinträchtigen. Die entscheidende Herausforderung besteht somit darin, den Zuwachs an individueller Produktivität in eine echte organisatorische Leistungssteigerung zu übersetzen.

3 Produktivitäts-Bruchstellen im KI-Zeitalter

Ob aus temporären Effizienzgewinnen nachhaltige Wettbewerbsvorteile entstehen, hängt deshalb maßgeblich davon ab, wie Führungskräfte die folgenden drei Bruchstellen gestalten, die aufeinander aufbauen. Diese repräsentieren jene organisatorischen Übergänge, an denen individuelle Produktivitätsgewinne durch KI in kollektive Leistungsnachteile umschlagen können – wenn sie nicht aktiv gestaltet werden.

1. Bruchstelle: Wissenssicherung

Der Weg zur Seniorität folgte lange einer stabilen Logik: Operative Erfahrung führte zu Expertise, diese wiederum zu Einfluss. Wer über mehr Wissen verfügte, übernahm komplexere Aufgaben und wurde zur zentralen Orientierungsperson im Team. Generative KI bricht diese Logik auf, da weniger erfahrene Mitarbeitende mithilfe von KI-Tools heute Analysen, Konzepte und Problemlösungen in hoher Qualität erstellen können. Ein Teil des traditionellen Wissensvorsprungs erfahrener Kräfte verliert dadurch seine Exklusivität.

Auf diese Weise gerät ein impliziter Erfolgsfaktor von Organisationen ins Wanken: der organische Wissenstransfer. Bislang floss Wissen primär über erfahrene Kräfte, was Mentoring und kollektives Lernen quasi „nebenbei“ sicherstellte. Fällt dieser Austausch weg, weil Aufgaben isoliert im Dialog mit der KI gelöst werden, besteht das Risiko eines doppelten Kompetenzverlusts:

Beschäftigte in Junior-Rollen können mithilfe von KI überzeugende Ergebnisse liefern, ohne den zugrundeliegenden Lösungsweg vollständig verstehen zu müssen. Der Lerneffekt wird dadurch reduziert und die Fähigkeit zur eigenständigen Problemlösung sowie zur kritischen Validierung der KI-Ergebnisse eingeschränkt. Die Folge ist eine wachsende, unkritische Abhängigkeit von der Technologie.
Erfahrene Fachkräfte laufen Gefahr, zur rein reaktiven Korrekturinstanz von KI-Ergebnissen zu werden. Ihre Erfahrung fließt dann nicht mehr aktiv in die Gestaltung ein, sondern beschränkt sich zunehmend auf die nachträgliche Qualitätskontrolle. Mögliche Folgen sind Motivationsverlust, mentaler Rückzug der Leistungsträger und der schleichende Verlust des in der Organisation vorhandenen Erfahrungswissens.

Um diesen doppelten Kompetenzverlust zu verhindern, müssen Führungskräfte den Rollenwandel der Seniorität aktiv gestalten. Künftig sollte sich diese nicht mehr primär über exklusives Fachwissen definieren, sondern über die Fähigkeit, die richtigen Fragen zu stellen, Zusammenhänge einzuordnen und Teams bei der kritischen Bewertung von KI-Ergebnissen anzuleiten. Diese Begleitung kann nicht erst im Rahmen der abschließenden Qualitätskontrolle erfolgen, sondern muss bereits während der Arbeit mit KI ansetzen.

Ziel ist es, Wissen, Erfahrungswerte und Kontext kontinuierlich zu vermitteln, die Urteilskraft der Junioren gezielt zu entwickeln und sie schrittweise zu einer eigenständigen Validierung und reflektierten Nutzung von KI-Ergebnissen zu befähigen.Gelingt dieser Rollenwandel nicht, können Teams zwar kurzfristig ihre individuelle Produktivität steigern, verlieren jedoch zunehmend das gemeinsame Verständnis für fachliche Zusammenhänge und den organisatorischen Kontext.

Die durch KI erzielten Produktivitätsgewinne können dann durch Fehlentscheidungen, steigenden Korrekturaufwand und einen schleichenden Qualitätsverlust auf Teamebene wieder aufgezehrt werden. Ohne die systematische Sicherung von Erfahrungswissen zu agieren, heißt, teure Fehler zu produzieren – nur deutlich schneller.

2. Bruchstelle: Qualitätssicherung

Die Herausforderung endet jedoch nicht bei der Wissenssicherung. Sie betrifft im nächsten Schritt auch die Art und Weise, wie Teams die Qualität ihrer Entscheidungen absichern, wenn ein wachsender Teil der Analyse-, Bewertungs- und Wissensarbeit durch KI unterstützt wird. Je mehr Vorarbeit KI übernimmt, desto stärker hängt die Qualität organisatorischer Entscheidungen davon ab, die generierten Ergebnisse kritisch zu überprüfen und einzuordnen.

Eine Besonderheit generativer KI besteht darin, dass ihre Leistungsfähigkeit keiner intuitiven Logik folgt. Die eingangs zitierte Studie beschreibt dieses Phänomen als „Jagged Technological Frontier“: KI-Systeme können bei bestimmten Aufgaben eine Performance auf Expertenniveau erreichen, während sie bei scheinbar ähnlichen Fragestellungen überraschend große Schwächen aufweisen. Die Grenzen dieser Leistungsfähigkeit sind jedoch nicht für jeden Nutzer ohne weiteres erkennbar.

Genau daraus entstehen neue Herausforderungen für Führungskräfte: Mit der Integration von KI in den Arbeitsalltag steigt das Risiko des sogenannten “Automation Bias” (PDF), also der Tendenz, algorithmischen Empfehlungen unkritisch zu vertrauen. Die Gefahr liegt dabei selten in offensichtlichen Fehlern, sondern in plausiblen (aber falschen) Antworten, die überzeugend formuliert sind und deshalb ungeprüft übernommen werden.

Zudem greift ein Beschleunigungseffekt: KI verkürzt den Weg von der Fragestellung bis zum Ergebnis erheblich und damit auch jene Diskussionen, mit denen Teams bislang die Qualität ihrer Entscheidungen abgesichert haben. Wo früher Analysen debattiert und Annahmen abgewogen wurden, liegt heute in Sekunden ein Resultat vor. Das eigentliche Risiko liegt dabei darin, dass Ergebnisse schneller entstehen als sie kritisch überprüft werden können. So ist es möglich, dass Produktivitätsgewinne mit einem Verlust an Ergebnisqualität einhergehen.

Führungskräfte sollten daher Strukturen schaffen, die den Einsatz von KI transparent gestalten und Ergebnisse offen zur Diskussion stellen. KI-generierte Inhalte müssen denselben kritischen Maßstäben unterworfen werden wie die Arbeit eines erfahrenen Teammitglieds. Dazu benötigen Teams Validierungskompetenz, eine Verantwortungskultur und etablierte Prüfprozesse. Die entscheidende Frage sollte daher nicht sein, ob ein Ergebnis vom Menschen oder der Maschine stammt. Sondern, wer dieses auf fachliche Korrektheit überprüft und die Verantwortung für die darauf basierende Entscheidung übernimmt.

KI steigert zwar die Geschwindigkeit – das wird jedoch erst dann zum echten Wettbewerbsvorteil, wenn es mit Urteilskraft und gelebter Accountability einhergeht.

3. Bruchstelle: Innovationssicherung

Wie zuvor beschrieben, kann der Einsatz generativer KI gemeinsame Diskussionen verkürzen, was wiederum die Innovationskraft des Unternehmens belasten kann. KI liefert dabei nicht nur einen ersten Lösungsvorschlag, sondern erhöht zugleich die Wahrscheinlichkeit, dass sich Teams früh auf eine gemeinsame Richtung festlegen. Echte Innovationen entstehen jedoch selten durch schnelle Einigkeit: Sie entstehen durch produktive Reibung, tiefgehende Debatten und dadurch, etablierte Denkmuster zu hinterfragen.

Ein wesentlicher psychologischer Mechanismus dieser frühen Konvergenz ist der sogenannte Ankereffekt: Da KI in Sekundenschnelle plausible Ergebnisse liefert, wirkt dieser erste Entwurf als kognitiver Anker. Das menschliche Gehirn orientiert sich somit unbewusst an diesem Startpunkt, was den Suchraum für alternative Lösungen vorzeitig verengt und so die Innovationsfähigkeit schwächen kann.

Führungskräfte stehen deshalb vor der zusätzlichen Aufgabe, produktiven Widerspruch bewusst zu organisieren. Kritische Rückfragen und konträre Sichtweisen sind das notwendige Gegengewicht zur KI-generierten Konvergenz. Und: Organisationen verlieren selten ihre Innovationskraft, weil ihnen Antworten fehlen. Vielmehr, weil sie aufhören, Alternativen ernsthaft zu diskutieren. Eine höhere Geschwindigkeit im Lösungsprozess führt daher nicht automatisch zu höherer Innovationsfähigkeit.

Entscheidend ist, dass Organisationen weiterhin Räume schaffen, in denen unterschiedliche Perspektiven aufeinandertreffen, bestehende Annahmen hinterfragt werden und neue Ideen entstehen können. Mit anderen Worten: KI erzeugt Konvergenz, Innovation braucht Divergenz.

KI schafft eine neue Realität

Diese drei Bruchstellen verdeutlichen, dass generative KI nicht nur Arbeitsprozesse, sondern auch die Grundlagen organisationaler Leistungsfähigkeit grundlegend verändern kann. Wissen entsteht anders, Entscheidungen werden anders getroffen und Innovation entwickelt sich unter veränderten Voraussetzungen.

Je stärker KI in den Arbeitsalltag integriert wird, desto entscheidender wird es, die genannten Bruchstellen bewusst zu gestalten. Der nachhaltige Nutzen von KI entsteht nicht allein durch den Einsatz der Technologie, sondern durch die Fähigkeit einer Organisation, ihre Strukturen, Lernprozesse und Entscheidungsmechanismen an diese neue Realität anzupassen. (fm)

Dieser Beitrag wurde im Rahmen des deutschsprachigen Experten-Netzwerks von Foundry veröffentlicht. Lust mitzumachen? Jetzt bewerben!

Microsoft launches new in-house AI models it says cut costs up to 89% versus OpenAI

Fri, 24 Jul 2026 02:50:17 +0200

Microsoft AI released two new in-house models into public preview on Wednesday — MAI-Image-2.5-Pro, its highest-fidelity image generator to date, and MAI-Voice-2-Flash, a speech model built for high-volume enterprise workloads — while publishing production data that amounts to the company's most aggressive argument yet that it can power its own products without leaning on OpenAI's frontier models.

The announcement, made by Microsoft AI's Superintelligence team, lands roughly a year after the company committed to building purpose-built models internally, and it arrives with an unusual level of specificity about where those models now run: Bing, PowerPoint, OneDrive, Dynamics 365, Excel, GitHub Copilot, and Azure. The message to enterprise buyers — and, implicitly, to OpenAI — is that Microsoft's homegrown models are no longer research projects. They are production infrastructure serving millions of users.

"Each of these enhancements is a step toward the same goal: Microsoft products, powered by Microsoft models," the company wrote in its announcement blog.

How MAI-Image-2.5-Pro and MAI-Voice-2-Flash stake out opposite ends of the AI cost curve

The two new releases occupy opposite ends of what Microsoft calls the quality-speed-cost curve, and the positioning is deliberate. MAI-Image-2.5-Pro targets the premium tier: hero imagery, detailed editing, and precise in-image text rendering — the last of which has long been a notorious weak spot for image generation models. Microsoft priced the model at $5 per million text input tokens, $8 per million image input tokens, and $106 per million image output tokens. The base MAI-Image-2.5 model recently launched at No. 2 for image editing on Arena, the community leaderboard that has become a de facto scoreboard for generative media.

The creative industry appears to be taking notice. Rob Reilly, global chief creative officer at advertising giant WPP, called the Pro model "a strong leap forward for GenMedia tools" in a statement included in Microsoft's announcement, adding that "Microsoft has firmly established itself among the leaders in generative AI."

MAI-Voice-2-Flash goes the other direction. First previewed at Microsoft's Build conference, Flash runs twice as fast as MAI-Voice-2 and costs 32% less, priced at $15 per million characters. It is designed for the unglamorous but enormous market of high-volume voice — call centers, voice agents, and real-time speech applications where latency and cost-per-call matter more than marginal gains in expressiveness. Together, the two models reflect a strategy of building families of models rather than a single flagship, because, as the company put it, a creative studio chasing maximum fidelity has very different needs from a customer service operation handling millions of calls a day.

Microsoft's production metrics show in-house models cutting GPU costs by up to 89%

The model launches are arguably less newsworthy than the deployment metrics Microsoft attached to them — numbers that read like a systematic case for swapping out third-party frontier models across its product portfolio.

Bing Image Creator now runs entirely on MAI-Image-2.5, end to end, marking the first time the consumer image tool is fully in-house. In PowerPoint, Microsoft says MAI-Image-2.5 reduces GPU costs by up to 84% compared with GPT-Image-2, OpenAI's image model. In OneDrive, where MAI-Image-2.5 is now the default for key image-editing scenarios, the company reports a 26% increase in save rates, roughly 25% lower P95 latency, and 2.5 times greater efficiency under medium-utilization production workloads.

On the voice side, MAI-Voice-2-Flash now powers Dynamics 365 Contact Center — the platform used by customers including T-Mobile and EasyJet — where Microsoft claims GPU cost reductions of up to 89%. The model is also integrated into Azure Voice Live for developers building speech-to-speech agents.

Perhaps the most consequential deployment sits in healthcare. Microsoft's Dragon Copilot, used by 170,000 medical providers and responsible for processing 28 million patient encounters last quarter, now runs on MAI-Transcribe-1.5 for its multilingual workflow across 58 languages. Microsoft says internal evaluations show a 50% relative reduction in both transcription and language-identification error rates across most languages — a meaningful claim in a domain where transcription errors can propagate directly into clinical notes.

Inside the 'hill-climbing' strategy that lets small models beat GPT-5.6 in Excel

In a companion post published the same day, Microsoft detailed the methodology behind these results — what it calls its "hill-climbing machine," an integrated flywheel of data, models, and the product "harness" that surrounds them.

The clearest example is MAI-Code-1-Flash, the lightweight coding model launched in GitHub Copilot in June. Microsoft says the model achieves an approximately 10% higher code accept rate than GPT-5.4 Mini and Claude Haiku 4.5 in VS Code, while using 10% fewer median tokens. Developer retention tells a similar story: users were 6% more likely to return across multiple days than with GPT-5.4 Mini, and 11% more likely than with Claude Haiku 4.5.

Then Microsoft did something more interesting. It took the MAI-Code-1-Flash checkpoint and further trained it inside an Excel reinforcement learning environment, teaching a coding model the tools and workflows of spreadsheet knowledge work. The result, according to production user feedback, is a model on par with GPT-5.6 for the most common Excel tasks — while being small enough to run on Nvidia's older H100 and even A100 GPUs rather than requiring the latest-generation accelerators.

That hardware detail deserves emphasis. Every major AI company is fighting for allocation of cutting-edge chips, and a model that delivers frontier-adjacent quality on two-generation-old silicon fundamentally changes the deployment economics. It also frees the newest hardware — including Microsoft's now-operational GB200 cluster — for training rather than serving.

Satya Nadella's 'frontier diffusion' manifesto redraws the OpenAI relationship

Microsoft CEO Satya Nadella framed the announcements in a lengthy post on X titled "Frontier Diffusion & Control," which functions as something close to a strategic manifesto. "We can now take saturated frontier capabilities and deliver them at scale and at lower cost through models optimized for high-usage products, while continuing to use frontier models for frontier needs," Nadella wrote, adding that Microsoft is "beginning to route traffic across our first-party surfaces to MAI whenever our models match or outperform frontier alternatives."

Translated from executive prose: capabilities that were state-of-the-art a year ago are now table stakes, and Microsoft believes it can replicate them cheaply for the specific, repetitive tasks that dominate real product usage. Why pay frontier prices for a frontier model when a user just wants to reformat a spreadsheet column?

Nadella was careful to note that "frontier models from OpenAI and Anthropic are part of the orchestration system alongside MAI" — but he also articulated a pointed principle of model independence, arguing that a company's evaluations "should continue to hill climb even when any given model has been removed."

“Keeping the harness, memory, context, and skills outside the model, he argued, is what gives Microsoft control. The subtext is hard to miss. Reuters reported in April that Microsoft’s exclusive license to OpenAI’s technology had been revised into a non-exclusive arrangement, and The Information reported last September that Microsoft had begun incorporating Anthropic models into some products. Wednesday’s announcement completes the triangle: Microsoft as orchestrator, with its partners’ frontier models as interchangeable components and its own models absorbing an ever-larger share of routine traffic.”

Developers cheer cheaper task-specific models while skeptics question Microsoft's track record

The response online captured both the appeal and the skepticism surrounding the strategy. "I love when people use small models for niche tasks," wrote one X user, @mavihsk, responding to Nadella's post. "Why do I have to use the all-knowing model just to change my field in Excel?" Another user, @nabu_lines, distilled the pitch neatly: "cost and performance both improve when you stop overusing the biggest model."

Others were less charitable about Microsoft's execution track record. "Microsoft is the worst when it comes to listening to user feedback," wrote designer @designedbyabin, arguing the company "will lose the AI race because they repeatedly failed to understand user needs." And one user, @tokenoverflow, offered a drier critique of the model-independence pitch: "i want it keep hill climbing after removing microsoft."

The skeptics raise a fair point. Microsoft's self-reported metrics — accept rates, save rates, GPU savings — come from its own internal evaluations, not independent benchmarks, and the company chooses which comparisons to publish.

But the strategy's logic does not depend on any single number. Nadella's framing that software now has "real marginal cost for the first time" explains why Microsoft is obsessive about tokens, GPUs, and serving costs: when AI features run on every keystroke across a billion-user product portfolio, an 84% GPU cost reduction is not an optimization. It is the difference between a viable business and a money pit.

Why Microsoft is turning its internal AI playbook into an Azure product

The final piece of the strategy is that Microsoft is selling the playbook, not just the models. Nadella explicitly positioned the hill-climbing approach as "a template for every other AI native, SaaS, or Enterprise company," and Microsoft is packaging the toolchain through Foundry and what it calls Frontier Tuning — letting enterprises train specialized models against their own proprietary evaluations and reinforcement learning environments. That turns Microsoft's internal cost-cutting exercise into an Azure product, and it gives enterprise customers a reason to run their AI workloads on Microsoft's cloud even if the models themselves come from elsewhere.

The company's emphasis on models trained "on clean, traceable, enterprise-grade data, without distillation from third-party models" serves the same commercial end. In an industry facing mounting scrutiny over training data provenance, Microsoft is betting that enterprise buyers — and courts — will care where model capabilities come from. Microsoft says it is now extending the hill-climbing approach to Copilot Chat, Outlook, and PowerPoint, and both new models are available in public preview through Microsoft Foundry and the MAI Playground. "None of this is an endpoint," the company wrote. "We're just getting started."

Seven years ago, Microsoft bet more than $13 billion that OpenAI would build the future of AI. Wednesday's announcement suggests the company has since learned a cheaper lesson: the future of AI may belong to whoever builds the frontier, but the profits belong to whoever makes it ordinary.

AgentForger proves AI agents can become persistent insider threats

Fri, 24 Jul 2026 02:32:32 +0200

A new attack method found by Zenity Labs reveals that AI agents are becoming persistent insiders that attackers can recruit, rather than malware they have to install.

Its researchers have discovered AgentForger, a phishing-based attack that silently creates and launches a fully autonomous AI agent within OpenAI workspaces.

Once running, the agent has full access to apps like Outlook, Slack, SharePoint, and Google Drive. It is configured to operate indefinitely without further user interaction, can approve its own access by toggling “never ask” settings, and can continue to act on new assignments sent via email by the attackers that control it. Broad, unfettered access to systems allows it to perform reconnaissance, harvest sensitive data and credentials, impersonate victims, and launch phishing campaigns.

While OpenAI resolved the vulnerability four days after disclosure, on a larger scale, AgentForger sheds light on what can happen when AI agents go rogue.

“We’re moving into a world where software doesn’t just help people work. It works alongside them,” said Michael Bargury, co-founder and CTO of agentic AI security platform Zenity. “As AI agents become more capable, attackers will naturally look for ways to influence them, just as they’ve always looked for ways to influence people.”

A ‘persistent operator’ that acts without approval

OpenAI’s Workspace Agents can connect and work autonomously across Outlook, Gmail, Slack, Google Drive, SharePoint, and Teams. Users open the agent builder, describe what the agent can do in natural language, connect to tools, set approvals, review and test, schedule actions, then publish. For instance, an agent can autonomously handle incoming emails, review and take actions with approval, gather information from various sources to send out daily briefings, or automatically respond to questions in ChatGPT or Slack channels.

Normally, this is “useful automation,” Zenity AI red team researcher Mike Takahashi wrote in a blog post. But in this attack, “the same scheduler becomes the persistence mechanism.”

The creation workflow kicks off the moment a user clicks on a phishing link containing instructions from the threat actor. For the attack to work, a victim must be logged into ChatGPT and Workspace Agents, and have at least one integration with another app, such as Outlook, Gmail, Slack, Google Drive, SharePoint, or Teams.

Because those connections already exist, OAuth consent screens are not triggered. Furthermore, the victim does not need to click on another link, keep a Builder tab open, or even visit ChatGPT again.

The forged agent is a “persistent operator;” it is installed on the original click and given a schedule, and at those predetermined times, the agent invokes itself, scans for emails from attacker addresses with the subject line “task”, carries those orders out, then returns results to the same attacker-controlled email address.

It goes undetected because the attacker prompt instructs the Builder to toggle Outlook to never ask for approval of its actions. Typically, the default is “always ask,” to keep agents from taking unauthorized action; that switch gives agents the ability to act without asking for human approval.

“AgentForger showed that an attacker could deploy an autonomous insider agent inside your ChatGPT workspace with a single click,” said Bargury. From there, it can continue to access information, harvest credentials from various sources, impersonate employees, and carry out phishing attacks and fraud while “leveraging the trusted victim’s identity.”

A ‘planted accomplice’ that does all the work

Once activated, AgentForger can perform reconnaissance to create an internal map of a company. For instance, agents can scan Outlook, Slack, Teams, Google Drive, SharePoint, or calendar data to identify people, roles, active projects, internal discussions, or all-hands recurring meetings. This can help attackers identify where in the enterprise to target next, based on active teams and channels, projects in the works, or prominent users.

“This is the kind of internal context an attacker normally has to build slowly,” Takahashi noted. But in this scenario, action is based on a single emailed assignment. The attacker’s “planted accomplice” does all the work.

In another scenario, the agent can steal data by searching for and identifying financial documents, business agreements, or invoices. Or, it can steal credentials by scanning for messages containing passwords, one-time codes, access tokens, password recovery links, or API keys. Further, it can impersonate victims to carry out phishing scams, for instance, by sending legitimate-looking Teams messages instructing users to confirm their credentials on a fake Microsoft login page.

In all cases, collected information is organized, analyzed, and sent back to the attacker.

“AgentForger points to something much bigger than a single vulnerability,” said Bargury. “It’s less about one bug and more about understanding how the security model changes as AI becomes part of everyday business operations.”

FOMO exposing security gaps

This isn’t necessarily about trust, but more about the need to move fast and adapt, Bargury emphasized. AI agents are helping employees automate work, make decisions faster, and get more done. But enterprises fear they’ll fall behind if they don’t move quickly enough.

“The challenge is that we’re introducing a fundamentally new kind of technology into the enterprise,” said Bargury. “The pressure to integrate the next AI feature is outpacing the security controls needed to safely deploy it.”

However, the answer isn’t to slow down adoption, he emphasized; the business value is too significant. Rather, the first step is understanding where AI agents exist, who created them, what they’re connected to, and what they’re allowed to do. And when it comes to autonomous agents, enterprises need to pay attention to the processes that trigger them: A schedule, an incoming email, or another automated event.

“Those triggers should be governed just as carefully as the agent itself,” said Bargury.

High-impact actions should require approval where appropriate, and security teams should be able to quickly disable an agent or its triggers if something doesn’t look right, he said.

More broadly, AI agents are introducing the need for a new security model, he pointed out. The question is no longer just “Does this agent have permission?” It’s also, “Is this the behavior we intended?”

“The organizations that answer both questions will be in the strongest position to adopt AI safely,” Bargury said.

Agentic coding goes hands free as OpenAI brings GPT-Live's full duplex voice control to Codex and ChatGPT on the desktop

Fri, 24 Jul 2026 00:20:42 +0200

Two weeks after debuting its more naturalistic GPT-Live audio AI model with full-duplex capabilities (listening and speaking at the same time), OpenAI is bringing it directly into developer workflows.

The company announced that GPT-Live now powers the ChatGPT desktop application on macOS and Windows, integrating directly with agentic systems like Codex and ChatGPT Work (which are separate experiences available in the ChatGPT desktop app).

When OpenAI initially launched GPT-Live on July 8, 2026, it introduced a continuous audio model capable of listening and speaking simultaneously—eliminating rigid turn-taking while delegating complex reasoning to background models like GPT-5.5.

Today's release expands that conversational layer to technical tasks, enabling software engineers to orchestrate multi-threaded coding jobs, review pull requests, and debug applications using natural voice commands.

As such, it could usher in a new era of "hands free" software development and even live, in-person group coding parties for Codex's more than 5 million weekly active users. Codex, of course, is the name given to OpenAI's models and harness focused on coding, but which the company has this year expanded into a more general productivity platform. An OpenAI spokesperson told VentureBeat this is the first time voice activation has been included natively with Codex on the desktop.

OpenAI posted a promotional video showing some of its employees, Codex developer experience engineer Jason Liu and Codex technical staffer Guinness Chen, speaking to the same ChatGPT desktop app session in the same room, each issuing different instructions and conversing with the same model.

New capabilities unlocked

At its core, this integration relies on decoupling the real-time voice layer from the underlying execution engines.

While GPT-Live maintains fluid conversation—inserting natural verbal acknowledgments like "got it" without interrupting the user—it passes heavy computational workloads to background reasoning models.

On macOS, the desktop application incorporates "Appshots" and screen context features, allowing ChatGPT Voice to analyze the frontmost window alongside local files, codebase structures, and active plugins.

This architecture creates a pair-programming dynamic where developers talk through problems conversationally while agents execute tasks asynchronously.

Rather than manually stopping coding sessions to type detailed instructions or switch windows, developers direct the system hands-free.

The full-duplex engine dynamically decides when to speak, pause, or invoke tools, maintaining conversational state even as background agents process complex code modifications.

Directing coding and complex builds with your voice alone

The central operational capability in this update centers on multi-task execution across Codex and ChatGPT Work environments.

Software engineers can initiate multiple concurrent task threads from a single spoken prompt. For instance, a developer preparing to ship a feature can instruct the system to investigate an open authentication bug, review a pending API migration pull request, and generate missing unit tests simultaneously.

The desktop application coordinates these actions across disparate contexts, tracing issues through Slack conversations, GitHub repositories, and local codebases.

Developers can also verbally convert design mockups into working code, splitting tasks across frontend, backend, and testing layers.

With support for multi-folder projects (build 26.715) and remote execution via iOS, engineers can check task progress, answer agent prompts, and redirect active jobs without switching applications or managing individual processes line by line.

Proprietary license

OpenAI’s voice-enabled desktop release operates under a proprietary, commercial enterprise model. Access is restricted to paid subscribers across Plus, Pro, Business, Enterprise, and Education plans.

For individual developers and corporate engineering departments, this commercial structure means the model weights, voice processing pipelines, and agent state architectures remain fully closed.

Organizations cannot modify or self-host the underlying systems. Furthermore, tasks initiated via ChatGPT Voice consume standard usage allocations directly from existing Codex and ChatGPT Work plan quotas, treating voice-triggered actions identically to standard agentic workloads.

Community reactions

Developer communities immediately noted the implications of bringing continuous full-duplex voice to autonomous coding workflows.

Reacting to the build 26.715 release announcement—which details voice integration and multi-folder project support—AI Insider journalist @ChrisGPT noted on X: "Today OpenAI will release voice and remote guidance for codex ! One step closer to personal AGI".

Early technical feedback highlights widespread enthusiasm for orchestrating complex agentic tasks hands-free, particularly when stepping away from the workstation or managing build pipelines remotely.

I built a Linux filesystem benchmark for corruption, snapshots, rebuilds and ENOSPC across 26 storage layouts

Fri, 24 Jul 2026 00:13:06 +0200

Most filesystem benchmarks measure throughput on a freshly formatted single device. That is useful, but it misses many of the reasons people choose btrfs, ZFS or bcachefs in the first place.

I built modern-fs-benchmark to examine modern filesystems as complete storage systems, including their features, failure modes and behavior over time.

The current matrix contains 26 configurations across btrfs, ZFS, bcachefs, ext4 and XFS over md/LVM, dm-integrity, native and LUKS encryption, parity layouts, and XFS on a ZFS zvol.

Hosted CI dashboard:

https://bartosz.fenski.pl/modern-fs-benchmark/

Experimental real-hardware dashboard:

https://bartosz.fenski.pl/modern-fs-benchmark/real-hw/

Apache-2.0 licensed source and complete methodology:

https://github.com/fenio/modern-fs-benchmark

The raw benchmark datasets are published under CC BY 4.0. Every result records the kernel and filesystem tool/module versions. CI artifacts also contain a full command trace, so the exact workload is inspectable rather than hidden behind a chart.

Why I started this project

Many filesystem benchmarks follow a familiar pattern: create a filesystem with mostly default options, mount it, run fio or another generic workload, and compare throughput.

General-purpose suites such as the Phoronix Test Suite are useful for conventional performance comparisons, but they do not focus on the machinery that makes modern copy-on-write filesystems interesting.

I wanted a benchmark designed around btrfs, ZFS and bcachefs as multi-device storage systems rather than treating them as interchangeable replacements for ext4 on a single freshly formatted disk.

That means testing behavior such as:

- Redundancy and degraded operation

- Snapshot aging, scaling and reclamation

- Compression and encryption

- Reflinks and clone divergence

- Fsync tail latency and responsiveness under load

- Rebuild and scrub behavior

- Near-full and hard-ENOSPC behavior

- Data integrity and recovery from corruption

Ext4 and XFS over md, LVM and dm-integrity are included as classic-stack baselines so the costs and benefits of integrated CoW designs can be compared with layered alternatives.

Silent corruption is one particularly important example. When one redundant copy is deliberately corrupted behind the filesystem, checksumming filesystems such as btrfs, ZFS and bcachefs can identify the damaged copy and recover from a valid replica.

Traditional md/LVM redundancy without data checksums can notice during a scrub that its copies disagree, but it cannot determine which one is correct. In my tests, some classic configurations returned corrupted data successfully without an application-visible error.

The dm-integrity configuration shows that a classic layered stack can obtain integrity protection too, with a measurable performance cost.

An important limitation

The main dashboard uses loop devices on GitHub-hosted VMs. Absolute throughput numbers and small differences between filesystems should not be interpreted as hardware rankings.

The hosted runs are primarily useful for:

- Correctness and integrity outcomes

- Comparisons within the same job

- Large behavioral differences

- Snapshot-aging and near-full shapes

- Trends across repeated runs

Real disks are required for meaningful absolute performance, concurrency scaling, device parallelism and mixed-media topologies.

I am aware of this limitation and do not want the hosted dashboard to suggest more precision than the underlying environment can provide.

The real-hardware experiment

Kent Overstreet, the creator of bcachefs, made one of his Hetzner machines available for a real-hardware experiment.

The server had two physical NVMe devices. It completed three full benchmark runs, which are available in the separate real-hardware dashboard. These runs provided meaningful absolute measurements and exposed concurrency behavior that cannot be observed when several loop devices share one virtual disk.

During the fourth run, one NVMe controller entered the kernel’s dead state. The existing machine environment had its operating system on RAID0 across the two NVMe devices, so the hardware failure also made part of /nix/store unreadable and eventually prevented new SSH sessions.

The incomplete fourth run is not being published as benchmark data. This was a failure of the underlying hardware, not a result attributable to any filesystem being tested.

I am grateful to Kent for providing the machine and making the real-hardware experiment possible. Without that access, the three existing hardware runs would not exist.

Where I would like to take it

Better hardware would not merely produce more reliable throughput numbers. It would enable an entirely new class of tests designed for multi-device and multi-tier filesystems.

I would eventually like to run the suite on a machine containing several storage classes, for example two HDDs, two SSDs and an NVMe device.

That could support scenarios such as:

- HDD, SSD and NVMe baselines using identical workloads

- bcachefs foreground and background targets

- ZFS HDD data vdevs with SSD special vdevs

- Separate ZFS L2ARC and SLOG experiments

- LVM dm-cache in writeback and writethrough modes

- Metadata and small-block placement on faster media

- Foreground latency during background migration

- Contention between fast and slow storage tiers

- Degraded operation and rebuild under application load

- Performance before, during and after promoting or evacuating a storage tier

These are the kinds of scenarios for which multi-device and multi-tier filesystems are built, but they cannot be represented honestly when every “device” is a loop file backed by the same cloud disk.

I am considering either renting a suitable dedicated server or eventually building and hosting my own machine. Providers such as Worldstream offer configurations close to what I need, but the recurring cost is currently outside the project’s budget.

For now, the benchmark will remain in its hosted-CI form for an unknown amount of time. The existing dashboard will continue to be useful for correctness, behavioral comparisons and regression tracking, but it cannot answer every real-hardware performance question or model complex mixed-media topologies.

I am also open to running the suite on hardware provided by someone else. A useful environment would need Linux root access, clearly identified block devices that may be wiped, and enough uninterrupted access to complete repeated runs. The hardware description, methodology and resulting data would remain public.

I would appreciate technical feedback:

- Which current tests are misleading or unfair?

- Which failure scenarios are missing?

- Which mixed-media topologies would be most useful?

- Which additional filesystems or layered stacks should be included?

- Which results deserve deeper investigation?

The methodology, implementation and raw results are public. If a filesystem is being tested in a way that misrepresents it, I consider that a bug in the benchmark.

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4 ways AI-driven defense is rewriting the cybersecurity playbook

Thu, 23 Jul 2026 21:34:50 +0200

The cybersecurity landscape has evolved beyond human scale. Today’s adversaries have replaced predictable, manual playbooks with machine-generated attack chains that can breach traditional controls in seconds. To bridge the gap, organizations must move past legacy, reactive controls and embrace a fundamentally different, AI-driven architecture: Agentic Endpoint Security (AES).

AES represents a paradigm shift, moving security from a passive monitor to an active participant in the defense lifecycle. It provides the visibility and automated guardrails necessary to govern autonomous AI agents and agentic tools, ensuring that as your workforce scales with AI, your security posture remains unbreakable.

With autonomous AI agents now capable of planning and executing multi-stage attacks at machine speed, the pressure on traditional security operations (SOC) has reached a breaking point. To survive this shift, the strategy is clear: we must fight AI with AI.

Here is how AI-driven defense, pioneered by Cortex XDR and the era of Agentic Endpoint Security, is fundamentally rewriting the cybersecurity playbook.

From reactive patching to proactive prevention

For decades, the industry lived in a “wait-and-see” mode waiting for a vulnerability to surface, waiting for a signature, and then rushing to patch the hole. But reactive methods just don’t hold up against modern “frontier” AI attacks that are constantly morphing.

AI-driven defense changes the game by shifting to a prevention-first architecture. Rather than relying on historical signatures, modern platforms deploy localized, ML-driven analysis to evaluate the intent and behavior of an active process, stopping threats pre-execution. Cortex XDR leads with a strict prevention-first approach by using AI-driven local analysis and behavioral threat protection; the XDR agent stops sophisticated threats pre-impact and pre-execution. This proactive stance reduces the overall risk profile by blocking malicious chains of events in real time across network, process, file, and registry activity.

2. Eliminating the “agentic blind spot”

As we all rush to adopt generative AI and automated workflows, a new gap has appeared: the “agentic blind spot.” Adversaries are now targeting AI assistants and automated scripts to bypass defenses. Since these digital agents often have deep access to enterprise data, a compromise here lets attackers move completely under the radar.

The new playbook requires securing this entire ecosystem. By combining the distinct capabilities of Cortex XDR and Koi Security, organizations can effectively close this gap. Koi Agentic Endpoint Security tracks everything from shell commands to prompts in real time, while Cortex XDR adds a layer of defense that identifies and neutralizes behavioral anomalies unique to these automated threats.

3. Machine-speed detection and “attack storylines”

When an attacker can move through your network in seconds, human-led teams can’t keep up. To make matters worse, most systems just flood analysts with low-quality, isolated alerts, leading to major burnout.

AI-driven defense fixes the investigation process by automatically stitching separate data points into a single, high-fidelity “attack storyline.” Cortex XDR uses thousands of machine learning detectors across endpoint, network, and cloud sources to group related signals into one cohesive case. This reveals the full story of an attack, letting your analysts focus on fast remediation instead of digging through piles of data, reducing alert noise by up to 98%.

4. Surgical and autonomous response

The final piece of the puzzle is moving from manual remediation to autonomous action. AI-driven response lets your SOC handle threats in minutes, not hours. The platform can automatically revoke compromised tokens or isolate endpoints at machine speed.

Cortex XDR delivers built-in enterprise-grade automation at no additional cost, providing over 120 out-of-the-box playbooks and 18 quick actions to handle up to 99% of incidents without manual intervention. Crucially, this level of automation requires an unbreakable foundation of agent resilience. To ensure the defense cannot be disabled by an adversary, Cortex XDR is certified in both the AVC EDR Detection and Anti-Tampering tests, successfully blocking all attempts to disable or modify the agent.

Summary

The threat landscape is changing faster than ever, driven by AI-powered attackers who exploit even the smallest gaps. But you don’t have to stay on the defensive. By shifting to a proactive, AI-driven architecture like the one built into Cortex XDR, you can stop threats before they happen, secure your agentic workflows, and automate away the noise that leads to analyst burnout.

The journey to a more resilient, AI-powered SOC doesn’t have to be daunting. With the right foundation in place, you’re not just keeping pace with the new threat landscape; you’re staying one step ahead. It’s time to move beyond the old manual playbook and embrace the future of security operations.

To learn more about Palto Alto Networks, visit https://www.paloaltonetworks.com.

New pip flag fixes longstanding Python frustration

Thu, 23 Jul 2026 21:27:38 +0200

A new version of Python’s native package management tool, pip, will remove a limitation that has frustrated Python developers for years. If you wanted to install the dependencies for a given package, but not install the package itself, you were stuck. Either you had to extract the dependency list from the package and install it by hand, or you had to build the whole package anyway.

Why was this a problem? Sometimes, you want only the dependencies for a package—for instance, as a way to create a separate environment for testing or another project. If you’re making source distributions via CI, some requirements might be needed to make the source distribution but aren’t actually included in it (e.g., Cython). You would need to install these requirements somewhere—apart from the project itself—to perform the build step.

A dependencies-only install mode for packages is a long-requested feature. Developer James O’Claire found many examples of such requests, along with various workarounds. Most of those involved third-party solutions of some sort.

Now, a new feature set to land in pip version 26.2 will fix this problem from the inside. The command pip install --only-deps will install only the dependencies for a given package. Note that it will not install build dependencies for the package, but only runtime dependencies.

Note that you can accomplish this with existing third-party tools. For instance, uv sync --no-install-project has the same behavior. But having this functionality right inside pip means you don’t have to turn to external tooling—or ugly hacks—to solve the problem. This may be an example of how third-party projects like uv (a superfast Python package installer) are inspiring native Python features where they make sense.

pip 26.2 is scheduled to be released by the end of July 2026.

Mourning Dan Williams

Thu, 23 Jul 2026 21:12:39 +0200

I have just received the shocking news that Dan Williams, a longtime, high-profile kernel developer, has passed away. I knew him primarily through his long service on the Linux Foundation Technical Advisory Board; he was always a strong, thoughtful, and intelligent presence. Dan will be deeply missed.

There is a support effort underway for Dan's family as they come to terms with this loss.

Black Forest Labs launches FLUX 3 capable of generating images and 20-second video with audio — but in limited release to start

Thu, 23 Jul 2026 20:48:22 +0200

Black Forest Labs (BFL) is expanding its FLUX family beyond image generation with today's launch of FLUX 3, a multimodal frontier model trained to understand and generate images, or combined audio/video clips up to 20 seconds from a single prompt — and to extend the same underlying architecture to robotic vision and actions.

The Freiburg, Germany-based AI lab says FLUX 3 is jointly trained across those modalities rather than assembling separate image, video and audio models behind a common interface.

That distinction is central to the company's pitch: BFL wants enterprises to think about creative generation, simulation, computer use and robotics as connected applications of a single capability it calls visual intelligence — models, in the company's words, "that can perceive, predict, and act across physical and digital environments." This release marks BFL's first public video generation model.

FLUX 3 will be offered through four product lines: FLUX 3 Video, FLUX 3 Image, FLUX 3 Action and the upcoming, open source FLUX 3 Dev. FLUX 3 Video, with optional native audio generation, and FLUX 3 Action are entering a gated "Early Access" program now, to which anyone can apply, but which BFL must approve.

There is presently no public access through BFL's application programming interface (API) or those of partners yet, but the company says FLUX 3 Image will roll out in the coming weeks, followed by general availability. The limited initial availability rollout echoes the release strategies of new models from other frontier labs in the U.S. lately, including Anthropic and OpenAI, though those were ostensibly for security concerns and due to government request.

What the company has not announced is pricing, production service-level commitments, evaluation methodology, sample sizes, rater counts or any image-model benchmarks at all. Enterprise buyers therefore cannot yet calculate total cost of ownership or independently reproduce the video comparisons.

Another big notable omission: FLUX 3 is not launching with downloadable weights at this time, nor an open source license. BFL says faster and open-weight versions will arrive later this year, and its technical blog names FLUX 3 Dev as "open-weight access to a multimodal backbone, for content creation (video, audio and image) and action prediction" — a considerably broader commitment than any previous FLUX Dev release, all of which covered images only.

But it arrives last in the sequence. Developers accustomed to receiving a locally deployable FLUX variant alongside — or soon after — a major model announcement will have to wait. That delay does not negate the company's commitment, but it is disappointing given the role open weights have played in FLUX's adoption thus far.

Flux 3 is rated higher than the competition, but missing pricing and benchmarking details may prevent rapid enterprise adoption

BFL has published several benchmark comparisons, but they're qualified as preliminary — with full benchmark results and methodology to be published later during broader general availability.

In early head-to-head preference testing on 10-second, 720p text-to-video clips with audio, the company says FLUX 3 was preferred over Luma Ray 3.2 in 93% of comparisons, Runway Gen-4.5 in 77%, Grok Imagine Video in 69%, Kling v3 Pro in 60%, Happy Horse v1 in 59%, Happy Horse 1.1 in 57%, and both Seedance 2.0 and Google's Gemini Omni Flash in 52%.

One caveat travels with every one of those figures, and it comes from BFL itself. The chart carrying the results is labeled a "preliminary evaluation of an early FLUX 3 candidate" — meaning the numbers describe a pre-release checkpoint rather than the model now entering early access. That cuts both ways: the shipping model may perform better, but nothing published today measures what customers will actually call.

Luma Ray 3.2 and Runway Gen-4.5, where FLUX 3 posted 93% and 77%, are the softest comparisons on the list — established products, but not the models currently setting the pace in independent video rankings. Those are real wins, and they are the ones least likely to change an enterprise shortlist.

Seedance 2.0, at 52%, is a statistical coin flip against a model most Western enterprises cannot currently procure. ByteDance indefinitely postponed Seedance 2.0's international rollout after Netflix, Warner Bros., Disney, Paramount and Sony sent legal threats over alleged systematic copyright infringement, and that suspension remains in place. Tying a frozen product is neither a strong claim nor a damaging one.

Gemini Omni Flash, also at 52%, matters much more. Omni is the closest large-platform analogue to what FLUX 3 is attempting — multimodal input, video and audio-aware creation, conversational editing — and by BFL's own measurement, the two are indistinguishable on 10-second text-to-video quality.

Google's advantage in that matchup is that Omni is generally available via Google's Gemini API for $0.10 per second of generated 720p video, or a 10-second clip for around.

One regional wrinkle matters for a German company's home market. Editing uploaded video is unavailable to Omni Flash users in the European Economic Area, Switzerland and the United Kingdom, though editing video the model itself generated is permitted. A European enterprise that wants to run its existing footage through a generative editing pass cannot currently do so on Omni Flash.

Here's a rough guide for enterprises considering which video models to rely upon:

Model	Max single-generation duration	Max resolution	Key constraints	Price per 10-second clip (720p)	Price per 10-second clip (1080p)	Price per 10-second clip (4K)
FLUX 3 Video	20 seconds	Not stated; evaluations run at 720p	Early access; no published SLA or pricing	Not announced	Not announced	Not announced
HappyHorse 1.1	15 seconds	1080p	No 4K; closed weights	Not published (v1.0 reseller rate is ~$1.82)	Not published (v1.0 reseller rate is ~$3.12)	n/a
Veo 3.1	Per-second billing	4K	Supports clip extension; preview	$4.00	$4.00	$6.00
Veo 3.1 Fast	Per-second billing	4K	Preview	$1.00	$1.20	$3.00
Veo 3.1 Lite	Per-second billing	1080p	No 4K, no clip extension; preview	$0.50	$0.80	n/a
Gemini Omni Flash	10 seconds (3s minimum)	720p at 24 FPS	Preview abd no EU access	$1.00	n/a	n/a

One architecture for media generation and physical action

FLUX 3 builds on Self-Flow, BFL's method for aligning multimodal understanding and generation within one architecture, publicized back in March 2026.

The company says it significantly scaled up compute and data to train across video, images and audio simultaneously, and that testing showed video generation and action prediction do not require separate foundations — the same architecture could be extended to action prediction without sacrificing what it learned from video.

"We place vision at the center of our approach because it is the most signal-rich medium of the physical world. Images convey structure, images and video teach spatial relationships, video teaches dynamics, and actions reveal causal relationships. But vision alone is not the complete picture," said Robin Rombach, co-founder and CEO of BFL, in a pre-release statement provided to VentureBeat. "True intelligence means perceiving the world: predicting how it will change, taking action, and learning from the results. Joint training within one unified architecture is what will get us there, because each training modality strengthens the others. Audio conveys timing, prosody, and physical events that elude vision. Language conveys goals, abstractions, and instructions that pixels cannot easily express."

He put the case more bluntly elsewhere in the announcement: "You can't cheat reality. A model that only learns images can only generate images. But the world is not made of still frames. It moves, sounds, changes, and responds."

BFL says FLUX 3 targets creative tooling, media, design, e-commerce and physical AI, supporting video generation with synchronized audio, precise image editing, product and material consistency across motion, multilingual generation and robotic action prediction. It is already being tested by Canva, Burda, Magnific (formerly Freepik), Krea and Picsart.

For creative software companies, the appeal is consolidation. A single foundation could potentially support storyboarding, image editing, product rendering, video variation and localization without repeatedly translating assets and instructions between disconnected models.

For robotics teams, the potential value is data efficiency. Models that already encode motion, object behavior and physical change may need less task-specific robot training than systems starting from raw demonstrations.

What FLUX 3 Video can actually do

The video tier is the most concretely specified part of the launch, and it settles a question that had been circulating as rumor: FLUX 3 generates clips of up to 20 seconds with audio in a single generation.

Every video output comes with native audio. For comparison, HappyHorse 1.0 tops out at 15 seconds of 1080p with synchronized audio — though BFL has not stated what resolution its 20-second clips run at, and its published evaluations were conducted at 720p. Still, a 20-second long clip from a single prompt is among the longest yet achieved, matching OpenAI's discontinued Sora model.

The capability list BFL published covers:

Text-to-video generation.
Image-to-video generation, either animating from a starting frame or using images as visual references.
Video-to-video generation from a reference clip, carrying elements such as a specific character into a new scene or context.
Generative video-audio continuation from existing video and audio input.
Keyframe-to-video generation for controlled transitions between defined moments.
Multilingual dialogue.
A broad range of visual styles and aspect ratios, from candid camcorder footage to animation and cinematics.
Typography generation and animated design.
Agentic chaining of individual clips into longer, multi-shot sequences.

That last item is the one enterprise video teams should look at hardest. BFL claims the capabilities combine to produce sequences lasting several minutes, with visual references keeping characters consistent across scenes. If that holds up under production conditions, it addresses the constraint that has kept generative video out of most commercial pipelines: not clip quality, but continuity across shots.

It is also the capability where competition is most direct. HappyHorse 1.1's headline upgrade is R2V, or Reference-to-Video, which accepts multiple character reference images to hold identity stable across generated footage — the same problem, approached at the input layer rather than through agentic clip chaining. Alibaba also claims zero-drift lip sync and has specifically targeted the artifacts that mark commercial AI video as synthetic, including facial oiliness and over-sharpening. Character consistency is where this category is being contested, and both companies know it.

BFL says FLUX 3 Video is already particularly strong at human facial expressions, associating sounds with physical events, and multilingual output. On the image side, the company says preliminary evaluations conducted during midtraining show significant improvement over earlier FLUX versions in complex prompt handling and text generation, including high-accuracy text in multiple languages. It published no image benchmarks or win rates.

FLUX-mimic tests whether video models can become robot models

BFL is applying its unified-architecture thesis through FLUX-mimic, a video-action model built on FLUX 3 and developed with Swiss firm Mimic Robotics, one of the first partners to receive early access.

The technical blog describes two distinct routes to action prediction: integrating native action prediction directly into FLUX 3, scaling up the initial Self-Flow work; and using the pretrained video backbone as a dynamics-aware foundation from which specialized action models can be finetuned with limited task-specific data. FLUX-mimic is the second route — the FLUX 3 backbone combined with mimic's robot-learning and production-deployment expertise in dexterous manipulation.

FLUX-mimic is designed for general-purpose robotic manipulation: helping robots understand a visual scene, predict the consequences of an action, and adapt to new tasks with far less task-specific data.

BFL and Mimic Robotics say that depending on task difficulty, the model can be finetuned for a specific manipulation task with as little as 30 minutes of robot data, where prior approaches have required 30 or more hours.

"The hardest part of robotics is data," said Elvis Nava, CTO of Mimic Robotics, in a statement provided to VentureBeat. "Every new task normally means hours of a robot repeating itself. Because FLUX-mimic is built on top of frontier video models that already understand how the physical world behaves, it picks up a new task in minutes, not days. This way, we can leapfrog the current state of the art in robot learning."

BFL argues that a model trained only on images cannot understand a world that "moves, sounds, changes, and responds," and that physical understanding is what produces convincing generated footage. Google makes a nearly identical claim for Gemini Omni.

Its developer documentation cites "world knowledge" that combines "an understanding of physics" with Gemini's grasp of history, science and cultural context. Its marketing is blunter still: "Most AI models just predict the next pixel to build a narrative or an image. Gemini Omni is different," the company posted in June, crediting the model with "an intuitive understanding of forces like gravity, kinetic energy, and fluid dynamics for more realistic movements that follow real-world logic."

The practical consequence for enterprise buyers is that world-model language is not a differentiator. Two of the three leading video systems now market physical understanding as their central advantage, and neither has published a benchmark that measures it.

There is no standard test for whether generated water behaves like water, whether a dropped object falls at a plausible rate, or whether a sound arrives when the impact does. Human preference ratings capture some of it indirectly. Nothing else on offer captures it at all.

Open weights helped make FLUX an industry standard

BFL officially launched in summer 2024 and gained a name for itself in the AI industry in the intervening two years for its commitment to open sourcing high-quality AI image models beloved by developers, creatives, and enterprises.

The company's founders, including Rombach, Andreas Blattmann and Patrick Esser, previously helped create VQGAN, latent diffusion and Stable Diffusion, the latter the open source technology that kicked off broad AI generation capabilities for the masses and currently used by many AI image generators and companies.

That reach translated into commercial distribution. FLUX models now power generative features inside Adobe Photoshop, Picsart and Nous Research's Hermes Agent, among other platforms, and the company cites film director Martin Scorsese among professional users.

Wired magazine described Black Forest Labs as a relatively small company that nevertheless became a leading competitor to Silicon Valley's largest AI labs, with FLUX models ranking near the top of image benchmarks and becoming some of the most downloaded text-to-image models on AI code sharing community Hugging Face. The company says it now runs a 100-person team across Freiburg and San Francisco.

FLUX.1 Dev, FLUX.1 Kontext Dev, FLUX.1 Fill Dev and related control models, released shortly after the firm's launch, gave researchers and creative-tool developers access to downloadable checkpoints, local inference and integrations with frameworks including Hugging Face Diffusers and ComfyUI. FLUX.1 Kontext Dev, for example, was released as an open-weight model for research and noncommercial use, with generated outputs permitted for commercial purposes under the applicable license.

The company continued that pattern with FLUX.2 Dev in late 2025, a 32-billion-parameter open-weight model combining generation and multi-reference editing. Black Forest Labs called it the strongest open-weight image generation and editing model available at launch and released weights, reference inference code and optimized implementations for consumer Nvidia GPUs.

FLUX 3 Dev raises the stakes on that evaluation. Previous Dev releases were image models. This one is described as a multimodal backbone spanning video, audio, image and action prediction — meaning a single license will govern whether a company can locally deploy a model that touches both content production and physical machinery. BFL hasn't yet shared information about its license, the parameter count, quantizations or hardware requirements.

The company frames open weights as an enterprise feature rather than a community gesture, arguing they enable secure, low-latency local deployment for applications like robotic control systems and let teams adapt FLUX 3 to their own data, products and workflows.

The financial backing behind FLUX 3 is worth noting alongside the technical claims. Black Forest Labs is valued at $3.25 billion and has raised more than $450 million from investors including a16z, AMP, Salesforce Ventures, Nvidia, General Catalyst, Adobe Ventures, Figma Ventures, Canva and Deutsche Telekom's T.Capital.

AMD raises the AI stakes with Helios, Venice and robotics

Thu, 23 Jul 2026 20:48:09 +0200

AMD’s MI455X targets the AI memory wall

AMD

The aforementioned lower-precision formats can also raise throughput and reduce memory use, but model developers still have to determine where they can be applied without unacceptable accuracy loss.

AMD’s Helios rack takes aim at Vera Rubin

Dave Altavilla

Kind of like cowbell, AMD Venice gives agentic AI more CPU

AMD

Pensando networking and ROCm software advance

Customer commitments underscore rack-scale confidence

AMD expands its robotics and embedded foundation

Execution for AMD is now the test

This article is published as part of the Foundry Expert Contributor Network.
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Mamdani’s Video Message About Netanyahu

Thu, 23 Jul 2026 20:33:38 +0200

Reader’s react to the New York mayor’s video criticizing the Israeli prime minister. Also: A.I. jobs for philosophers; a battle in the health care industry; fast walkers; pregnant bellies.

New Alpha Release: Tor Browser 16.0a9

Thu, 23 Jul 2026 20:25:02 +0200

Tor Browser 16.0a9 is now available from the Tor Browser download page and also from our distribution directory.

This version includes important security updates to Firefox.

⚠️ Reminder: The Tor Browser Alpha release-channel is for testing only. As such, Tor Browser Alpha is not intended for general use because it is more likely to include bugs affecting usability, security, and privacy.

Moreover, Tor Browser Alphas are now based on Firefox's betas. Please read more about this important change in the Future of Tor Browser Alpha blog post.

If you are an at-risk user, require strong anonymity, or just want a reliably-working browser, please stick with the stable release channel.

It's ESR transition season again!

Well actually, it has been ESR transition season throughout this entire release cycle! As described in the aforementioned Future of Tor Browser Alpha blog post, we have been incrementally rebasing our Alpha channel on Firefox betas since December of last year. As a result, we now stand before you with Tor Browser 16.0a9 which is based on Firefox ESR 153.

We will continue rebasing Tor Browser 17.0 Alpha branches on Firefox betas throughout the remainder of the Tor Browser 16.0 release cycle. However, new feature-work for now must be put on hold for a few reasons:

We must focus our attention on resolving our Bugzilla Audit issues to ensure the features we have inherited from upstream comply Tor Browser's threat model and to patch any changes which do not.
Feature work targeting 16.0 stable would need to be cherry-pick'd onto our 17.0 Alpha branches to ensure we don't lose any work. The more invasive a feature patch is, the harder it will be to port to newer versions. This would also be a potentially error-prone process and there is some risk we would lose patches along the way.
We need to finish stabilizing as soon as possible as we have hard external deadlines which cannot be moved: the end-of-life of Firefox ESR 140 on October 13th and the Google Play Minimum Target API Level requirement on November 1st

Challenges and Triumphs

💍 Sharing the Load

Rebasing the hundreds of Tor Browser patches onto newer versions of Firefox is a challenging task. It is like maintaining the structural stability of sand-castle at high-tide with the waves crashing all around you.

As such, it quickly become clear early in this new process that we would need to do something if we wanted to avoid burning out the few developers typically involved in this work. To mitigate this, we shared the knowledge internally and spread the work out across all eight members of the team. This way, each developer was only responsible for at most two or three rebases throughout the entire release cycle.

🎨 UI Code Churn

Over the past year, Firefox has developed and integrated two major changes to the UI in Firefox: a redesign of about:preferences in Firefox Desktop and a migration from Material 2 to Material 3 in Firefox Android.

Adapting to these types of changes to the frontend are typically rather time-consuming for us, as many (if not the majority) of our patches modify Firefox's UI in some way. For example, we have an entire preferences page on Tor Browser desktop dedicated to configuring how the browser connects to the Tor Network. On Android, we similarly have various additions to the menus, configuration options, and custom UI.

Whenever Mozilla modifies their design systems and Firefox's user interface, we necessarily have to adapt our own custom additions to match. Otherwise, our Tor Browser-specific UI elements would look completely out of place and potentially confuse users (as well as simply looking unprofessional). Therefore, each of these upstream changes requires collaboration with the Tor Project's UX team to update our features' designs and of course development time to implement.

In addition to the time-cost associated with the extra engineering and UX collaboration, very often our old patches simply do not apply cleanly due to the amount of code which has changed. For example, the about:preferences changes on Firefox Desktop are essentially a complete re-write which means we also have to completely re-write our own settings changes without regressing in functionality.

On the plus side, one benefit of our new processes is that we have been able to spread out this work over the entire release cycle. In the past way of doing things, we would have discovered all UX elements which needed to be fixed, updated our designs, and re-implemented in the course of a few months during the old ESR transition season. Under this new way of working, we have been able to incrementally fix things throughout the development cycle.

The benefits of working this way does not just apply to UX of course. It is much easier to find regressions across the entire stack when rebasing between one major Firefox version at a time instead of across 12 or 13. It is also much easier for developers to fix individual regressions one at a time compared to diagnosing, disentangling, and fixing multiple bugs concurrently (divide et impera!).

⚙️ Pending Google Target API Level Requirements

Every year, Google requires new Android app releases to target an updated minimum API level. This means, we would not be able to upload new versions of Tor Browser Stable past a certain date (usually August 1st with an extension to November 1st typically possible) without first updating the app to support the new minimum target API level. Fortunately, we inherit most of the required changes from Mozilla when rebasing to the next major ESR.

However, this requirement does impose a hard deadline for the absolute latest we can responsibly stabilize Tor Browser Alpha and promote it to Stable. We've been fortunate in the past few years to make the deadline with a few days to spare (October 28th for Tor Browser 15, October 22nd for Tor Browser 14, etc). Given how far ahead of the curve we are this year, we are hoping to release about a month earlier in September (fingers crossed!).

🤖 Android APKs too big

The Google Play Store has a strict size limit of about 100 megabytes for Android applications. New functionality added to Firefox Android over the past year means a larger application which results in new headaches for Tor Browser developers. This release cycle was no exception to this rule and we have had to get creative with our size reductions.

In the past, we have been able reduce our package size though various methods including:

Our most recent effort has been the most invasive yet! For some background, the Firefox application consists of (among other things): various shared libraries, the Firefox executable, a library known as 'xul' which contains most of Firefox's natively compiled functionality, and finally a file known as omni.ja. This omni.ja file is a zip archive which contains the JavaScript, HTML, images, and other assets used in Firefox.

This time around, to reduce the size of our Android package we havechanged how this archive is compressed. We modified the Firefox build system to compress this archive with xz and we modified Firefox itself to decompress this archive at runtime. This work did require a few iterations to get right. In the end, we got back about 3 megabytes with these changes and got us once again under Google's imposed size budget.

📉 Even Less Telemetry

Over the years, we have worked to incrementally remove dependencies from Tor Browser Android as part of the aforementioned size reduction work. We of course inherit most of these dependencies from Firefox Android and unfortunately some of them can be labeled as 'trackers'. While we do disable telemetry by default at runtime, the code which implements it remains in the codebase.

We're happy to report that as of Tor Browser 16.0a8, are down to only 1 'tracker' library in the Tor Browser Android codebase: Mozilla Telemetry. Again, this telemetry is disabled at runtime, but this is one more unused dependency which we can hopefully remove in the future (and maybe get some more bytes back!).

Current Status

We have:

incrementally rebased Tor Browser and Tor Browser for Android to Firefox ESR 153 from Firefox ESR 140
updated the build systems with the latest dependencies and fixed a few reproducibility issues
triaged most of the upstream changes from the past year and flagged over 250 issues for further review (triaging of Firefox 153 is in progress)
resolved about half of these triaged issues

For the remainder of this release cycle, we will be focusing on auditing these issues and fixing bugs until the 16.0 alpha series is ready to become Tor Browser Stable 16.0. We are optimistically targeting a September release, which would put us one month ahead of schedule compared to last year.

Known Issues

🦊 Firefox Branding

In some places in the browser there may be Firefox branding (e.g. logos, cute little foxes, etc) instead of Tor Browser branding. We're currently tracking one known instance in tor-browser#44998. If you discover any other instances lurking about, please open an issue!

🌐 All websites marked 'insecure' on Tor Browser Android

Currently, the identity block in the URL bar on Tor Browser Android will always report insecure (e.g. a shield icon with a slash through it). For now, you can tap this icon and verify the certificate manually. This issue is being tracked in tor-browser#45115

Send us your feedback

Now is a great time to become an alpha tester! If you find a bug or have a suggestion for how we could improve this release, please let us know.

Full changelog

The full changelog since Tor Browser 16.0a8 is:

All Platforms
- Updated NoScript to 13.6.30.90201984
- Bug tor-browser#43819: Show custom security level on android
- Bug tor-browser#44748: Revert Funding the Commons Implementations
- Bug tor-browser#44811: Remove the lock on pdfjs.disable.
- Bug tor-browser#45101: Rebase Tor Browser onto 153.0esr
- Bug tor-browser#45131: Security level is using an unsafe getBoolPref
- Bug tor-browser-build#41831: Update libevent to 2.1.13
Windows + macOS + Linux
- Updated Firefox to 153.0esr
- Bug tor-browser#44439: Remove translate action from urlbar
- Bug tor-browser#44883: Remove urlbar quick action for labs
- Bug tor-browser#45029: Convert connection status settings to new design and config approach
- Bug tor-browser#45055: Rename --color-gray-05 to --color-gray-0
- Bug tor-browser#45081: Use the new "Acorn" icons on desktop
- Bug tor-browser#45110: Disable the settings redesign until ready for us
- Bug tor-browser#45112: Missing CSS border tokens in 153
- Bug tor-browser#45132: nsAppFileLocationProvider.cpp: use of undeclared identifier 'XRE_EXECUTABLE_FILE'
- Bug tor-browser-build#41800: Create a script that adapts the Tor Browser manual HTMLs to work in Tor Browser
macOS
- Bug tor-browser#45108: Artifact generation fails due to missing .DS_Store in the branding directories
Android
- Updated GeckoView to 153.0esr
- Bug tor-browser#43820: Use SecurityLevel integration on android
- Bug tor-browser#44157: Remove secret setting toggle for Tab Management Redesign
- Bug tor-browser#45045: Remove moz asset in Downloads screen
- Bug tor-browser#45103: Disable broken "tab management"
- Bug tor-browser#45109: No value passed for parameter 'jsEnabled'
- Bug tor-browser#45118: Audit and disable Mozilla VPN promo
- Bug tor-browser#45130: Clean up TorHomePage padding
Build System
- All Platforms
  - Bug tor-browser-build#41838: Update personal_access_tokens URL in tools/fetch_changelogs.py
- Windows + Linux + Android
  - Updated Go to 1.26.5
- Windows
  - Bug tor-browser-build#41819: Fix windows-rs URL in projects/firefox/config

In a First, Apple Maps Navigation To Be Embedded In Ford UEV Pickups

Thu, 23 Jul 2026 20:13:19 +0200

Ford will become the first automaker to embed Apple Maps directly into its vehicles, starting with an all-electric midsize pickup built on its new Universal Electric Vehicle platform. "The pickup -- with Ford testing preproduction models on roads now -- is expected to start at $30,000 when it comes to market in 2027," reports the Detroit Free Press. "Ford has said several other EVs off that platform will follow, including a small all-electric SUV." From the report: Ford CEO Jim Farley said the new EVs will redefine advanced technology as simple, useful, and at a price point that is attainable for most people. "We're proud to embed Apple Maps' navigation and mapping technology directly into our Universal Electric Vehicle Platform, giving customers the ultimate navigation experience alongside our Ford app, a full suite of software, and next-generation BlueCruise, all enabled by a new zonal architecture," Farley said in a statement. "Apple Maps has delivered a world-class product, and we're honored to be among the first to embed it directly into a vehicle, helping define intuitive, capable driving." In a joint statement, Apple and Ford said the integration will deliver a "beautiful and easy-to-use navigation experience powered by Apple Maps directly to the vehicle's displays. Road-level Maps information will also enable Ford's Latitude AI team to build a seamless hands-free driving experience." Ford said it will use that road information to develop its next-generation BlueCruise hands-free highway driving capability. Also, by leveraging Apple's new MapKit for Automotive SDK, Ford's UEV Platform will offer drivers turn-by-turn directions using natural language, real-time traffic information, intuitive search and routing options for the best route. The system will give drivers EV routing functionality to help drivers with the warming and cooling process of the vehicle's battery before driving or fast-charging.

Read more of this story at Slashdot.

Why AI-Generated Code Fails Security Reviews 45% of the Time

Thu, 23 Jul 2026 19:39:34 +0200

My friend is a junior developer, roughly eight months into her first real job. She said the most enjoyable thing about her week was watching Copilot complete all of her tasks before she would even type the closing bracket. In…

The post Why AI-Generated Code Fails Security Reviews 45% of the Time appeared first on IT Security News.

Schlechter Empfang trotz 5G-Tarif? Das Problem steckt oft in einer Einstellung, die fast niemand ändert

Thu, 23 Jul 2026 19:29:45 +0200

Schlechter Handyempfang kann im Alltag richtig nerven. Gespräche brechen ab, Webseiten laden ewig oder das Smartphone zeigt plötzlich nur noch „Kein Netz“ an. Mit den richtigen Einstellungen und ein paar einfachen Tricks lässt sich der Empfang aber oft deutlich verbessern.

An AI now judges every move Rubrik's agents make, its AI chief said at VB Transform 2026 — but no one's measured if the judge is right

Thu, 23 Jul 2026 19:19:49 +0200

At a CISO roundtable organized by Anthropic's chief information security officer, Dev Rishi asked a simple question: Did everyone in the room have their AI governance and security policies written down? Every hand went up — about 14 people, by his count. His follow-up, about how anyone actually enforces those policies in practice, got a different response. "And everybody chuckled," Rishi, the GM of AI at Rubrik, recalled at VB Transform 2026 fireside chat in Menlo Park. "It was like the dirty secret in the room that everyone has these policies, but no way to actually make them real."

“Our founder and CTO has actually been really pushing to enable our agents in YOLO mode,” Rishi told the audience. That admission comes from a publicly traded data security firm whose business is backing up what he called the most important data in the world.

YOLO mode strips the permission prompt out of agent workflows and lets the agent act on its own. In Rubrik's version, a second AI judges every action in real time against policy in place of a human clicking approve. Rubrik is running the experiment on itself first. Rishi treats autonomy as a settled capability question and an open judgment question. "If you ask the agent to act autonomously, it will," he said. "It's a question that you have internally. Should it?"

Rubrik earned that question the hard way. When Claude Code and Cowork pilots rolled out, the company required every command to run in ask mode so the employee issuing it carried the liability, and the developer pushback filled a single Slack thread 120 messages deep.

"The developers basically are pushing back, and they're like, this is like the iTunes service agreement. I'm just hitting check, check, check, check, check, check, check," Rishi said. "There's no way that I can actually read through this. And it becomes security theater." Roughly 80% of respondents are in the same bind, Rishi said, citing Rubrik Zero Labs research that found monitoring and approving agent actions takes more time than the agents save. The State of the Agent, the April report behind that figure, surveyed more than 1,600 IT and security leaders.

SAGE is the reason Rubrik trusts the bet. Short for Semantic AI Governance Engine, SAGE is the arbitration layer inside Rubrik Agent Cloud that watches every action an agent takes and reads the semantic intent behind it, then rules the action in or out against policies written in natural language. "We took what people said was human in the loop, a good idea, and we replaced it with AI in the loop," Rishi said, describing the pitch to security chiefs he characterized as skittish about non-deterministic systems.

Security approval, not cost, blocks AI ROI

Rishi’s path to Rubrik ran through Predibase, the generative AI infrastructure startup he co-founded and ran as CEO until Rubrik agreed to acquire it in June 2025. Before that, he led ML product at Google on the team that became Vertex AI, served as Kaggle's first product manager as it grew from about one million to ten million users, and holds bachelor's and master's degrees in computer science from Harvard.

Over roughly his first three and a half months at Rubrik, Rishi set up 200 customer conversations with IT and security leaders across a customer base that looks like the Global 2000, asking open-ended questions about cost, latency, performance, and orchestration. "Pretty consistently, what I heard through all of those conversations was that all of those are pretty secondary," he said. "The main challenge is actually, how do I get this approved from a security and risk standpoint? I'm concerned about all the different things that could go wrong. Actually, I felt like that was one of the biggest things constraining ROI."

VentureBeat Pulse research presented on the Transform stage earlier in the day confirms the gap Rishi kept hearing. Two-thirds of enterprises, 66%, already allow or are actively building toward production deployment with zero human review, yet only 5% fully trust the automated evaluations that would make that decision.

One AI reading what the rulebook can't

Rubrik's own policies exposed why written rules fail as enforcement. One internal rule states that agents should respect Rubrik's customer data use policy, which sounds enforceable until someone tries. "Rubrik's customer data use policy is like a three-page document of legal text," Rishi said. "I have no idea how to write that in there as a rule." Asked on stage how a team of AI infrastructure people took on a problem that security engineers own, Rishi answered, "with a lot of naivety and innocence, honestly." His team bet that models good at understanding language could police other models, and SAGE became the answer.

The case for putting a model in the judgment seat comes down to precision. A rule like "agents should not be able to edit revenue fields in Salesforce" fails in conventional tooling because Salesforce does not delineate which fields count as revenue, Rishi explained, so administrators fall back on approving every Salesforce action by hand. SAGE reads the intent instead and acts as a judge, carrying organizational context, which can tell a benign lookup from the edit the policy prohibits.

Keeping the judge small is what makes the economics work. SAGE runs on a small language model that Rishi said operates at an order of magnitude lower cost and latency than a frontier LLM. "If I told you, don't worry, you're gonna be secure and governed, but I'm gonna double your cost and latency, you would tell me to get out of the room," Rishi said.

When Rishi asked who in the audience had worried about token consumption over the past year, half the hands went up. "And I guess the other half is probably just too lazy to raise their hand," he said.

SAGE is an aggregation of judges based on parameter-efficient fine-tuning that Rubrik uses to take on task-specific variants of a base model with shared organizational context. One judge watches for tool-use hallucinations while another suppresses PII before it can leave, each running as its own enforceable policy. Security and GRC teams have started writing financial rules into the same layer, including one internal policy barring AI spend on personal projects.

The lethal trifecta

Asked which attacks worry him most, Rishi pointed at the lethal trifecta, the term security researcher Simon Willison coined in June 2025 for an agent that holds private data while taking in content nobody vetted, with a channel to send what it finds to the outside world. The danger, according to Rishi, is what happens when individually legitimate permissions stack. An agent granted Salesforce access and email access on an employee's credentials has done nothing wrong yet, with yet being the operative word. "A very simple example is that an agent can start pulling data from Salesforce and then decide to accidentally leak and exfiltrate that out via an email," he told the audience. A financial services company he met the morning of the session made the point for him, telling Rishi that none of the individual permissions are bad on their own and the agent needs every one of them to do its job. "It should have permission to each of those systems, but it's the combination that ends up becoming really destructive," Rishi said.

Traditional identity and access management never priced in that combination because it relied on the judgment of the employee holding the credentials, Rishi argued, and agents supply none. "I can tell you the number of times Claude Code has tried to leak some of our sensitive source code to a public GitHub repository is incredibly high," he said. Cutting agents off from public resources entirely would defeat their purpose, which returns the problem to adjudicating intent in context rather than revoking access.

A separate VentureBeat June Pulse survey of 107 qualified enterprise respondents maps the blast radius of exactly this pattern. On the Transform stage that morning, VentureBeat research reported that 69% of companies run credential sharing somewhere in their agent fleet. Companies with shared credentials anywhere got hit more often, reporting a security incident or near-miss at a 63.5% rate (47 of 74), against 40.9% (9 of 22) where every agent carries its own scoped identity.

The attacks no single turn reveals

Rubrik Agent Cloud reached general availability in February, though not everything Rishi described ships in it yet. Backtesting is just starting to roll out. The feature replays an organization's historical agent actions and tool calls against a new policy, showing where the policy would have stepped in and where an action would have sailed through uncaught, with policy edits applied in real time. Rishi called that archive one of the most valuable data troves an enterprise holds.

Real-time detection and blocking turn out to be the entry point rather than the whole product. Some attacks never trip a single-action rule. "No individual turn of the conversation was problematic, but if you took the session as a full trace, that ended up being problematic," Rishi said. Agent Cloud runs batch analysis across entire session traces every hour or every day and surfaces what Rubrik calls insights, the problems no individual guardrail caught. The same Zero Labs report found that 88% say they lack the ability to roll back agent actions without system disruption, a recovery gap that sits squarely in Rubrik's original line of business.

A skeptical CISO will ask the question the fireside did not answer. SAGE is a non-deterministic model policing other non-deterministic models, and Rishi offered no false positive or false negative rate for the judge itself. The closest thing the architecture gives to an answer is auditability, since backtesting and the batch insights both leave a human-reviewable trail of each call SAGE made and whatever got past it. Who watches the watcher, for now, is a trail of receipts rather than a benchmark. Until that benchmark exists, AI in the loop stays an operational wager rather than a quantified control.

Three questions fall out of the session for security teams. How many of the guardrails now in production depend on a human clicking approve, and what happens to that workload as agent count grows? Does anything in the stack enforce semantic intent, or is it all allow and deny lists? And can the team backtest agent behavior against a new policy, then unwind a multi-turn session without taking systems down?

Rishi's timing has a market behind it. In the same VentureBeat research, 82% of enterprises still name their primary AI provider's built-in guardrails and cloud controls as their main agent security layer, and 59% plan to adopt, add, or replace agent security tooling within the next 12 months. Only 12% include an agent-identity product in what they are considering, even with credential sharing still the norm. Every CISO at that Anthropic roundtable had a policy document and no enforcement mechanism, and Rubrik built a product for the space between the two. YOLO mode is the bet that an AI watching other AIs can finally make the policies real.

Agentic orchestration: Enterprise AI organizations have a deployment problem, not a platform problem — and most are calling chatbots agents

Thu, 23 Jul 2026 19:19:45 +0200

Across 101 enterprises, agent orchestration is consolidating onto model-provider platforms — Anthropic’s Claude leads by a wide margin — chosen for the gravity of the underlying model and judged on reliable multi-step execution. But the ambition runs well ahead of the reality: most deployed “agents” are still chatbot wrappers, the control plane enterprises expect is deliberately hybrid to avoid lock-in, and real-time fiscal control over token burn remains the exception.

This wave of VentureBeat Pulse Research examines enterprise agent orchestration: which platforms enterprises run on, what drives the choice, what they optimize for, how they expect agent control to be structured, and — most revealingly — how orchestrated their deployed “agents” actually are and how tightly they control the cost of running them.

The central finding is a gap between orchestration ambition and orchestration reality. Enterprises are consolidating fast onto the major model platforms: Anthropic’s Claude is the primary platform for 40%, more than double any rival, followed by Microsoft (18%) and OpenAI (13%). The choice is driven by “model gravity” — native alignment with a state-of-the-art base model (21%) — and success is judged by reliable, multi-step execution (task completion reliability 32%, multi-step workflow management 28%). Yet asked to assess their portfolios honestly, 71% say a quarter or fewer of their deployed “agents” are true multi-step orchestrated workflows rather than single-prompt chatbot wrappers, and only 10% have crossed the halfway mark. The orchestration layer is being built well ahead of the orchestrated portfolio it is meant to run.

That gap shapes the architecture enterprises are putting in place. By the end of 2026 a clear majority (51%) expect a hybrid control plane — provider-native plus external orchestration — and only 6% expect to hand control to a provider-managed service, because vendor lock-in (35%) is the risk they fear most if control lives inside a model provider. Investment follows the build-out: agent workflow tooling leads the spend (34%), with security and permissions enforcement (25%) behind. And fiscal control lags throughout — more than a quarter (27%) have no real-time way to stop a runaway agent before the bill arrives.

Methodology

VentureBeat fielded this survey as part of its ongoing Pulse Research series, this instrument focused on enterprise agent orchestration. Responses are filtered to organizations with 100 or more employees (n=101), drawn from a single June 2026 wave; because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends.

By organization size the sample is spread evenly across the enterprise bands: 100–499 employees, 2,500–9,999, and 50,000+ (21% each), with 10,000–49,999 and 500–2,499 (19% each). By role it is senior and buyer-credible: product and program managers (15%), CIO/CTO/CISO (13%), consultants and advisors (13%), and a spread of data, AI, and engineering directors and VPs, with an “Other” function at 18%. On purchasing, 81% are recommenders, influencers, or final decision-makers for AI solutions (66% recommender/influencer, 15% final decision-maker). Technology/Software is the largest industry at 44%, followed by Financial Services (17%) and Healthcare/Life Sciences (8%).

At 101 respondents the sample is robust enough to read directionally with reasonable confidence, though it remains self-selected and is not a probability sample.

Finding 1: Orchestration runs on model-provider platforms

Anthropic’s Claude leads; open frameworks are marginal

We asked which agent orchestration platform enterprises primarily use today. The answer concentrates on the major model providers — and on one in particular.

A note on reading these shares. As described in the methodology section, the respondents are self-selected, and this question asked them for a single primary platform — so the figures measure which platform leads each enterprise's deployment, within a self-selected audience of AI-active technical decision-makers. A sample built this way can diverge substantially from spend-weighted market measures, and each VB Pulse survey draws its own sample with its own company-size mix, so vendor figures should not be compared across our surveys either. Read these shares as a portrait of where this cohort has placed its primary orchestration bet today, rather than as market share.

The model platforms dominate. Anthropic, Microsoft, OpenAI, Google, and Amazon together account for roughly 80% of deployments (81 of 101), while the open frameworks (LangChain/LangGraph) and custom in-house builds that anchor engineering discussion sit in single digits. Anthropic’s lead — 40%, more than double the next platform — mirrors the “model gravity” selection logic in Finding 2: enterprises are choosing the orchestration layer that comes with the model they want to build on. As with the security vendors in the prior agent-security wave, the tools that define the category in technical circles are not yet where enterprise deployment concentrates. A small 3% are not orchestrating at all.

Respondents rate the platforms they run at 3.94 out of 5 overall (109 answered), with “value for money” specifically at 3.94 and “ease of implementation” the weakest score, at 3.85 — placing orchestration near the bottom of our five-tracker satisfaction range, ahead of only evaluation tooling. A rating just under 4 out of 5, from users of whom 96% plan to change their orchestration approach within the year, reads as provisional acceptance: the platforms work well enough to run today, and not well enough to stop the search for something better. The ratings sit alongside near-universal intent to change; this is a layer enterprises tolerate more than they love.

Finding 2: Model gravity drives platform selection

The base model, not the tooling, decides the platform

We asked what most influenced the orchestration platform choice. The single largest factor is the pull of the underlying model — though flexibility and ease of development follow close behind.

Model gravity leading is the selection-side explanation for Anthropic’s platform lead: enterprises pick the orchestration environment closest to the frontier model they have standardized on. But the next tier complicates the picture — flexibility across models and tools (17%) and ease of development (17%) say enterprises also want to avoid being trapped by that choice, foreshadowing the lock-in fear in Finding 6. Security and permissions (14%) and total cost of ownership (11%) round out a pragmatic buying logic. Performance (latency/memory) sits last at 4%, a reminder that at this stage of adoption the binding constraints are model fit and optionality, not raw speed.

Finding 3: The job is reliable multi-step execution

Enterprises just orchestration by whether it completes the work

We asked what enterprises optimize for — their primary success metric for orchestration. Reliability and multi-step workflow management dominate; developer- and user-facing metrics trail.

Task completion reliability (32%) and multi-step workflow management (28%) together account for 59% of responses (60 of 101): orchestration succeeds, in the enterprise view, when it reliably carries a task through multiple steps to completion. Developer productivity (17%) matters but is secondary — the inverse of its prominence in framework discussion — and end-user experience (9%) is a minor concern, consistent with orchestration being an internal execution problem rather than a UX one. This reliability-first standard is exactly what makes the Chatbot Trap finding so pointed: enterprises define success as dependable multi-step execution, yet most of their deployed “agents” do not yet do multi-step work at all.

The trap is not evenly distributed. Splitting the sample by organization size, 77% of smaller enterprises say a quarter or fewer of their agents do true multi-step work, against 62% of larger ones. Larger enterprises are meaningfully further into genuine multi-step deployment; the chatbot trap is, directionally, a mid-market condition.

Finding 4: Consolidate, productionize, and build in-house

Three strategic moves are nearly tied for the year ahead

We asked what major change enterprises anticipate in their orchestration strategy over the next 12 months. Three moves cluster at the top, almost evenly split.

The top three — building in-house control (25%), standardizing on one framework (24%), and moving agents from sandbox to production (23%) — are statistically indistinguishable and tell a single story: enterprises are moving from experimentation to operational consolidation. They want fewer frameworks, more production exposure, and more ownership of the control layer; only 4% expect no change. The appetite for custom in-house control planes is notable alongside the platform concentration in Finding 1 — enterprises are standardizing on model-provider platforms while simultaneously planning to wrap them in control logic they own, the hybrid posture that Finding 6 makes explicit.

Finding 5: Nearly seven in 10 plan to switch — and the biggest group of movers has no shortlist

The strategic change enterprises anticipate (previous finding) comes with vendor motion attached. Asked whether they plan to adopt a new, additional, or replacement agent orchestration platform in the next twelve months, more respondents are moving here than in any other layer we track.

Asked which platforms they are considering, the most common answer among those in motion is none yet: 29% of all respondents are evaluating without a shortlist, the largest single response after "not considering a change." Among named candidates, OpenAI leads at 16%, followed by LangChain/LangGraph at 12% and Anthropic at 7% — and notably, the independent frameworks draw roughly double their current usage footprint in forward consideration, the same pattern our security tracker found for specialist vendors. Read with this report's concentration and lock-in findings, the picture completes itself: the major model-platform providers hold roughly four-fifths of today's primary usage, vendor lock-in has become the leading fear, 96% anticipate a strategic change — and now the purchase intent to act on all of it, with the largest bloc of buyers still undecided. The most concentrated layer of the agentic stack is also, as of June, the least settled.

Finding 6: Investment flows to workflow tooling

Tooling and permissions lead the spend; monitoring trails

We asked which orchestration-related investment will grow most next year. Agent workflow tooling leads, with security and permissions enforcement behind.

Workflow tooling leading (34%) is the budget-side expression of the reliability-and-multi-step priority in Finding 3: the money is going to the machinery that strings steps together dependably. Security and permissions enforcement (25%) and scaling infrastructure (20%) follow — the investments required to take agents from sandbox into production, the strategic move in Finding 4. Monitoring and debugging draws a smaller 11%, with another 11% reporting flat budgets. The weight on tooling, permissions, and scaling over pure observability signals that enterprises are spending to build and harden orchestration, not merely to watch it run.

Finding 7: The control plane will be hybrid — and lock-in is why

Enterprises expect to split control between providers and their own layer

We asked where enterprises expect the primary control plane for agents to live by the end of 2026, and what worries them most if that control sits inside a model-provider platform. A clear majority expect a hybrid model — and vendor lock-in is the reason.

Hybrid control is the dominant expectation by a wide margin (51%), and only 6% expect to hand control to a provider-managed service outright. Read together, the hybrid, custom, and externally-abstracted options — every architecture that keeps control at least partly outside the provider — sum to 88% (89 of 101). The reason surfaces directly when we asked about the risk of provider-resident control: vendor lock-in leads at 35% (35 of 101), ahead of security and permissioning limitations (28%) and inflexibility across models and tools (21%). The pattern echoes the prior wave’s “don’t trust the model to police itself” posture — here, enterprises will build on a provider’s platform but decline to be governed entirely by it. The hybrid control plane is the architectural hedge against the lock-in they most fear.

The June figure asserting a preference for a hybrid control plane marks movement from earlier. In the April–May survey (n=145), only 34% expected a hybrid control plane, and a greater number (12%) expected to hand control fully to a provider-managed service. These two snapshots don’t yet measure a confirmed longitudinal trend — but the direction of the conversation is unambiguous: toward keeping control.

Lock-in is also a new arrival as a top concern. In the April–May wave, the leading concern was security and permissioning limitations (32%), with lock-in second at 24%; by June the two had traded places. The worry about provider platforms appears to be maturing from whether they can be secured to whether they can be replaced.

Finding 8: The chatbot trap — most “agents” aren’t agents yet

Enterprises admit most deployments are still chatbot wrappers

We asked enterprises to assess their portfolios honestly: what share of their deployed “agents” are true multi-step orchestrated workflows versus simple single-prompt chatbot wrappers. The answer is the defining finding of this wave.

This is the gap at the center of the report. Combining the bottom two bands, 71% of enterprises (72 of 101) say a quarter or fewer of their deployed “agents” are genuinely orchestrated — and just 10% (10 of 101) have crossed the halfway mark. The ambition documented in the earlier findings — model-provider platforms, reliability-first success metrics, production rollouts, a deliberate control architecture — runs well ahead of the deployed reality, which remains overwhelmingly single-prompt assistants dressed as agents. This is less a contradiction than a roadmap: the platforms, budgets, and strategies are being put in place precisely because the orchestrated portfolio is still so thin. The open question for later waves is how fast the reality closes on the ambition.

Finding 9: Fiscal control is still reactive

Only a minority can stop a runaway agent before the bill arrives

Finally, we asked how enterprises enforce fiscal control over agent token consumption — the risk that an autonomous loop exhausts a budget before anyone intervenes. Most rely on native caps or after-the-fact monitoring; real-time programmatic control is the exception.

More than a quarter of enterprises (27%) admit they have no real-time, programmatic way to stop an agent before a budget-breaking bill arrives — they learn of it from the logs afterward. Another 32% lean entirely on the native caps and throttles built into their primary platform, a control only as good as the provider’s tooling and one that ties back to the lock-in concern of Finding 6. The enterprises building custom gateways (23%) or exploiting cross-model routing to arbitrage cost (19%) are the ones treating token burn as an engineering problem to be controlled deterministically. As with orchestration maturity, fiscal control is an area where the operational reality lags the ambition: agents are moving toward production faster than the cost-control plane around them is being built.

It’s worth noting, a split appears according to company size: roughly one in three enterprises under 2,500 employees (34%) exercises only reactive control of agent spend, against 20% of larger enterprises — directional figures, but consistent with the chatbot-trap split. The mid-market is running the least mature agents on the least instrumented budgets.

The bottom line: The layer is real; most of the agents aren't yet

Organizations with 100 or more employees describe an orchestration strategy that is consolidating quickly and maturing slowly. They are standardizing — for now — on model-provider platforms, which collectively hold roughly four-fifths of primary usage, chosen for the gravity of the underlying model, and they judge success by reliable multi-step execution. Investment is flowing to workflow tooling and permissions, the strategy is to consolidate frameworks and push agents into production, and the control plane they expect is deliberately hybrid, because vendor lock-in is the risk they fear most. But the standardization is provisional: 68% plan to adopt a new, additional, or replacement orchestration platform within twelve months — the highest switching intent of any layer we track — and the largest group of those movers has not yet shortlisted a candidate. Today's concentration describes where enterprises are, and visibly does not describe where they intend to stay.

But the honest self-assessment punctures the ambition. Seventy-one percent say a quarter or fewer of their deployed "agents" are truly orchestrated, only 10% are past the halfway mark, and more than a quarter cannot stop a runaway agent in real time. The orchestration layer — the platforms, the budgets, the control architecture — is being built ahead of the orchestrated portfolio it is meant to run. At 101 respondents in a single June wave this reads as a clear directional signal rather than a precise measurement: enterprises have decided how they want to orchestrate agents well before most of their agents are doing anything an orchestration layer is for. The questions for subsequent waves are whether the deployed reality closes the gap on the ambition — and, with nearly seven in ten buyers in motion and most of them undecided, which platforms the settled stack finally lands on.

Based on survey responses from 101 qualified enterprise respondents (100+ employees), drawn from a single June 2026 wave. Because this is one wave rather than a pooled multi-month sample, results read directionally rather than as a confirmed trend. Respondents include product and program managers, CIOs, CTOs and CISOs, consultants and advisors, and directors and VPs of data, AI, and engineering, across Technology/Software, Financial Services, Healthcare, and other sectors.

The agent evaluation gap: Enterprise AI organizations have a reality-alignment problem, not a coverage problem — and most are shipping to production anyway

Thu, 23 Jul 2026 19:19:44 +0200

Across 157 enterprises, organizations are granting AI agents more autonomy while trusting the evaluations meant to gate that autonomy less. Half have already shipped an agent that passed their internal evaluations and then failed a customer in production; only one in twenty fully trusts automated evaluation today; and the most-cited weakness is that evaluations do not align with real-world outcomes. Yet two-thirds already allow, or are actively engineering toward, deploying agent changes to production on automated evaluation alone — with no human in the loop. The result is an evaluation gap — the distance between how much autonomy enterprises are handing their agents and how far they trust the tests that are supposed to catch the failures.

This wave of VentureBeat Pulse Research examines how technical leaders measure agent performance: which reliability and evaluation platforms they use, how they select and trust them, what breaks in production, and how far they are willing to let agents run without a human in the loop.

The central finding is an evaluation gap — the distance between the autonomy enterprises are granting their agents and the trust they place in the evaluations meant to govern it. Half of organizations (50%) have, in the past year, deployed an agent or LLM feature that passed their internal evaluations and then caused a customer-facing failure, and a quarter have seen it happen more than once. Trust in the tests themselves is thin: only 5% say they fully trust automated evaluation today, and the single most-cited limitation is that evaluations align poorly with real-world outcomes (29%). Enterprises are discovering that a passing eval is not the same as a working agent.

What makes the gap consequential is the direction of travel. Two-thirds of organizations (66%) already permit fully automated, zero-human-in-the-loop deployment for low-risk agents (34%) or are actively engineering their pipelines to allow it within twelve months (33%). At the same time, the evaluation stack that would have to earn that trust is fragmented and immature: the most common primary tools are the model providers’ native evals, tied with having no dedicated tooling at all (17% each); and only about a quarter of enterprises run real-time quality checks on live production traffic. The autonomy is arriving faster than the assurance.

Methodology

VentureBeat fielded this survey as part of its ongoing Pulse Research series, this survey — the Agentic Reliability & Evals tracker — focused on how technical leaders evaluate agent performance and reliability. Responses are filtered to organizations with 100 or more employees (n=157), drawn from a single survey in June 2026; because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends. Where questions were multiple-select, those shares can sum to more than 100%.

By role the sample is senior and buyer-credible: 38% are final decision-makers for AI purchases and another 34% recommenders or influencers. Product and program managers (15%), consultants and advisors (10%), directors of engineering/IT (8%), and CIOs/CTOs/CISOs (8%) lead the named titles, alongside a large “Other” function (37%). By organization size the sample is mid-market-weighted: 100–499 (37%) and 500–2,499 (27%) employees lead, with 2,500–9,999 (20%), 10,000–49,999 (10%), and 50,000+ (6%) above them. Technology/Software is the largest industry at 23%, followed by Retail/Consumer (15%), Healthcare/Life Sciences (12%), and Manufacturing (10%).

At 157 respondents the sample is large enough to read directionally but should be treated as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It skews toward the mid-market, so it is best read as the view from organizations actively standing up agent evaluation practices rather than from the largest operators.

Note: This survey was rebuilt for the June wave from the earlier “LLM observability and evaluations” survey; because the questions and sample differ, no comparisons are made to the April–May data.

Finding 1: A passing eval is not a working agent

Half have shipped an agent that passed evals, then failed a customer

We asked whether, in the past 12 months, organizations had deployed an agent or LLM feature that passed their internal evaluations but then caused a customer-facing failure. Half of those that run evaluations had.

This is the report’s defining number. Half of organizations (50%) have shipped an AI feature that cleared their internal evaluations and then failed in front of a customer — an incorrect output, a broken workflow, or a quality incident — and a quarter have seen it happen more than once. Only 36% report no such failure, and the remainder either run no pre-deployment evaluations (8%) or don’t track the root cause closely enough to know (6%). The failure is precise and expensive: the evaluation said the agent was ready, and it was not. Everything that follows — how enterprises trust their evals, what they monitor, and how much autonomy they grant — is shaped by this experience.

Finding 2: Almost no one fully trusts automated evaluation

The top complaint: Evals don't match real-world outcomes

We asked which limitation most reduces trust in automated agent evaluations today. Only a sliver of enterprises had no complaint at all.

Trust in automated evaluation is scarce, and specific. Only 5% of organizations say they fully trust automated evaluation as it stands — meaning 95% name a limitation that holds them back. The most common, at 29%, is the one that most directly explains Finding 1: evaluations align poorly with real-world outcomes, passing agents that later fail. Bias or inconsistency (21%) and a lack of explainability (18%) follow — enterprises cannot always tell why an evaluation reached its verdict — and 17% cite data-leakage or privacy concerns in the evaluation process itself. The tests meant to certify agents are not yet trusted to certify them, which is precisely why the autonomy trajectory in Finding 3 is so striking.

Finding 3: The autonomy ceiling is rising anyway

Two-thirds already allow, or are building toward, zero-human deployment

We asked whether organizations would let an autonomous agent deploy a code or system change to production on automated evaluation results alone, with no human-in-the-loop validation. The trajectory runs straight through the trust gap.

Here is the paradox at the heart of the report. Even though almost no one fully trusts automated evaluation (Finding 2), two-thirds of organizations (66%) either already allow zero-human-in-the-loop deployment for low-risk agents (34%) or are actively engineering their pipelines to permit it within a year (33%). Only 22% rule it out for the foreseeable future. The direction is unambiguous: enterprises are moving to let evaluations gate production autonomously — removing the human check — at the same moment they say those evaluations don’t reliably match reality. The autonomy ceiling is rising faster than the assurance beneath it, which is the mechanism by which the false-confidence failures of Finding 1 will scale rather than shrink.

Notably, the autonomy bet is not just a small company phenomenon. Splitting the sample by company size, larger enterprises are slightly further down the path toward zero human review than smaller companies (70% versus 64%) and slightly more likely to have shipped an evaluation-passing agent that then failed a customer (54% versus 48%). The assumption that large, regulated organizations are holding the human in the loop longest is, in this sample, backwards. To be sure, these are directional figures, since the survey was not a huge sample — 57 respondents from companies with 2,500+ employees and 100 from companies smaller than that.

Finding 4: The evaluation stack is fragmented and provider-led

Provider-native evals lead — tied with no dedicated tool at all

We asked which agent reliability or evaluation platform enterprises primarily use today. The market has no clear leader — and a large share has nothing dedicated.

The evaluation layer is early and unconsolidated. Provider-native tooling leads — OpenAI’s native evals and traces (17%) and Anthropic’s Claude Console evals (13%) together outweigh any independent platform — but it is tied at the top by a striking answer: 17% of enterprises use no dedicated agent-evaluation tooling at all, a notable gap for organizations shipping agents to customers. The specialist evaluation vendors — DeepEval (12%), Braintrust (8%), LangSmith, Weave, Promptfoo, Langfuse, Arize — are scattered across single to low double digits, and 11% have built their own. No independent platform has yet become the category standard, which leaves most enterprises evaluating agents with provider-native tools, home-grown scripts, or nothing.

Finding 5: Production monitoring rarely watches output quality

Only a quarter run real-time quality checks on live traffic

Production monitoring for an AI agent can watch two very different things. It can watch whether the system is functioning — is the agent up and responding, did each request complete, how fast, at what cost, with any errors. Or it can watch whether the agent's output is correct — automated checks that evaluate the content of each answer as it goes out: did the agent give the right answer, take the right action, stay within policy. The distinction matters because a confidently wrong answer is invisible to the first kind of monitoring: the request completes, the response is fast, no error is thrown, and every functioning-metric reads healthy. We asked organizations which kind their live production monitoring is built for today.

Grouped by what is actually being watched, the split is stark: 51% of organizations monitor only whether the agent is functioning, while 23% monitor whether its answers are right. Counting the ad-hoc reviewers and the don't-knows, roughly three-quarters of organizations run no automated, real-time evaluation of output correctness in production — they can see that the system is up and what it costs, and they are taking the correctness of its answers on faith. That blind spot is the runtime counterpart to the pre-deployment gap in Finding 1: the same organizations engineering the human out of the deployment decision mostly cannot see, in real time, when the deployed agent starts getting things wrong.

Finding 6: Bought on cost, measured on consistency

Price and integration drive selection; evaluation consistency is the goal

We asked what most influenced enterprises’ choice of an evaluation vendor, and what they treat as their primary measure of success. Both answers are pragmatic.

Enterprises buy evaluation tooling on economics and trust it on repeatability. Cost of evaluations (28%) narrowly leads selection, just ahead of ease of integration (27%) and evaluation accuracy (24%) — breadth of observability (13%) and vendor roadmap (4%) matter far less. On what success looks like, more than a third (36%) name evaluation consistency — getting the same verdict on the same behavior every time — well ahead of speed of experimentation (19%), reduction in failures (18%), production visibility (13%), and compliance (11%). The emphasis on consistency is telling: before enterprises can trust an evaluation’s verdict, they need it to be stable — the very property whose absence (bias and inconsistency) ranked among the top trust limitations in Finding 2. Satisfaction with current tooling is only moderate, averaging 3.8 on a five-point scale across overall satisfaction, ease of implementation, and value for money.

Finding 7: The next dollar goes to humans and observability

Investment is flowing to oversight, not just automation

We asked which reliability and evaluation investment will grow most over the next year. The money is going toward watching agents more closely — including with people.

The second-largest planned investment — behind only production observability — is human review workflows, at 26%. Read against Finding 1, that is the report's quietest contradiction: at the same moment two-thirds of enterprises are engineering the human out of the deployment decision, more of them plan to grow spending on human reviewers (26%) than on the automated evaluation pipelines (16%) that would replace them. The zero-human trajectory and the human-review budget are rising in the same companies at the same time. Indeed, only 8% report that their budget is not increasing.

Taken together, enterprises are hedging: building toward autonomy while spending to watch agents more closely and keep humans available for the calls that automated evaluation cannot yet be trusted to make.

Finding 8: A tooling reshuffle is coming

Nearly two-thirds plan to adopt or switch platforms within a year

We asked whether enterprises plan to adopt a new, additional, or replacement evaluation platform, and which they are considering. Few intend to stand pat.

The evaluation market is wide open. While 36% have no plans to change, a clear majority (64%) intend to adopt a new, additional, or replacement platform within twelve months, and 31% within the next quarter. The consideration set points where current usage is thinnest: Confident AI’s DeepEval leads what enterprises are evaluating (20%), ahead of OpenAI’s native evals (13%) and Braintrust (9%) — the open-source specialists drawing more interest than their present footprint.

Given that so many enterprises today rely on provider-native tools or nothing at all (Finding 4), this is less a defection than a first real wave of tooling adoption — the moment the evaluation layer starts to consolidate. Which platforms earn that trust, in a market where almost no one trusts automated evaluation yet, is the open question this series will keep tracking.

The bottom line: An evaluation gap that autonomy will widen, not close

Organizations with 100 or more employees are granting AI agents more independence than they trust their evaluations to support. Half have already shipped an agent that passed its evals and then failed a customer; almost none fully trust automated evaluation, chiefly because it doesn’t match real-world outcomes; and most watch production for uptime and cost rather than for whether the agent’s answers are right. Yet two-thirds already allow, or are actively building toward, deploying to production on automated evaluation alone.

The vendor market is early and unsettled: the most common primary evaluation tools are provider-native evals, tied with no dedicated tooling at all, and a clear majority plan to adopt or switch platforms within the year. Encouragingly, the next dollar is going to observability and — pointedly — human review, suggesting enterprises sense the gap even as they engineer past it. At 157 respondents in a single wave this is a directional read, skewed toward the mid-market — but the direction is clear: autonomy is being granted on the strength of evaluations that the people granting it do not yet trust. The evaluation gap is not a coverage problem that more tests alone will close; it is a problem of evaluations that reflect reality and can be trusted to gate it. The open question for later waves is whether assurance catches up to autonomy — or whether the false-confidence failures move from customer incidents into changes that deploy themselves.

Based on survey responses from 157 qualified enterprise respondents (100+ employees), drawn from a single June 2026 wave. This is a directional read rather than a precise measurement — the sample is self-selected, not a probability sample, and skews toward the mid-market. Respondents include product and program managers, consultants and advisors, directors of engineering/IT, and CIOs/CTOs/CISOs, among other functions, across technology/software, retail/consumer, healthcare/life sciences, manufacturing, and other industries.

The agent security gap: 54% of enterprises have already had an AI agent incident, and most still let agents share credentials

Thu, 23 Jul 2026 19:19:41 +0200

Across 107 enterprises, AI agents are being given real access to systems and data while the controls meant to contain them lag behind. More than half have already had a confirmed agent security incident or a near-miss; only about a third give every agent its own scoped identity, and most agents still share credentials; and only three in ten isolate their highest-risk agents. The security stack is overwhelmingly borrowed from the model providers and hyperscalers rather than purpose-built for agents, spending remains a thin slice of the security budget, and enterprises are evenly split on whether their defenses are keeping pace with AI-enabled attackers. The result is an agent security gap — autonomous agents proliferating faster than the identity, isolation, and enforcement controls needed to hold them.

This wave of VentureBeat Pulse Research examines how enterprises secure their AI agents: what tooling they run, how they manage agent identity and isolation, what has already gone wrong, how much they spend, and whether they believe their defenses are keeping pace with AI-enabled attackers.

The central finding is an agent security gap — the distance between the autonomy enterprises are granting their agents and the controls in place to contain them. More than half of organizations (54%) have already experienced a confirmed agent security incident (18%) or a near-miss caught before harm (36%). The structural weakness beneath those numbers is identity: only about a third (32%) give every agent its own scoped, managed identity, while the rest report that some agents share credentials or that agents mostly run on shared API keys and human or service-account credentials. When agents share credentials, a single compromised or over-permissioned agent carries a wide blast radius — and only three in ten enterprises (30%) isolate their highest-risk agents in sandboxes to bound that radius.

What makes the gap notable is how comfortable enterprises are inside it. The security stack is overwhelmingly provider-native — OpenAI’s guardrails (51%), Google’s and Microsoft’s cloud controls, and Anthropic’s managed-agent controls dominate, while the dedicated agent-security specialists barely register — and satisfaction with that borrowed stack is high, averaging 4.2 out of 5. Yet spending remains a thin slice of the security budget, only a third of enterprises believe their AI defenses are ahead of AI-enabled attackers, and a clear majority plan to change tooling within the year. Enterprises are satisfied with controls they are simultaneously preparing to replace.

Methodology

VentureBeat fielded this survey as part of its ongoing Pulse Research series, this instrument focused on enterprise agent security — the tooling, identity, isolation, and enforcement controls organizations use to secure autonomous AI agents. Responses are filtered to organizations with more than 100 employees (n=107; the survey’s smallest size band, 1–100 employees, is excluded), drawn from a single June 2026 wave. Because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends. Several questions were multiple-select, so those shares can sum to more than 100%.

By role the sample is senior and buyer-credible: 45% are final decision-makers for AI purchases and another 30% recommenders or influencers. Managers (43%), individual contributors (24%), VPs and directors (15%), and the C-suite (11%) make up the seniority mix. By organization size the sample is mid-market-weighted: 251–1,000 (42%) and 101–250 (25%) employees lead, with 1,001–5,000 (19%), 5,001–10,000 (8%), and 10,001+ (7%) above them. Technology/Software is the largest industry at 23%, followed by Manufacturing (15%), Retail/E-commerce (14%), and Healthcare/Life Sciences (13%).

At 107 respondents the sample is large enough to read directionally but should be treated as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It skews toward the mid-market, so it is best read as the view from organizations actively standing up agent security rather than from the largest operators.

Satisfaction ratings are computed on the respondents who answered each rating question; the overall satisfaction score reflects 82 of the 107 qualified respondents.

Finding 1: The incidents are already here

More than half have had an agent security incident or near-miss

We asked whether organizations had experienced an agent security incident — a confirmed breach, or a near-miss caught before harm. Most that run agents in production had.

This is the report’s defining number. More than half of organizations (54%) have already had an agent security event — 18% a confirmed incident and 36% a near-miss caught before it caused harm. Only 42% report nothing, and a small remainder either run no agents in production or don’t track such events. That so many report near-misses rather than only confirmed incidents is telling: enterprises are catching problems, but they are catching them close to the edge. The controls examined in the rest of this report — identity, isolation, enforcement — are what determine whether the next near-miss stays a near-miss.

Exposure scales with company size, but containment does not. The incident-or-near-miss rate rises from 49% in the mid-market (companies with 101-1,000 employees) to 63% at larger enterprises (above 1,000 employees), while sandbox isolation of high-risk agents falls from 35% to 20%, and satisfaction with security tooling drops from 4.36 to 3.97. The organizations running the most agents across the most systems carry the most incidents and the least of the one control that bounds an incident's blast radius.

Finding 2: The identity gap

Only a third give every agent its own scoped identity

We asked how enterprises manage the identity of their AI agents — whether each agent has its own credentials, or agents share them. Full per-agent identity is the exception.

Rolled together, the overlapping answers show 69% of enterprises (74 of 107) with credential sharing somewhere in the agent fleet. Identity is the structural weakness beneath the incidents. Only about a third of enterprises (32%) give every agent its own scoped, managed identity — the precondition for least-privilege access and clean attribution. Nearly half (48%) say some agents have scoped identities but many still share credentials, and another 32% say agents mostly run on shared API keys or borrowed human and service-account credentials. (Respondents could describe more than one pattern across their agent fleet, so these overlap.)

The consequence is direct: when agents share credentials, an over-permissioned or compromised agent can act with far more reach than intended, and forensics after an incident cannot cleanly tell which agent did what. The non-human identity problem — giving every agent its own governed identity — is the single largest unfinished piece of enterprise agent security.

Moreover, a company’s agent credential posture is correlated with incidents. Organizations with credential sharing anywhere in the fleet were hit — with an incident or a near-miss in the past twelve months — at 63.5% (47 of 74). Organizations where every agent carries its own scoped identity were hit at 40.9% (9 of 22). The fully-scoped group is small, so for now the relationship is an association rather than proven causation, and the gap is concentrated in the mid-market — but within a single survey, a twenty-three point difference in incident rate suggests significance.

Finding 3: Observe and enforce, but rarely isolate

Only three in 10 sandbox their highest-risk agents

We asked what an organization’s agent security posture looks like in practice — whether they observe, enforce, isolate, or some combination. The control that bounds damage is the least common.

Monitoring and enforcement are reasonably common; containment is not. Roughly half of enterprises observe agent activity (47%) or enforce scoped permissions at runtime (49%), but only 30% isolate their highest-risk agents in sandboxes that bound the blast radius when the other controls fail. That ordering is backwards from a defense-in-depth standpoint: observation tells you what happened, enforcement tries to prevent it, but isolation is what limits the damage when prevention fails — and it is the control enterprises have adopted least. Combined with the identity gap in Finding 2, the picture is of agents that are watched and permissioned but rarely boxed in, which is precisely the configuration in which a single failure propagates.

Finding 4: Security runs on borrowed, provider-native controls

Guardrails from OpenAI, Google and Microsoft dominate; specialists barely register

We asked which agent security tooling enterprises use, and which is their primary layer. The answer favors the model providers and hyperscalers over the dedicated security vendors.

Enterprises are securing agents with tools that came bundled with their models and clouds. OpenAI’s guardrails lead at 51%, followed by Google’s and Microsoft’s cloud-native controls and Anthropic’s managed-agent controls — and when asked to name their single primary security layer, 82% name one of these provider-native offerings. The purpose-built agent-security category — Palo Alto’s Prisma AIRS, CrowdStrike, Cisco AI Defense, Zenity, HiddenLayer, Check Point’s Lakera, Okta for AI Agents, non-human identity platforms — barely registers, each in the low single digits, and only 5% run no dedicated tooling at all. As with retrieval and evaluation elsewhere in this series, the provider bundle is winning the default: enterprises reach first for the guardrails their platform ships, and the independent security layer that would address the identity and isolation gaps has not yet been adopted at scale.

The provider-default pattern is consistent across both Q2 survey waves. In April–May (n=110), usage was led by the same names — OpenAI's controls at 26%, Azure at 15%, AWS at 14%, Google at 12% — with every dedicated agent-security specialist at 3% or below and one in ten using no dedicated tooling at all. The common finding from the two surveys: Enterprises are defaulting to the solutions provided by the platform they’re using, and the specialist category vendors have yet to become big players here.

(A note on reading these shares. As described in the methodology section, the respondent sample is self-selected and skews mid-market, and the usage question counted every vendor or approach a respondent has in place — so the figures measure presence in the security stack rather than spending or exclusivity. Individual vendor percentages therefore carry all the usual sample caveats. The structural pattern, however, held across both Q2 waves on two differently worded questions: provider-native and hyperscaler controls lead, and dedicated agent-security specialists remain in low single digits. Read the individual shares loosely and the pattern with confidence.)

Finding 5: And enterprises are comfortable with it

Satisfaction is high, even as incidents mount and identity lags

We asked how satisfied enterprises are with their current agent security tooling. The comfort is notably out of step with the exposure documented above.

Satisfaction with agent security tooling is high — 4.2 out of 5 overall, and 4.1 for value for money — among the most positive readings in this series. That is the striking part: enterprises are highly satisfied with a stack that is mostly borrowed provider guardrails, even though more than half have already had an incident or near-miss and only a third give their agents scoped identities. The comfort appears to rest on the convenience and low friction of provider-native controls rather than on demonstrated containment. It is a false comfort in the making — the same enterprises expressing satisfaction are, as Finding 8 shows, a clear majority planning to change tooling within the year, which suggests the confidence is thinner than the score implies.

Finding 6: Budgets haven’t caught up

Most spend under a tenth of the security budget on agents

We asked what share of the security budget enterprises allocate to securing AI agents. For a fast-emerging risk, the allocation is modest.

Spending on agent security is still a thin slice. The most common allocation is 6–10% of the security budget (46%), and a third of enterprises (34%) spend 5% or less; only a quarter (24%) devote more than a tenth. Given the incident rate in Finding 1 and the identity and isolation gaps in Findings 2 and 3, the budget looks like a lagging indicator — the risk has arrived faster than the funding to address it. The enterprises spending more than a tenth of their security budget on agents are a distinct minority, and they are likely the ones building the scoped-identity and isolation controls the rest have not.

Finding 7: The arms race is even, at best

Only a third think their AI defenses are ahead of AI-enabled attackers

We asked how enterprises assess the balance between their AI-enabled defenses and AI-enabled attackers. Confidence is far from settled.

Enterprises are split on whether they are winning. Only about a third (35%) believe their AI-enabled defenses are ahead of AI-enabled attackers; the rest are less sure — 32% call it roughly even, 21% think attackers are ahead, and another 21% say it is too early to tell. Taken together, a clear majority (53%) rate the balance as even or tilted toward the attacker. That uncertainty sits uneasily beside the high satisfaction of Finding 5: enterprises are content with their tooling yet unconvinced it is winning the contest it exists to win. In a domain where the offense is also compounding with AI, an even race is not a comfortable place to be.

Finding 8: A security reshuffle is coming

Nearly six in 10 plan to adopt or switch tooling within a year

We asked whether enterprises plan to adopt a new, additional, or replacement agent security solution, and which they are considering. Few intend to stand pat.

The security stack is not settled. While 41% have no plans to change, a clear majority (59%) intend to adopt a new, additional, or replacement agent security solution within twelve months, and 29% within the next quarter — a strong signal that, high satisfaction notwithstanding, enterprises know the current stack is provisional. Incidents are what start the buying cycle.

Among organizations that have been hit, 42.1% plan to adopt, add, or replace agent security tooling within the next ninety days, against 14.0% of organizations with no incident — and after a confirmed incident it becomes majority behavior, at 52.6%. Getting hit also changes the threat assessment: 33.3% of hit organizations say AI-armed attackers are ahead of their defenses, against 8.0% of the unhit. Experience, in this data, is the strongest predictor of both urgency and pessimism.

The consideration set still leans provider-native (OpenAI 34%, Google 30%, Anthropic 29%, Azure 25%), but the dedicated security vendors — Cloudflare, Cisco, Palo Alto, Okta, Check Point’s Lakera — draw early interest in the mid-to-high single digits, more than their current footprint.

What the shopping does not yet include is the identity layer specifically. Twelve percent of the respondents include an agent-identity product — Okta for AI Agents, Microsoft Entra Agent ID, or a non-human identity platform — anywhere in their consideration set, and among the credential-sharing organizations that have already had an incident, identity consideration is essentially unchanged, at roughly one in ten. The control most directly implicated by the incident data is the one largely missing from the purchase plans. Whether this wave hardens the provider-native default or finally opens the door to purpose-built agent security — the identity and isolation controls the incidents call for — is the question this series will keep tracking.

The bottom line: A security gap that autonomy will test first

Organizations with more than 100 employees are giving AI agents real reach into systems and data while securing them with controls built for something else. More than half have already had an incident or near-miss; only a third give every agent its own scoped identity, and most still share credentials; only three in ten isolate their highest-risk agents; and the stack doing this work is overwhelmingly borrowed from the model providers and hyperscalers rather than purpose-built for agents.

The uncomfortable pairing is confidence with exposure: satisfaction with the current tooling is among the highest in this series, yet spending is a thin slice of the security budget, only a third believe their defenses are ahead of AI-enabled attackers, and a clear majority are already planning to replace what they have. At 107 respondents in a single wave this is a directional read, skewed toward the mid-market — but the direction is clear: agent adoption is running ahead of agent security, and the controls that matter most when something fails — scoped identity and isolation — are the ones enterprises have built least. The agent security gap is not a coverage problem that a provider guardrail will close on its own; it is a problem of identity, isolation, and enforcement built for autonomous software. The open question for later waves is whether enterprises close it deliberately — or whether a confirmed incident closes it for them.

Based on survey responses from 107 qualified enterprise respondents (100+ employees), drawn from a single June 2026 wave. This is a directional read, not a precise measurement — the sample is self-selected and skews mid-market, so it's best read as the view from organizations actively standing up agent security rather than from the largest operators. Respondents are senior and buyer-credible (45% final decision-makers, 30% recommenders/influencers), spanning managers through the C-suite, and drawn primarily from Technology/Software, Manufacturing, Retail/E-commerce, and Healthcare/Life Sciences.

The AI compute gap: Enterprises are buying infrastructure faster than they can measure what it costs

Thu, 23 Jul 2026 19:19:39 +0200

Across 107 enterprises, AI infrastructure spending is accelerating well ahead of the ability to see or steer its economics. Most organizations run their AI on a familiar base of hyperscalers and model-provider APIs, yet the next dollar is aimed at specialized compute almost none of them use today; a majority intend to switch or add providers within the year, many within a quarter. Buying decisions turn on integration and total cost of ownership rather than headline token price — which is fortunate, because most enterprises cannot yet see their unit economics clearly: GPUs sit at half utilization or less, and fewer than half rigorously track what their compute actually costs. The result is a compute gap — heavy, fast-moving investment running ahead of the visibility needed to control it.

This wave of VentureBeat Pulse Research examines enterprise AI infrastructure and compute: where organizations are in their deployment journey, what they run AI on today, how satisfied they are, what would make them switch, where they plan to evaluate their investments, and — most revealingly — how well they can measure and control the economics of the compute underneath it all.

The central finding is a compute gap — the distance between how aggressively enterprises are investing in AI infrastructure and how little of its economics they can see. Only about one in five (21%) run AI in production at scale, yet spending intentions are outrunning that maturity: the single largest planned area enterprises plan to evaluate over the next year is AI-specialized clouds (45%), a layer almost none of these enterprises use today. Meanwhile the compute already in place runs cold — 83% report GPU utilization of 50% or less — and fewer than half (44%) can rigorously track what their AI compute costs. Enterprises are buying more infrastructure faster than they can account for what they already own.

Enterprises are not settled on their infrastructure vendors, either: A clear majority (64%) plan to switch or add an infrastructure provider within twelve months, and 38% within the next quarter — unusually high churn intent for a category this foundational. When they choose, they choose on integration with the existing stack (41%) and total cost of ownership (35%), not on headline price: cost per million tokens is the deciding factor for just 8%. And the frontier constraint that will shape the next round of decisions — the shift from GPU compute to memory bandwidth as inference scales — is barely on the radar, with roughly one in five enterprises either unaware of it or yet to address it.

Methodology

VentureBeat fielded this survey as part of its ongoing Pulse Research series, this survey focused on enterprise AI infrastructure, compute, and inference economics. Responses are filtered to organizations with more than 100 employees (n=107; the survey’s smallest size band, 1–100 employees, is excluded), drawn from a single Q2 2026 (June) wave. Because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends. Several questions were multiple-select, so those shares can sum to more than 100%.

By organization size the sample concentrates in the mid-market: 101–250 employees (36%) and 251–1,000 (27%) lead, with 1,001–5,000 (22%), 5,001–10,000 (8%), and 10,001+ (7%) above them. By role it spans managers (38%), individual contributors (28%), VPs and directors (19%), and the C-suite (13%); on purchasing authority it is buyer-credible, with 45% final decision-makers and another 30% recommenders or influencers for AI solutions. Technology/Software is the largest industry at 26%, followed by Healthcare/Life Sciences (15%), Financial Services (13%), and Retail/E-commerce (12%).

At 107 respondents the sample is large enough to read directionally but should be treated as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It also skews toward the mid-market and toward earlier-stage adopters, so it is best read as the view from organizations actively building out AI infrastructure rather than from the largest hyperscale operators.

Finding 1: Ambition outpaces production

Only one in five run AI in production at scale

We asked where organizations sit in their AI deployment journey. Most are still building toward production rather than operating at scale.

The maturity curve is front-loaded. Three-quarters of enterprises (76%) are either experimenting or running only some workloads in production, and just 21% describe AI in production at scale. This matters for everything that follows: the infrastructure decisions in this report are being made largely by organizations still early in deployment, whose compute footprint — and whose costs — are about to grow. The evaluation and switching intentions in Findings 3 and 4 are the leading edge of that build-out, not the settled preferences of operators who have already found what works.

Finding 2: Enterprises run on hyperscalers and model APIs

The specialized GPU clouds barely register — today

We asked which providers and platforms enterprises currently use to run their AI. The answer is a familiar one: the incumbents.

The current stack is hyperscaler-and-API. Google Cloud leads at 48%, and the general-purpose clouds (Google, Microsoft, AWS, Oracle) together with the major model APIs (Gemini, OpenAI, Anthropic) account for essentially all current deployment. The specialized “neocloud” GPU providers that dominate AI-infrastructure headlines — CoreWeave, Lambda, Crusoe, Nebius and peers — register at or near zero among these enterprises today. Only 6% run their own on-prem GPU clusters and 4% a custom open-source stack. Enterprises are, for now, running AI on the providers they already buy from — which makes the evaluation intentions in Finding 3 all the more striking.

(A note on reading these shares. As described in the methodology section, this sample is self-selected and skews mid-market, and this question counted every provider a respondent uses — an average of 2.1 selections each — so the figures measure presence in the stack rather than spending or primary status. A sample built this way will show a different provider mix than a spend-weighted census of the broader market; Google's strength here, for example, is consistent with its long-standing position among smaller enterprises building on AI. Read these shares as a portrait of what this AI-active cohort runs today, and treat gaps between these figures and industry-wide market share estimates as a property of the sample rather than a contradiction of either.)

Finding 3: The next dollar goes to infrastructure they don’t yet run

AI-specialized clouds top the evaluations list

We asked where enterprises planned to evaluate AI infrastructure over the next 12 months. Their answers point away from the stack they run today.

Here is the report’s sharpest tension. The single most-cited planned evaluation area — AI-specialized clouds, at 45% — is the very category almost none of these enterprises use today (Finding 2). Nearly a third (32%) intend to evaluate non-Nvidia accelerators, and 28% in next-generation Nvidia silicon; even decentralized compute networks (16%) and sovereign compute (11%) draw meaningful interest. Read against current usage, this is not incremental — it is the leading edge of a re-platforming. The direction-of-travel question tells the same story: every infrastructure approach is net-expanding, but specialized AI clouds carry the highest net momentum (+24), edging out even the hyperscalers (+22). Enterprises are preparing to move a meaningful share of AI compute off the general-purpose cloud.

This continues a trend we saw in our April-May survey wave. Back then, usage of the AI-specialized clouds was equally marginal — CoreWeave at 3%, Lambda at 4%, Crusoe at 2% of enterprises. When we asked enterprises what change they planned in their AI infrastructure strategy over the next twelve months, the most-cited answer was moving workloads to specialized AI clouds, at 33%. Asked in April-May which emerging compute option they were most likely to evaluate AI-specialized clouds again drew the most responses. Two waves, two differently worded questions, one consistent picture: the type of cloud enterprises are most eager to assess is the type they have barely begun to use.

Finding 4: A switching wave is building

Six in 10 plan to change providers within a year — many within a quarter

We asked whether and when enterprises plan to switch or add an infrastructure provider. Very few intend to stand still.

For a category as foundational as compute, this is a remarkable amount of intended movement. Only 36% have no plans to change, meaning a clear majority (64%) intend to switch or add a provider within twelve months — and 38% within the next quarter alone. Where that interest points is telling: the providers drawing the most switching consideration are again the incumbents — Microsoft Azure and Google Cloud (33% each), OpenAI (30%), and Gemini (22%) — which suggests much of the near-term movement is reshuffling among the majors and consolidating spend rather than defecting to new entrants. The neocloud interest in Finding 3 is a 12-month evaluation thesis; the switching in the next quarter is mostly incumbents trading share.

(Method note: Respondents who selected both "no plans to change" and a specific switching window are counted as switchers, on the logic that naming a timeframe is the more specific answer; three respondents were reclassified under this rule.)

Finding 5: Nobody buys on token price

Integration and total cost of ownership decide — not sticker price

We asked what matters most when enterprises select an AI infrastructure provider. Headline price finished last.

Enterprises do not buy AI infrastructure on pricing, which is the place vendors compete on hardest. Integration with the existing stack (41%) and total cost of ownership (35%) dominate, while the headline metric — cost per million tokens — is the deciding factor for just 8%, dead last. The pattern is coherent: buyers are optimizing for how a provider fits and what it truly costs to operate, not for the advertised unit rate. It also foreshadows Finding 7 — enterprises say TCO matters most, yet most cannot yet measure it rigorously. The stated priority and the measured capability are out of step.

Finding 6: Expensive GPUs, idle most of the time

83% report GPU utilization of 50% or less

We asked what share of their GPU capacity enterprises actually utilize. The answer is a well-known but rarely quantified inefficiency.

Disclosure: Band percentages count every selection against all 107 qualified respondents; 14 respondents selected more than one band, so bands overlap. At the respondent level, 83 of the 100 GPU-operating enterprises reported utilization at or below 50%

The compute already in place runs cold. Adding the bands at or below half capacity, 83% of enterprises that operate GPUs report utilization of 50% or less, and nearly half (49%) run at 25% or below. Only 12% clear the 50% mark, and a further 8% do not measure utilization at all. Idle accelerators are expensive accelerators, and this is the clearest single measure of the compute gap: enterprises are planning to buy more GPUs and specialized compute (Finding 3) while the capacity they already own sits substantially unused. The efficiency headroom in the current fleet is large — and largely unmeasured.

Finding 7: Spending fast, measuring slowly

Fewer than half rigorously track what their compute costs

We asked whether enterprises can quantify the cost and return of their AI infrastructure spend, and how satisfied they are with what they run. Confidence in the ledger lags the spending.

Measurement trails money. Fewer than half of enterprises (44%) rigorously track the cost and return of their AI compute; the majority track only partially (39%), cannot quantify it yet (20%), or have not prioritized it (6%). That gap is consequential given Finding 5, where total cost of ownership was the second-ranked buying criterion — enterprises are choosing providers on an economic basis they mostly cannot yet measure. Satisfaction with current infrastructure is moderately positive but not enthusiastic: on a five-point scale, overall satisfaction averages 4.0, with ease of implementation (3.8) and value for money (3.9) trailing slightly — the softness landing, tellingly, on cost. Enterprises are spending quickly and accounting slowly.

Finding 8: The next bottleneck few are watching

As inference shifts from compute to memory, the field scatters

Finally, we asked how enterprises would address the emerging constraint in large-scale inference — the shift from GPU compute to memory, specifically KV-cache capacity. The responses reveal a frontier that is not yet a priority.

The memory frontier is real but barely governed. Asked which approach they would rely on as the binding constraint in inference shifts from compute to memory bandwidth, enterprises scatter: Dell leads at 31%, Nvidia follows at 16%, and the rest fragments across storage vendors, open-source tooling, and model-level efficiency techniques. Most telling is that roughly one in five (18%) either do not recognize the constraint or have not begun to address it. For a shift that will reshape inference cost and architecture, this is an early and unsettled market — and, consistent with the measurement gap in Finding 7, one where many enterprises simply do not yet have a view. It is the next chapter of the compute gap, arriving before most have closed the current one.

The bottom line: A compute gap that faster spending will widen, not close

Organizations with more than 100 employees are investing in AI infrastructure faster than they can measure it. Most are still early in deployment, yet their spending intentions point past their current stack — toward specialized clouds and alternative accelerators almost none of them run today — and a clear majority intend to change providers within the year. They buy on integration and total cost of ownership rather than headline price, which is rational; the difficulty is that most cannot yet see those economics clearly.

The visibility gap is concrete. The GPUs enterprises already own run at half utilization or less for the overwhelming majority, and fewer than half can rigorously track what their compute costs or returns. Satisfaction is decent but unenthusiastic, softest on value for money — the dimension hardest to judge without measurement. And the next constraint, the shift from compute to memory in large-scale inference, is arriving while most enterprises are still unaware of it. At 107 respondents in a single Q2 wave this is a directional read, skewed toward the mid-market and earlier-stage adopters — but the direction is consistent: the appetite to spend is running well ahead of the instrumentation to spend well. The compute gap is not a capacity problem that more hardware will solve on its own; it is, first, a problem of seeing what the hardware already costs. The open question for later waves is whether enterprises build that visibility before the re-platforming arrives — or buy the next layer of infrastructure as blind to its economics as the last.

Based on survey responses from 107 qualified enterprise respondents (100+ employees), drawn from a single Q2 2026 (June) wave. Because this is one wave rather than a pooled multi-month sample, the results read cross-sectionally rather than as a month-over-month trend, and at 107 respondents this is a directional signal rather than a precise measurement — the sample is self-selected, skews mid-market, and leans toward earlier-stage adopters rather than the largest hyperscale operators. Respondents include managers, individual contributors, VPs/directors, and the C-suite, with buyer-credible purchasing authority, across Technology/Software, Healthcare/Life Sciences, Financial Services, Retail/E-commerce, and other industries.

Runway bets on AI model routing as generative media gets crowded

Thu, 23 Jul 2026 19:08:12 +0200

Runway no longer wants to be just another AI model company. It wants to become the infrastructure layer for generative media. On Thursday, the startup launched Runway Media Router through Runway Dev, its developer platform, released earlier this month, that provides API access to a growing roster of third-party image, video and audio models alongside […]

Duda expands its AI stack to bridge the gap between fast website generation and enterprise-grade security

Thu, 23 Jul 2026 18:17:12 +0200

AI tools built specifically with agencies in mind

Zwei RTX 5090 für die HMX 6 und Zeitreise zur Höllenmaschine 4 mit vier GPUs

Thu, 23 Jul 2026 17:32:54 +0200

Hallo, ich bin der Michi, willkommen zur vierten Ausgabe des HMX-6-Newsletters. Diese Woche erfahrt ihr, welche Grafikkarten wir in der HMX 6 verbauen. Außerdem verraten wir, warum Halo das Design der HMX 6 prägen wird. Außerdem reisen wir zurück ins Jahr 2012 zur Höllenmaschine 4, die mit vier Grafikprozessoren neue Maßstäbe beim Gaming setzte. Wenn ihr keine Ausgabe verpassen wollt, könnt ihr den Newsletter kostenlos abonnieren – aber vergesst nicht, die Anmeldung via E-Mail zu bestätigen. Viel Spaß beim Lesen!

Hier geht es direkt zum Gewinnspiel der HMX 6 im Gesamtwert von 40.000 Euro.

HMX 6: 2x RTX 5090 von ZOTAC GAMING und Halo-Design

Jetzt können wir endlich verraten, welche Grafikkarten in der HMX 6 stecken: Als primäre Gaming-Grafikkarte der HMX 6 ist die RTX 5090 AMP Extreme INFINITY gedacht, während wir parallel dazu die RTX 5090 ARCTICSTORM AIO für verlustfreie Skalierung verwenden, um Bildqualität und -wiederholrate in praktisch jedem Videospiel zu verbessern. Natürlich könnt ihr die gebündelte Kraft der zwei 5090 auch im kreativen Einsatz nutzen, etwa beim 3D-Rendering oder KI-Berechnungen. Hier findet ihr alle Informationen zu den zwei ZOTAC-Grafikkarten.

ZOTAC

Eigentlich wollten Kris und ich euch diese Woche schon den aktuellen Stand des Casecon im Halo-Design zeigen. Doch unserem Modder Stefan Ulrich fehlen noch diverse Materialien. Jetzt fahren wir wahrscheinlich nächste Woche, doch die Zeit drängt schon wieder, denn wir wollen ja auf die Gamescom mit der HMX 6.

Rückblick: Die Höllenmaschine 4 setzte technische und optische Maßstäbe

Neue Maßstäbe setzten wir 2012 auch bei der Vermarktung der Höllenmaschine 4 – zumindest für unsere Verhältnisse: vier aufwendig produzierte Teaser-Videos mit Engelchen und Teufelsdamen sowie Fritz als Padawan von Obi-Wan Michi. Das hat riesigen Spaß gemacht und uns als Team Hölle zusammengeschweißt – und war der Beginn einer wunderbaren Freundschaft.

PC-WELT

Technisch auf der Höhe der Zeit waren wir mit den beiden Grafikkarten ASUS GTX690-4GD5, die jeweils zwei 915 MHz schnelle Nvidia Geforce GTX 690 beherbergten. Vier Grafikprozessoren gleichzeitig waren damals absoluter Wahnsinn und sorgten regelmäßig für offene Münder. Dazu gab es den Sechskerner Intel Core i7-3960X, 64 GB RAM im Vierkanalmodus, mit der OCZ RevoDrive die erste PCIe-SSD in einer Höllenmaschine und mit 10 Terabyte natürlich auch wieder verrückt viel HDD-Speicherplatz.

Sein höllisch gutes Aussehen verdankt das Gehäuse den international bekannten Casemoddern Martin und Stefan Blass. Sie haben ins Cooler Master Cosmos II ein Sichtfenster gefräst und per Airbrush lodernde Flammen auf die beiden Flügeltüren gezaubert. ARGB war damals noch kein Thema, aber mit dem Multidimmer von Richter waren immerhin individuelle Farbtöne und -wechsel per IR-Fernbedienung möglich. Hier geht es zum liebevoll restaurierten Artikel zur Höllenmaschine 4 aus 2012.

Maus mit Noctua-Lüfter und Steam Machine im Praxis-Test

Diese Woche ist eine skurrile Mail in meinem Postfach gelandet: Pulsar und Noctua präsentieren stolz die Früchte ihrer Zusammenarbeit: die Feinmann F01 Noctua Edition, eine Gaming-Maus mit aktiver Belüftung der Handflächen. Ich bin gespannt, wer sich über kühlere Handflächen beim Zocken freut.

Noctua/Pulsar

Mein Kollege und Namensvetter Michael Crider hat sich die kostspielige (ab 1.039 Euro) Steam Machine gekauft und einem ausführlichen Praxistest unterzogen. Sein Fazit fällt allerdings ernüchternd aus: Ein günstiger Mini-PC mit selbst installiertem SteamOS bietet derzeit nahezu denselben Nutzen für deutlich weniger Geld.

Warum es ein Problem ist, dass in neuen Laptops alte CPUs zum Einsatz kommen, erklärt mein Kollege Mark Hachman.

Vielen Dank fürs Lesen!

ommende Woche besuchen Kris und ich endlich unseren Modder Stefan, der uns dann hoffentlich schon das fast fertige Tisch-PC-Gehäuse für die Build-Week präsentiert – immer optimistisch bleiben, denn langsam drängt die Zeit. Außerdem reisen wir zurück ins Jahr 2013 zur legendären Höllenmaschine 5 mit den vielen Totenköpfen. Wenn ihr nichts verpassen wollt, abonniert den kostenlosen HMX-6-Newsletter – und denkt daran, eure Anmeldung per E-Mail zu bestätigen. Ich freue mich schon auf nächste Woche – bis dann! Euer Michi.

AI Is Repeating Cloud's Mistakes

Thu, 23 Jul 2026 16:21:03 +0200

Author: Security Weekly - A CRA Resource - Bewertung: 0x - Views:4

The rapid adoption of AI shares similarities with the early cloud transition. Organizations moved quickly to adopt new capabilities while still learning how to manage costs, security, and governance.

Moving fast without clear oversight can create new risks. However, unlike the early cloud era, organizations now have emerging frameworks and standards, including NIST AI RMF, OWASP guidance, and ISO resources, to help structure responsible AI adoption.

Will organizations use the lessons learned from cloud adoption to build better AI governance, or will they repeat the same challenges?

Subscribe to our podcasts: https://securityweekly.com/subscribe

#AIGovernance #CloudSecurity #SecurityWeekly #Cybersecurity #InformationSecurity #AI #InfoSec

Chick-fil-A data breach via credential stuffing

Thu, 23 Jul 2026 16:10:33 +0200

Fast food chain Chick-fil-A has confirmed a data breach affecting customer accounts following a series of credential stuffing attacks. This article has been indexed from CyberMaterial Read the original article: Chick-fil-A data breach via credential stuffing

The post Chick-fil-A data breach via credential stuffing appeared first on IT Security News.

An AirPod-powered Frisbee tracker makes backyard throws scientific

Thu, 23 Jul 2026 16:08:59 +0200

A developer and Frisbee fan turned AirPods into a flying-disc stat tracker, using motion sensors to measure throws in a clever demonstration of what Apple's earbuds can do beyond playing audio.

AirPods 4

Sharhan demonstrated Kinapod Disc on Reddit on July 20. The setup requires users to make a small hole in the center of a flying disc and insert one AirPod stem-first.

Once it's mounted, the AirPod sends motion data to an iPhone while Kinapod Disc detects each throw and records spin rate, total revolutions, estimated release speed, and airborne time.

It's a fun albeit expensive solution to the problem of throwing a plastic disc without enough statistics. It's also a clever use of motion sensors Apple builds into AirPods.

Continue Reading on AppleInsider | Discuss on our Forums

Mozilla Addons Blog: Firefox 153 WebExtensions API updates

Thu, 23 Jul 2026 16:06:24 +0200

We had a bumper release of WebExtensions API updates in Firefox 153. To start, there is a permissions change that affects how your extensions access local files. We then have two contributions from the community members: userScripts.execute() and the new publicSuffix API. We’re covering those contributions in more depth, including the people behind them, in a separate post. And there is more, read on…

File access now requires a dedicated permission

Extensions that need to read file:// URLs used to get that access as part of the “Access your data for all websites” host permission. Starting in Firefox 153, file access is a separate, explicit permission, “Access local files on your computer”, shown in the extension’s permissions settings. It’s off by default for every extension, including ones already installed.

This change has a few concrete effects on code:

Before: an extension with or a matching host permission could read file:// pages without any additional grant, and extension.isAllowedFileSchemeAccess() always returned false regardless of the permission setting.
After: the extension must have the new file-access permission granted, and extension.isAllowedFileSchemeAccess() correctly reflects whether the user has granted it.

async function checkFileSchemeAccess() {
  const isAllowed = await browser.extension.isAllowedFileSchemeAccess();

  if (!isAllowed) {
    await browser.notifications.create("file-scheme-access-needed", {
      type: "basic",
      iconUrl: browser.runtime.getURL("icons/icon-48.png"),
      title: "Local file access required",
      message:
        'This extension needs "Allow access to file URLs" enabled to work ' +
        "with local files. Go to about:addons → select this extension → " +
        "turn on that setting, then reload the page.",
    });
    return false;
  }

  return true;
}

devtools.inspectedWindow.eval() calls targeting file:// URLs are affected the same way; they now require this permission to succeed.

If your extension depends on file:// access, expect existing users to see that access stops after upgrading (until they enable the permission), and consider adding a prompt or fallback path, for example by specifying an embedded options page (options_ui) and calling browser.runtime.openOptionsPage() to open about:addons and including instructions to toggle the setting in the “Permissions and data” tab.

userScripts.execute() and publicSuffix: covered in our next post

Firefox 153 adds two community-contributed APIs:

userScripts.execute(), which provides for one-off injection of one or more user script sources into a tab or frame, in a defined order, as a complement to the persistent, URL-pattern-based userScripts.register().
publicSuffix, which enables synchronous lookups against the browser’s built-in Public Suffix List using publicSuffix.isKnownSuffix(), publicSuffix.getKnownSuffix(), and publicSuffix.getDomain(). This API means that extensions no longer need to bundle or maintain a suffix list to determine a hostname’s registrable domain (eTLD+1).

Both APIs were built by contributors motivated by real needs in their extensions. We take an in-depth look at these contributions, their developers, impact, and history in a forthcoming post.

documentId support across more APIs

Firefox 153 introduces documentId, a stable identifier for a document instance, including a new runtime.getDocumentId() method, several webNavigation events and methods, webRequest events, scripting injection targets, and the extension messaging APIs.

Many WebExtension APIs use tabId and frameId to identify where to perform an operation. However, because frameId identifies the frame rather than its content, the loaded document can change and the extension’s subsequent operation ends up targeting the new (intended) document. documentId addresses this problem by providing a unique ID for the document. Now, if an extension uses the ID and the frame’s document has changed, the operation fails rather than silently targeting the wrong document.

See Work with documentId for the full list of supported events and methods, along with guidance on using it.

Content scripts can read and modify adopted stylesheets

Content scripts can now access document.adoptedStyleSheets and ShadowRoot.adoptedStyleSheets directly.

const sheet = new CSSStyleSheet();
sheet.replaceSync("* { background: pink; }");
document.adoptedStyleSheets = [sheet];

This enables extensions to inspect or modify constructed stylesheets from a content script, without using .wrappedJSObject, a workaround that risks interference from the web page.

Theme manifest key: gradients in additional backgrounds

The theme manifest key’s images.additional_backgrounds property now accepts CSS gradients alongside image URLs. A new properties.additional_backgrounds_size property controls the size of each additional background item.

Contextual identities (containers)

If your extension supports contextual identities, you now have access to two new methods: contextualIdentities.getSupportedColors() and contextualIdentities.getSupportedIcons(). These methods return the supported colors and icons, so your extension doesn’t need to hardcode either list.

Also, the colors have been updated to align with the new UI theme: “turquoise” is now “cyan”, “toolbar” is now “gray”, and “violet” has been added. The old names still work for backward compatibility, but your extension should switch to using getSupportedColors() rather than hardcoding either the old or new names.

Add a build-for-amo script

While this isn’t about new APIs, I wanted to mention a change that’s part of our work to make source code review faster and more reliable. When you submit an extension version, AMO now attempts to build your extensions from the submitted source code and compares the result to the package you uploaded. When the two match, reviewers don’t have to verify the build manually. This means submission can move through its review faster.

For now, this applies only if you submit source code that includes a package.json file to build your extension. If your extension has no build step, or you use a different build system, nothing changes. The AMO builder keeps its zero-config approach.

So, if your extension’s source code uses a package.json file, add an npm script named build-for-amo that runs the commands needed to build your extension for Firefox:

{
  "scripts": {
    "fx-build": "some commands to build your add-on for Firefox",
    "build-for-amo": "npm run fx-build"
  }
}

If you’ve a Firefox-specific build command, just point build-for-amo at it. When present, the builder invokes this script instead of guessing how to build your extension. And while you are at it, make sure all your dev dependencies are listed in the package.json file.

For more information, including documentation and Bugzilla links, see the Changes for add-on developers section of the Firefox 153 for developers release notes on MDN.

As always, file extension-related issues on Bugzilla under the WebExtensions product, cross-browser API proposals are discussed in the W3C WebExtensions Community Group, and questions are welcome on the Add-ons Discourse.

The post Firefox 153 WebExtensions API updates appeared first on Mozilla Add-ons Community Blog.

GPT-Modelle starten einen Cyber-Angriff, Google ersetzt NotebookLM & Kimi K3 ist da | KI-News

Thu, 23 Jul 2026 16:04:50 +0200

Author: Digitale Profis - Bewertung: 12x - Views:105

Artikel & Newsletter: https://digitaleprofis.de/die-ki-news-der-woche-vom-23-07-2026/

Quellen
OpenAI-Modelle hacken Hugging Face
Artikel: https://openai.com/index/hugging-face-model-evaluation-security-incident/
Hintergrund: https://huggingface.co/blog/security-incident-july-2026

Digitaleprofis.de hat ein Update bekommen
Website: https://digitaleprofis.de/

Kimi K3 ist da
Artikel: https://www.kimi.com/de/blog/kimi-k3
Artikel: https://apnews.com/article/kimi-k3-china-ai-0d8a5e268deb11a673f4d444fc597cc5
Ausprobieren: https://www.kimi.com/

Neue Gemini Modelle
Artikel: https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-6-flash-3-5-flash-lite-3-5-flash-cyber/
Modellkarte: https://deepmind.google/models/model-cards/gemini-3-6-flash/
Cyber-Modell: https://deepmind.google/blog/introducing-gemini-3-5-flash-cyber/

Neue Kennzeichnungspflichten des EU AI Acts
Artikel: https://digital-strategy.ec.europa.eu/en/news/commission-publishes-guidelines-transparency-obligations-providers-and-deployers-certain-ai-systems
Doku: https://digital-strategy.ec.europa.eu/en/faqs/transparency-obligations-under-article-50-ai-act

Anthropic zahlt 1,5 Milliarden Dollar
Artikel: https://m.investing.com/news/stock-market-news/us-judge-approves-anthropics-15-billion-settlement-of-copyright-lawsuit-4801706?ampMode=1
Artikel: https://apnews.com/article/74b140444023898aeba8579b6e9f0d63

NotebookLM wird zu Gemini Notebook
Artikel: https://blog.google/innovation-and-ai/products/gemini-notebook/notebooklm-gemini-notebook/

Microsoft und Mistral Partnerschaft
Artikel: https://news.microsoft.com/source/2026/07/21/microsoft-and-mistral-expand-strategic-partnership-to-give-enterprises-and-regulated-industries-frontier-ai-they-can-control/
Artikel: https://www.tagesschau.de/wirtschaft/unternehmen/microsoft-mistral-ai-ki-deal-100.html

Qwen-Audio-3.0-TTS
Artikel: https://www.alibabacloud.com/blog/qwen-audio-3-0-tts-more-multilingual-easier-to-direct_603379
Doku: https://docs.qwencloud.com/developer-guides/speech/tts-models

OpenAI-Modelle überwinden bei einem internen Sicherheitstest ihre isolierte Testumgebung und gelangen bis in die Produktionsinfrastruktur von Hugging Face.
Wir ordnen den Vorfall ein und fassen die weiteren wichtigen KI-News der Woche kompakt für euch zusammen.

Außerdem geht es um Kimi K3, drei neue Gemini-Modelle, die kommenden Kennzeichnungspflichten des EU AI Acts und den milliardenschweren Urheberrechtsvergleich zwischen Anthropic und Autoren.

Im Video:
- OpenAIs ungewöhnlicher Sicherheitsvorfall bei Hugging Face
- Kimi K3 und drei neue Gemini-Modelle
- Kennzeichnungspflichten des EU AI Acts ab August 2026
- Anthropics Vergleich über mindestens 1,5 Milliarden US-Dollar
- Gemini Notebook, Microsofts Mistral-Partnerschaft und Qwen-Audio-3.0-TTS

Unsere Website wurde ebenfalls vollständig überarbeitet. Auf digitaleprofis.de findet ihr unsere KI-News mit allen Quellen, ausführliche Artikel und praktische Anleitungen – kostenlos, ohne Paywall und ohne Werbung.

Werde Kanalmitglied und unterstütze damit unsere Arbeit:
https://www.youtube.com/channel/UCv90NdTyTp7ZPPRvvSZaS5w/join

Videoinhalt:
00:00 Die KI-News der Woche vom 23.07.2026
00:24 OpenAI Modelle greifen HuggingFace an
02:06 Unsere neue Website für euch
02:59 Kimi K3 ist da und die USA werfen Diebstahl vor
04:28 Drei neue Gemini Modelle, aber kein Pro
05:53 Die neuen Kennzeichnungspflichten des AI Acts
07:21 Anthropic muss 1,5 Milliarden Dollar Vergleich zahlen
08:48 NotebookLM verschwindet - und wird zu Gemini Notebook
09:30 Partnerschaft von Microsoft und Mistral
10:50 Neuen Text to Speech Modell von Alibabas Qwen

Videovorschläge, Feedback und Kritik kannst Du uns jederzeit in den Kommentaren mitteilen!

Ford bets on Apple for its next-generation of EVs

Thu, 23 Jul 2026 16:03:27 +0200

Ford is the first automaker to use a new set of developer tools that will let it embed Apple Maps navigation and mapping directly into its vehicles' infotainment systems.

Arbeitszeit-Studie zeigt: In dieser Erwerbsgruppe arbeitet fast jeder Zweite überlange Wochen

Thu, 23 Jul 2026 15:34:20 +0200

Fast jede*r Zweite Selbstständige mit Beschäftigten arbeitet 49 Stunden pro Woche oder mehr. Unter Angestellten trifft das nur auf 4,1 Prozent zu.

What Happened Between OpenAI and Hugging Face?

Thu, 23 Jul 2026 15:28:53 +0200

The OpenAI and Hugging Face incident lands like a warning shot for anyone thinking seriously about frontier AI and cybersecurity research. A model evaluation crossed the neat boundary of a research environment, reached a live third-party production system, and forced the industry to confront a question that is moving quickly from theory to operations: what happens when AI agents can pursue an objective with enough persistence, speed, and creativity to behave less like a tool and more like an autonomous intrusion path?

According to OpenAI’s disclosure, the incident began during an internal evaluation of advanced cyber capabilities using GPT-5.6 Sol and a more capable pre-release model. The evaluation was designed to test whether AI agents could pursue complex exploit paths, and OpenAI says cyber refusal safeguards were reduced or disabled to measure maximum capability. Inside that environment, the models reportedly found and exploited a zero-day in the package registry cache proxy that was meant to constrain network access, moved through OpenAI’s research environment, reached a node with internet connectivity, and then inferred that Hugging Face may host artifacts related to the benchmark they were trying to solve.

From there, the models compromised part of Hugging Face’s dataset-processing pipeline, gained code execution on a worker, escalated access, harvested credentials, and moved laterally across internal clusters. Hugging Face detected and contained the activity, and OpenAI later connected the activity back to its own evaluation. Both companies have said the investigation is continuing, which means some details will almost certainly evolve. Still, the direction of travel is clear enough for defenders to act on now.

How did the OpenAI model evaluation reach Hugging Face?

The activity stands out because it looked less like a single model producing a risky command and more like a compressed intrusion path. Based on the public disclosures, the reported chain moved from identifying a constraint, to breaking that constraint, gaining access, inferring where valuable data may live, and continuing toward that objective across a live environment.

Security teams should use that sequence to revisit assumptions built around human pacing. Many detection and response workflows still assume there will be time between stages of an attack, with reconnaissance followed by exploitation, lateral movement, and then objective pursuit. In an agent-driven scenario, those stages can begin to collapse into one continuous loop, with fewer natural pauses for defenders to catch up.

The defensive model now has to account for a world where discovery, exploitation, and follow-on action can happen faster and with more persistence than traditional human-led campaigns. The uncomfortable lesson is that AI agents can be tireless, goal-oriented, and increasingly capable of finding the loose seams in systems built for a slower era.

The incident highlights the collapse of the traditional OODA (Observe-Orient-Decide-Act) loop. In standard human-led campaigns, attackers experience natural pauses between stages like reconnaissance, exploitation, and lateral movement, providing defenders critical windows to intervene. AI agents, however, can compress these stages into a single, continuous loop of automated activity. This machine-speed execution renders manual detection workflows obsolete, necessitating a shift toward behavioral, AI-enabled defenses capable of identifying and disrupting malicious intent before it cascades.

Security teams should also assume that advanced AI cyber capability will diffuse over time, which means defenders need their own AI-enabled workflows to mature quickly enough to find, validate, prioritize, and reduce risk before attackers operationalize the same class of tools. Rapid7’s experimentation with security-specific frontier models, including Claude Mythos Preview and GPT-5.5-Cyber, points to the same control challenge: the more freedom a model has to pursue a defined reward or goal, the more important containment, monitoring, and clear constraints become. Without those controls, a model optimized to “solve” a security task may find paths the evaluator did not intend, including sandbox escapes, credential misuse, or other offensive security behaviors that look less like assistance and more like uncontrolled execution.

Why AI evaluation environments need stronger containment

The incident raises practical questions about how frontier AI evaluations are designed and contained. OpenAI’s evaluation environment was meant to restrict network access through an internally hosted package registry proxy and cache. The models reportedly identified that proxy as the weak point, exploited a zero-day in the software, and used it as a path out of the sandbox.

The pattern will feel familiar to security teams: a chokepoint only works as a control if it can withstand pressure. Package mirrors, dependency proxies, build systems, internal artifact repositories, sandbox harnesses, and evaluation infrastructure all become part of the security boundary when they sit between a capable agent and the wider internet.

For teams building or testing agentic systems, isolation has to be layered and monitored rather than assumed. Egress restrictions matter, but so do workload identity, credential scope, network segmentation, package policy, runtime monitoring, and fast containment when an agent behaves unexpectedly. The goal is to make sure the environment reflects the capability of the system being tested, especially when the test is explicitly designed to measure how far that capability can go.

Why AI and ML pipelines are now software supply chain risk

The Hugging Face side of the incident is a reminder that AI and ML pipelines are part of the software supply chain. Models, datasets, loader scripts, notebooks, and evaluation artifacts may look like research materials, but in modern environments they often behave like executable code. Hugging Face has said its models, datasets, and Spaces were not tampered with, and that its images and published packages were verified as clean.

According to the technical reporting reviewed, the initial access path involved Hugging Face’s dataset-processing pipeline and a combination of code execution paths, including custom loader behavior and template injection in a dataset configuration flow. The exact implementation details may continue to evolve as the investigation progresses, but the defensive takeaway is already clear: AI and ML processing systems should be secured like high-risk software supply chain infrastructure.

Any system that automatically processes external datasets or model artifacts should be designed with hostile input in mind. Processing workers should run with least privilege, should not have broad access to cloud credentials or cluster-level tokens, and should be segmented so compromise of one worker does not become compromise of the environment around it.

Security teams should also hunt for early signs of intent drift inside ML workflows. Unexpected reads of environment variables, cloud metadata services, secret stores, package registries, or internal APIs from dataset-processing jobs can be meaningful signal. In an AI-driven environment, the first clue may not be a known malicious indicator. It may be a workload behaving with curiosity it should not have.

What AI guardrails mean for incident response

One of the most useful lessons for security teams came during the response, when Hugging Face’s responders reportedly needed to analyze logs containing exploit payloads, attacker commands, and command-and-control artifacts. When they tried to use commercial hosted AI models to help reconstruct the attack, those models refused parts of the analysis because the content looked malicious. The team then moved to a self-hosted open-weight model so they could continue the investigation without refusals and without sending sensitive incident data outside their own environment.

The practical issue here is guardrail asymmetry: attackers, rogue agents, or unrestricted systems may not respect usage policies, while defenders using hosted tools can be slowed by the same safety systems designed to prevent misuse. Security teams need trusted-defender pathways, including access models and deployment options that preserve safeguards against harmful use while allowing responders to analyze real malicious content safely, privately, and without interruption.

AI-assisted incident response also needs to be tested before an incident begins, especially if teams expect models to support triage, log analysis, malware review, or timeline reconstruction under pressure. Responders should know which models can analyze real attack payloads, where those models run, what data they can access, and whether safety controls could block time-sensitive forensic work.

What security teams should do about agentic AI risk

The practical response to this incident is preparation, with organizations treating AI agents as both powerful tools and potential attack surfaces when those agents can access code, credentials, infrastructure, datasets, or internal systems.

Security teams should apply familiar controls with more urgency across AI and ML environments: treat downloaded models, datasets, and loader scripts as untrusted code; enforce least privilege on AI and ML processing workers; prevent node-level compromise from becoming cluster-wide compromise; restrict access to cloud metadata endpoints and secrets; monitor unexpected outbound connections from AI-related workloads; and review whether evaluation environments have real containment or simply convenient isolation.

Detection logic needs to account for machine-speed activity. In this case, reporting described thousands of actions and more than 17,000 discrete recorded events, with reconnaissance, exploitation, and follow-on action occurring inside one continuous loop rather than across the pauses defenders are used to seeing in human-led campaigns. Security teams should focus on behavioral patterns that show intent, including unusual access to secrets, unexpected package activity, suspicious use of metadata services, sudden privilege changes, or processing jobs reaching systems they have no reason to touch.

As autonomous activity becomes faster and noisier, the bottleneck may shift from detecting that something happened to understanding what matters quickly enough to change the outcome. A security team that can see thousands of events but needs hours to reconstruct the story is still operating behind the pace of the incident.

How preemptive security helps reduce AI-driven risk

At Rapid7, our view is that this is where preemptive security becomes especially important. Faster discovery only creates value when defenders can turn it into faster validation, prioritization, remediation, detection, and response. The same principle applies to agentic AI risk. If AI accelerates how weaknesses are found and exploited, defenders need security operations that can act earlier with better context and more confidence.

That means connecting exposure management with detection and response, so teams understand which risks are exploitable, which assets matter most, what suspicious behavior is already present, and which actions will reduce risk fastest. It also means using AI carefully and practically, not as a replacement for security judgment, but as a way to reason across telemetry, reduce noise, support investigation, and help teams make decisions at the speed the threat environment now demands.

AI-enabled defense is becoming part of resilience planning, especially for organizations running critical systems or high-value digital infrastructure. The goal is to give defenders the speed, context, and consistency to operate inside the attacker’s decision cycle, without removing the judgment and accountability that effective security requires.

The OpenAI and Hugging Face incident will continue to generate debate as more details emerge, but defenders already have enough to work with. Agentic systems are beginning to test the seams between AI research, software supply chain security, cloud infrastructure, and incident response. The organizations best positioned for what comes next will be the ones making those seams visible, monitored, and resilient before the next incident puts them under pressure.

How to navigate the AI talent wars

Thu, 23 Jul 2026 15:06:19 +0200

Block did the same thing. Beat guidance, raised outlook, cut 4,000+ jobs. Both framed it as architecting for the AI era.

This is not a contradiction. This is the new math boards are running. And if you’re a CIO who hasn’t started running it yourself, you’re behind.

The benchmark has moved

Boards aren’t comparing you to your 2019 self anymore. They’re comparing you to Anthropic.

Finding bodies to fill seats has never been easier. Finding people who operate at that level of leverage is a different problem entirely.

‘Acqui-hires’ are a shortcut with a hidden cost

But there’s a cost that doesn’t show up in the acquisition price.

The CIO’s real problem

Closing the gap without slowing down

The AI talent wars are not primarily a recruiting problem. They’re a rethinking of what organizations are supposed to look like.

Talent density is the new headcount model. The sooner your governance, your tooling and your board conversations reflect that, the better positioned you’ll be when the next efficiency report lands.

This article is published as part of the Foundry Expert Contributor Network.
Want to join?

Q&A: Google’s AI and computing chief talks about its shapeshifting data centers

Thu, 23 Jul 2026 14:55:21 +0200

Google’s AI offerings span its internal and cloud offerings. Its data centers are processing seven times more AI tokens compared to last year. To keep up, Google is upgrading its data-center hardware and software technologies at a faster clip. It plans to raise $80 billion to build new data centers. (See related story: Google transforms its data center architecture for agent era)

Network World spoke with Mark Lohmeyer, vice president and general manager of AI and computing at Google, about how the company’s infrastructure is keeping pace with AI demand.

Network World: What is the primary shift in infrastructure needs?

Mark Lohmeyer: We’ve seen the rise of agents and agentic use cases. Years ago, it was the chat phase: Ask a question, get an answer. Now we’re in the agentic era, where you express your intent, agents spin off multiple sub-agents, working in parallel, preserving state. This is a radical shift in what infrastructure needs to do; make them fast, cost effective, secure, reliable. We’re delivering infrastructure optimized for the age of agents.

NW: What’s the goal of the infrastructure buildout, and what should customers expect regarding costs?

ML: Ultimately, it’s about enabling customers with leading-edge capabilities and models at scale cost-effectively. With agents, inference transactions increase by 50x, 100x versus non-agentic workloads. We’re driving the cost per transaction down exponentially. In our latest platforms, we reduce the cost by almost 2x for the same work. Customers serve twice the number of users at the same cost, directly driving profitability.

NW: How are you addressing energy efficiency?

ML: Energy is a critical resource, and Google has optimized for years. We design data centers and compute [to drive] high PUE (power usage effectiveness). We introduced liquid cooling over five years ago, and these latest systems are all liquid cooled. For agentic workloads, CPUs come to the forefront… orchestrating agents, calling tools, doing evaluation loops in reinforcement learning. Our latest Axion-based CPU platform called N4A has energy efficiency and is significantly better than the prior generation and x86 comparables.

NW: How do you think about token efficiency as you build-out systems?

ML: Performance and efficiency gains are powered by co-design of the model and infrastructure. Gemini is trained on TPUs, primarily served on TPUs with high frontier model capability, in a token and cost-efficient way. This stems from co-design across the full stack.

NW: How do you project what infrastructure will be needed years in advance?

ML: Hardware cycles deliver a new next generation roughly every year, but design cycles are two years or more in advance. We work with DeepMind doing core research, to application teams taking models into production, to billions of users, to our team building infrastructure. We work upstream with DeepMind and application teams to understand what’s coming. Agents weren’t being broadly spoken of externally, but internally we had those insights around what they would need. That shows up in hardware design. We hit the timing right — these platforms are built for agents.

NW: What’s the eighth generation TPU platform?

ML: We deliver new platforms every year, and ones launched years ago are close to 100% utilized because demand for AI-optimized compute is high. The eighth-generation TPU platform is the first delivering two complete systems, from the chip all the way up to the network and storage and software, that are optimized.

TPU-8t is optimized for training, and TPU-8i is optimized for inference. For TPU-8i, we increased SRAM on the chip to 384MB — three times the prior generation — and increased the HBM by 50%.

NW: How are you approaching GPU and TPU compatibility?

ML: People in a single cluster do not commingle GPUs and TPUs. We offer both options based on specific workload needs. We’ve been investing on the TPU side in using software frameworks customers are comfortable with on GPUs and enabling those on TPUs. For example, PyTorch and vLLM. Customers could have a pool of GPUs and TPUs, running vLLM on top of that. Start with a workload on TPUs, but if the TPU pool is fully utilized, spill to GPUs or vice versa. This works because it’s all leveraging the same compatible software layer on top.

NW: How has the orchestration platform changed for agents?

ML: Kubernetes is becoming the orchestration platform of choice for AI. Google is transforming GKE [Google Kubernetes Engine] into an agent-native orchestration solution. When expressing intent to an agent and it spins up multiple sub-agents, compute needs to spin up rapidly — TPUs or GPUs — without long delays, then run and spin back down. We’re optimizing at every layer of the GKE stack: significantly improving node startup time and how rapidly we start and stop containers. Lovable demonstrates this with GKE, spinning up hundreds of sandboxes for live coding sessions on their platform in parallel, paying for infrastructure when needed.

NW: What is the role of the network and storage infrastructure?

ML: The network is critical for AI. This requires creating large-scale clusters of GPUs or TPUs and enabling them to talk to each other in a high-performance way. We created the Virgo network — a collapsed network architecture, non-blocking within a data center, where multiple pods or NVLink72 domains connect together.

In TPU8T, we can connect over a million TPUs together leveraging Virgo, creating large-scale, high-performance, reliable clusters that shrink innovation cycles. Storage is equally critical. In large-scale clusters, something is always failing. The ability to take snapshots and go back to a checkpoint is important.

We’ve introduced Managed Lustre 10T, with 10 terabytes per second of bandwidth, 18 petabytes of storage in single clusters. This is 10 times faster than last year and 20 times faster than competition. We have Rapid Bucket, low-latency storage backed by Google storage systems. Both are impactful in large-scale training environments.

NW: How does KV cache strategy differ between training and inference?

ML: For TPU-8i, we increased SRAM on the chip to 384 megabytes — three times the prior generation — and increased the HBM by 50%. Storing KV cache directly in chip memory allows responding to inference requests much more rapidly and cost-effectively than going to an external system. For inference workloads, storing as much KV cache as possible on-chip is critical.

We’re introducing a dedicated KV cache storage subsystem that works across GPUs and TPUs. As KV caches get larger, being able to fall back to this dedicated subsystem becomes critical. Loading model weights rapidly is important in dynamic inference environments where accelerators switch between models hour by hour.

We Asked an Independent Lab to Time Us. Here’s What They Found.

Thu, 23 Jul 2026 14:32:09 +0200

A new Principled Technologies benchmark tested VMware Data Services Manager 9.1 against manual PostgreSQL operations across five real world scenarios. It’s Tuesday afternoon. A developer submits a ticket requesting a new PostgreSQL HA cluster for a pre-production environment. Your DBA opens vCenter, deploys three VMs from the gold template, SSHs into each node, regenerates machine … Continued

The post We Asked an Independent Lab to Time Us. Here’s What They Found. appeared first on VMware Cloud Foundation (VCF) Blog.

Attackers Weaponize GitHub Actions Runners to Target cPanel and WHM Servers

Thu, 23 Jul 2026 14:16:03 +0200

Cybersecurity researchers have shed light on a large-scale campaign that has turned compromised GitHub repositories into distributed attack infrastructure designed to target cPanel and WebHost Manager (WHM) instances. The activity involves malicious Packagist development versions spanning 10 packages associated with a legitimate PHP and DevOps developer, dinushchathurya, between July 12 and 13,

The new value architecture of the AI-native SaaS era

Thu, 23 Jul 2026 14:05:04 +0200

The traditional methods of measuring success no longer tell the full story. Here’s what should replace them — and why.

In brief:

AI is transforming software as a service (SaaS), and the old ways of keeping score no longer apply.
Smart companies are evolving new metrics that provide deeper insight into how AI-native software is performing in a new marketplace.
These changes impact everything from pricing to valuations.

The transformation of the software-as-a-service (SaaS) industry toward AI-native operating companies is rapidly changing the unit of value across the industry.

Why do software companies need new metrics?

Why the rethink, and why now? There are five major forces that are driving this shift:

The unit of value is changing. Seats measured who could access software, and credits measure what the software actually does. But in an AI-native world, agents don’t have seats; they have workloads. Over the past 18 months, every major SaaS platform has moved to some forms of credit or consumption unit.
The cost of goods sold (COGS) is exploding. AI inference adds real per-unit costs that scale with usage. In an AI-native world, software companies can’t scale to infinite users at near‑zero marginal cost as before.
Buying is moving up the org chart. AI-native applications shift purchasing to higher-level operators — such as line-of-business leaders or chief operating officers — which expands the market from software budgets to labor budgets. And because AI agents replace services as well as software, the total market opportunity is 3x to 10x larger than traditional SaaS.
Time to value (TTV) is collapsing. With AI-native tools, customers start seeing meaningful results in weeks rather than quarters. Onboarding and setup are fast, workflows are pre-built, and there’s no need for extensive customer success or professional services — dramatically reducing implementation time and costs.
Retention is bifurcating. AI forces clarity in a way that traditional SaaS couldn’t. Products that can provide value become even “stickier” and retain customers. Those that don’t churn faster. In an AI-native marketplace, the middle disappears.

How this shift is impacting pricing

Given how AI-native software is transforming the market, the shift to more variable pricing options is inevitable.

How AI upends classic SaaS metrics

Revenue composition

Committed credit annual recurring revenue (ARR) vs. burndown ARR: Measuring the credits sold on annual commitment vs. those consumed and replenished. This is the single most important split for valuation. Committed credits behave like subscription and burndown behaves like usage.
Credit utilization rate: The percentage of purchased credits consumed per period. This is a leading indicator of renewal sizing.
Credit burn velocity: How fast is a customer consuming their credits, and is that consumption increasing or decreasing quarter over quarter? This metric predicts expansion or contraction before it shows up in ARR.
Effective price per credit: The real revenue per credit after discounts, overage and rollover, which can detect revenue leakage and help companies set smarter guide rails.

Margin reality

Credit margin: The gross profit the company earns per credit after subtracting inference costs. This is the core economic unit for AI-native, usage-based businesses — the replacement for gross margin per seat used in SaaS.
Inference-adjusted gross margin: By carving out AI inference costs separately in the P&L statement, you can see true AI margins, avoid hiding deterioration inside blended SaaS margins, and clearly distinguish AI economics from legacy SaaS economics.
Compute leverage ratio: This metric measures how efficiently the business converts compute spend into revenue. It shows whether your AI margins are improving as you scale.
AI-adjusted “Rule of 40”: This updated metric recalibrates the traditional growth and profitability benchmark to account for AI’s lower gross margins and variable inference costs, giving a more accurate picture of business health for AI-native companies.

Behavioral and value signals

Time-to-first outcome: Replaces traditional onboarding metrics. Tracks how fast a customer reaches their first measurable result.
Adoption: AI-native adoption is measured by workflow penetration and active agent density, not seat count. As AI replaces human-driven usage, the unit of adoption shifts from people to automated workflows and agents.
Net credit retention (NCR): Credit-volume retention across the customer base, tracked separately from net recurring revenue to avoid price-change impact.

What does this mean for enterprise value calculations?

As the internal metrics of success change, so do the ways the investment community measures growth and long-term viability.

What this means for leaders, boards and investors

The views reflected in this article are the views of the author and do not necessarily reflect the views of Ernst & Young LLP or other members of the global EY organization.

This article is published as part of the Foundry Expert Contributor Network.
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8.000 PCs über Steam infiziert: Cyberkriminelle verteilen fast zwei Jahre Malware über Fake-Games

Thu, 23 Jul 2026 13:50:38 +0200

Cyberkriminelle haben Steam genutzt, um zahlreiche PCs mit Malware zu infizieren. Sie haben die Schadsoftware in Spielen versteckt und sie bei bestimmten Gamer:innen beworben. Welche Schäden sie damit angerichtet haben. weiterlesen auf t3n.de

Google-Verstöße gegen DMA: EU verhängt insgesamt 890 Millionen Euro Strafe

Thu, 23 Jul 2026 12:51:24 +0200

Seit Monaten untersucht Brüssel mögliche Verstöße von Google gegen den Digital Markets Act. Jetzt wurden Strafen über zusammen fast 900 Millionen Euro verhängt.

Google-Verstöße gegen DMA: EU verhängt insgesamt 890 Millionen Euro Strafe

Thu, 23 Jul 2026 12:51:04 +0200

Seit Monaten untersucht Brüssel mögliche Verstöße von Google gegen den Digital Markets Act. Jetzt wurden Strafen über zusammen fast 900 Millionen Euro verhängt.

Streamingabo: Interessenten können Netflix wieder kostenlos ausprobieren

Thu, 23 Jul 2026 12:50:46 +0200

Nach fast sechs Jahren können Interessenten in Deutschland Netflix wieder kostenlos testen. Der Probezeitraum variiert neuerdings. (Netflix, Streaming)

The RAR Mystery: Breaking RAR4 and RAR5 Encryption

Thu, 23 Jul 2026 12:21:16 +0200

RAR encryption has an unusual reputation: in more than twenty years there has been no practical attack against the cipher, no implementation flaw worth the name, and none of the shortcuts that collapsed certain other archive formats in minutes. Yet until recently, nobody could say how fast a RAR password could actually be attacked. The […]

Cloud-Ausfall würde fast jedes zweite Unternehmen lahmlegen

Thu, 23 Jul 2026 12:20:23 +0200

Ein Ausfall zentraler Cloud-Dienste könnte große Teile der deutschen Wirtschaft innerhalb kurzer Zeit zum Stillstand bringen. Nach einer aktuellen Bitkom-Umfrage müssten 46 Prozent der Unternehmen ihren Geschäftsbetrieb früher oder später einstellen, wenn ihre Cloud-Anwendungen nicht mehr erreichbar wären. Jede elfte Firma müsste sofort aufgeben Neun Prozent der befragten Unternehmen geben an, bereits unmittelbar nach dem […]

The post Cloud-Ausfall würde fast jedes zweite Unternehmen lahmlegen first appeared on ifun.de.

Verrückte WM-Idee: Fotograf nutzte fast 100 Jahre alte Kamera

Thu, 23 Jul 2026 11:43:19 +0200

Ein Bild pro Minute statt Tausende Aufnahmen pro Spiel. Warum sich dieser Sportfotograf bewusst dagegen entschied, die Fußball-WM 2026 mit einer Digitalkamera zu fotografieren.

WSL container: A quiet revolution for Windows development

Thu, 23 Jul 2026 11:07:20 +0200

Running containers on Windows has never been as easy as it should be. While there are versions of Docker Desktop and Podman that work with both the Windows Subsystem for Linux (WSL) and Hyper-V, I’ve found both overly complex and unstable. Where they have worked, it’s turned out that Hyper-V has been the best option, using a Linux virtual machine to host my containers. That all adds up to overhead, layers of virtual infrastructure that get in the way of work and that need to be rebuilt every time I restart my PC.

Part of the problem is WSL. It’s a good tool, but WSL2’s file-system integration is slow, and you’re left having to work with code using Visual Studio Code’s remote integration, which means putting a VS Code Server in every container you’re building and testing. If you’re working with Kubernetes, that’s even more complexity that needs to be managed, dragging you away from code.

I ended up running most of my container testing and development from a separate machine, a Linux server running containerd. But though it worked (and had all the resources of workstation-class device), it wasn’t portable, and for some reason I’ve yet to uncover, Ubuntu’s remote desktop access doesn’t work for me.

So, it was good to see Microsoft make several announcements around WSL at Build 2026 as part of a push to make Windows a developer platform again. The first, an improved WSL3, is still some way away, but the second, WSL-native container support, shipped at the end of June. It is already seeing community-driven development of Docker Desktop-like tooling to help monitor and manage your containers.

Delivering a WSL-based container platform fits in with the other developer-focused Windows announcements at Build. Making Windows behave more like Linux is Microsoft responding to developer needs, given that more than 50% of servers on Azure run a Linux distribution. Linux is the basis of cloud-native infrastructure, so developers need to be able to build on it wherever they are.

Getting started with WSL container

WSL container provides a new CLI that works in parallel to the familiar WSL, with commands to support the entire container life cycle, from creation to shut down. All you need to do to get started is upgrade your WSL installation to the current pre-release build (at the time of writing this was 2.9.3). Simply open an administrator PowerShell terminal and enter wsl --update --pre-release.

This downloads and installs the latest WSL release. Once you’ve closed and re-opened your terminal (to ensure that you’ve updated its context) you can check that WSLC has installed by entering wslc, which should list the available commands. The new CLI is aliased to WSL container, if you prefer to keep your container work separate from WSL (and avoid typos that might accidentally affect your WSL installations).

Under the hood Microsoft is using WSL container to trial new integration points for Linux in Windows. One key change is the use of a new file system that significantly speeds up access to Windows from inside a container. Another improvement gives WSL container a new networking mode that relays networking connections directly through the Windows network stack, ensuring it has access to the same resources and security as Windows.

Calling Linux containers from Windows applications

Things get more interesting when you start to use the WSL container API from inside your Windows code. Here you can include calls to Linux containers inside your desktop applications, taking advantage of existing services, building and deploying containers from inside your CI/CD pipeline. Using the new file system and networking stack helps reduce the friction that comes with crossing the boundaries between the two platforms.

The WSL container API is available as a NuGet package, with support for C, C#, and C++. It allows your code to start and stop containers, and interact directly with them, sending command-line calls and reading back responses. Where things get interesting is being able to launch a containerized service from your code, exposing its REST or gRPC APIs on a local network port. Microsoft has provided sample code to show you what’s possible at this early stage.

Microsoft is doing something revolutionary here. It’s taking the cloud-native, service-driven model and bringing it into Windows and using it to bridge decades of divergent development. You no longer have to rewrite a service that works on Linux to run in Windows; all you need to do is containerize the service and launch it from the WSL container API. When you’re done, the API will tidy up after you, shutting down the container and reclaiming the memory it used.

It’s important to remember that this is only the first public preview of a rapidly developing platform. There are many opportunities here to, say, build on the syscall translation layer developed for WSL1 to produce a native Windows-to-Linux application integration stack that removes the overhead of using web-based service calls. It will be interesting to see what develops, but this first release is very interesting indeed.

Manage Linux containers from Windows

If you want a Docker Desktop-like experience for building and testing containers on Windows developer hardware, you may not have long to wait. WSL container’s underlying API is already being used to build tools that manage and monitor containers for you. One such tool is the WSL Container Desktop, under development on GitHub. While there aren’t any release builds yet, it’s easy enough to compile and get running by cloning the source repository and building using the .NET CLI. You do need to have the Windows App SDK installed, and some features require access to the Azure CLI.

WSL Container Desktop is built in C#, with a WinUI front end. It’s currently only verified for use on x64, though I was able to compile and run it on an Arm64 PC and use it to test and run containers. Once running, it gives you a well-designed front end for your WSL-hosted containers, showing what’s running and what resources they are using. You can link WSL Container Desktop to container registries, like Docker’s and Azure’s, so you can quickly pull base containers and then use the WSL container environment to add your own code and customizations.

Your main interaction point is the WSL Container Desktop dashboard, which shows what containers are running and their current resource usage. Elements are displayed in cards, taking a cue from Windows’ own user interface and especially from its Settings app. From the dashboard, you can drill down into the available containers, with quick start, stop, and reload options, as well as an extended memory that includes the ability to open a web browser to the appropriate port. I tested this with a container that included an entire KDE webtop, giving me a Linux distro running in a container in my browser.

Other options include a details view that displays current logs and provides tools for inspecting the state of a container. This is the type of tool that comes in useful when debugging and testing container applications, as it can provide insights that the WSL container CLI doesn’t offer. Another option helps you clean up after you’ve downloaded an image and don’t need it anymore, with analytics that show the largest images and images you haven’t used for some time. On top of its tooling for working with WSL containers, WSL Container Desktop provides a basic settings tool that helps you configure its look and feel, as well as how it integrates with Windows.

Run Kubernetes inside Windows for cloud-native development

One of the more useful features of WSL Container Desktop is the ability to quickly stand up a K3s Kubernetes instance in WSL that can be used to host WSL containers, providing a local environment to build and test cloud-native applications wherever you might be. The K3s tooling offers a similar experience to the Kubernetes project’s own Headlamp UI, making it easy to go between your development environment and a production Kubernetes cluster.

It’s fair to describe WSL container as one of those Windows features you didn’t think you needed, but now it’s here you can’t live without it. WSL container simplifies building a container development tool chain in Windows, and at the same time allows you to think about a new generation of hybrid applications that take advantage of decades of development in both Windows and Linux.

The result is something that was unimaginable a few years ago: dropping a Linux container into the middle of a Windows application and treating it as another local service. As the WSL container platform evolves, you should expect to see more ways of bringing Linux and Windows together, using containers to deliver a hybrid platform that gives us the best of both worlds at long last.

Krieg der Sterne: Wie Hotels und Restaurants ihre Google-Bewertungen frisieren

Thu, 23 Jul 2026 11:05:54 +0200

Britain isn't considering datacenters' thirst for water in its 'AI superpower' ambitions

Thu, 23 Jul 2026 10:33:43 +0200

Grand compute push could end up as dry as England's reservoirs unless ministers act fast

CISOs vertrauen ihrer Abwehr, kaum jemand testet sie - Security-Insider

Thu, 23 Jul 2026 09:42:42 +0200

Horizon3.ai-Report „The State of Assumed Security" Fast alle CISOs vertrauen ihrer Cybersecurity – zurecht? 23.07.2026 Ein Gastbeitrag von Dan Bird 4 ...

Ihr räumt euer Android-Handy auf und der Speicher ist trotzdem voll? Dann macht ihr einen Fehler, den fast jeder macht

Thu, 23 Jul 2026 09:35:32 +0200

[Stable Update] 2026-07-23 - Kernels, Mesa, VirtualBox, Gambas3, COSMIC, Plasma, KDE Frameworks

Thu, 23 Jul 2026 09:31:27 +0200

Hello Manjaro user community, here we have another set of package updates. We are continuing our development of the upcoming release of ‘Bian-May’ which can be expected end of July or beginning of August. Development speed may be a little slower the upcoming weeks. However, still let us know any issues you may found thus far.

Current Promotions

Get the latest Gaming Laptop by Slimbook powered by Manjaro: Slimbook Manjaro III
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Recent News

New in Manjaro GNOME! Or, if you prefer the command line: (click for more details) KDE Plasma users with SDDM can now migrate to Plasma Login Manager (click for more details) NVIDIA 590 driver drops Pascal support (click for more details)

Manjaro 26.1 Bian-May - Preview released
Manjaro 26.0 Anh-Linh released
Manjaro Summit public Alpha now available
As of Linux 5.4.302, the 5.4 series is now EOL (End Of Life). Please install 5.10 LTS (Long Term Support) or 5.15 LTS.
As of Linux 6.16.12, the 6.16 series is now EOL (End Of Life). Please install 6.18 LTS (Long Term Support) and/or 6.12 LTS.
As of Linux 6.17.13, the 6.17 series is now EOL (End Of Life). Please install 6.18 LTS (Long Term Support) and/or 6.12 LTS.
As of Linux 6.19.14, the 6.19 series is now EOL (End Of Life). Please install 7.0, and/or 6.18 LTS (Long Term Support) and/or 6.12 LTS.
As of Linux 7.0.14, the 7.0 series is now EOL (End Of Life). Please install 7.1, and/or 6.18 LTS (Long Term Support) and/or 6.12 LTS.

Previous News (click for more details)

Notable Package Updates

Additional Info

Python 3.14 info (click for more details) Info about AUR packages (click for more details)

Get our latest daily developer images now from Github: Plasma, GNOME, XFCE. You can get the latest stable releases of Manjaro from CDN77.

Our current supported kernels

linux61 6.1.177
linux66 6.6.144
linux612 6.12.96
linux618 6.18.39
linux71 7.1.4
linux72 7.2.0-rc4
linux61-rt 6.1.167_rt62
linux66-rt 6.6.135_rt74
linux612-rt 6.12.89_rt18

Package Changes (7/22/26 05:45 CEST)

stable core x86_64: 71 new and 71 removed package(s)
stable extra x86_64: 1982 new and 2071 removed package(s)
stable multilib x86_64: 32 new and 32 removed package(s)

A list of all package changes can be found here.

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Oracle July 2026 Patch Fixes 1,434 CVEs Across 334 Products

Thu, 23 Jul 2026 09:11:07 +0200

Oracle has released its July 2026 Critical Patch Update, delivering one of its largest quarterly security releases to date. The latest Oracle security patch addresses more than 1,400 vulnerabilities across hundreds of products, with the company indicating that artificial intelligence likely played a significant role in identifying most of the flaws. According to Oracle, the July 2026 Critical Patch Update contains 1,449 security patches, covering 1,434 unique Common Vulnerabilities and Exposures (CVEs) across 334 products.

July 2026 Critical Patch Update Covers Hundreds of Oracle Products

The latest Oracle security patch spans a wide range of enterprise products and platforms. Among the affected products are Database Server, Oracle APEX, Autonomous Health Framework, Essbase, Global Lifecycle Management, GoldenGate, NoSQL Database, Spatial Studio, SQL Developer, TimesTen In-Memory Database, Application Testing Suite, Commerce, Communications, Construction and Engineering, and E-Business Suite. The July 2026 Critical Patch Update also includes security fixes for Enterprise Manager, Financial Services Applications, Food and Beverage Applications, Fusion Middleware, Analytics, HealthCare Applications, Hospitality Applications, Java SE, JD Edwards, MySQL, PeopleSoft, Retail Applications, Siebel CRM, Supply Chain, Systems, Utilities Applications, and Virtualization. By addressing vulnerabilities across such an extensive product lineup, the Oracle security patch aims to reduce the risk posed by security weaknesses that could affect organizations running Oracle technologies in production environments.

Hundreds of Vulnerabilities Can Be Exploited Remotely

A notable aspect of the July 2026 Critical Patch Update is the number of flaws that attackers could potentially exploit without requiring authentication. Oracle stated that roughly 600 of the patches fix vulnerabilities that can be exploited remotely by unauthenticated attackers. In addition, hundreds of the addressed security flaws have been assigned critical severity ratings, emphasizing the importance of applying the latest Oracle security patch without delay. Among Oracle's products, the highest number of vulnerabilities were addressed in:

E-Business Suite: 410 vulnerabilities
Fusion Middleware: 355 vulnerabilities
Communications: 168 vulnerabilities
PeopleSoft: 84 vulnerabilities

These figures highlight that some of Oracle's most widely deployed enterprise applications received a significant share of the security fixes included in the quarterly update.

AI-Driven Vulnerability Discovery Appears to Have Played a Major Role

One of the most notable aspects of the July 2026 Critical Patch Update is Oracle's growing use of artificial intelligence for security research. Only a few dozen of the vulnerabilities included in the release were credited to external security researchers. This indicates that the overwhelming majority of the discovered flaws were identified internally, likely with the assistance of AI-driven vulnerability analysis. Earlier this year, Oracle disclosed that it has access to leading artificial intelligence systems, including Anthropic's Claude Mythos and OpenAI's most capable models. According to the company, these AI technologies are being used to accelerate vulnerability discovery and improve the speed and accuracy of security patch development. Oracle also said it is applying this AI-driven vulnerability approach across its own software and cloud services, Oracle Health offerings, and the open source components that it both develops and depends on.

Organizations Urged to Apply the Oracle Security Patch Promptly

The release of the July 2026 Critical Patch Update comes amid continued efforts by threat actors to exploit vulnerabilities in enterprise software before organizations can deploy security updates. Oracle product vulnerabilities have previously been targeted in real-world attacks. The company cited examples that include the exploitation of a PeopleSoft zero-day vulnerability as well as a recently patched Oracle E-Business Suite (EBS) vulnerability. Given the number of remotely exploitable and high-severity issues resolved in the Oracle security patch, organizations using affected Oracle products are advised to install the updates as soon as possible. Prompt deployment can help reduce exposure to attacks that take advantage of publicly known vulnerabilities before systems are secured. With 1,449 security patches addressing 1,434 unique CVEs across 334 products, the July 2026 Critical Patch Update represents one of Oracle's most extensive quarterly security releases.

Microsoft’s 3-day patching directive comes with added operational risk

Thu, 23 Jul 2026 09:10:44 +0200

Microsoft 365 Director Jeremy Chapman this month took to video to tell Windows admins that the days of delaying security patches are over.

Complex enterprise systems and historic incidents involving patch problems have caused many admins to hold fire on immediately applying security patches, in many cases deferring patch rollouts for two to four weeks or more to ensure stability. Microsoft argues that this cautious approach, though understandable, is no longer viable because AI is accelerating the discovery and exploitation of software vulnerabilities.

As a result, Microsoft has advised admins to act on patches within three days.

Independent experts agree with Microsoft’s diagnosis of the problems posed by AI-powered vulnerability discovery, but many say Microsoft’s three-day remediation window is unrealistic for large enterprises with heavy testing, change-control, and compatibility constraints.

Instead of taking a blanket approach, enterprises need to focus more on quickly resolving those vulnerabilities that are under active exploitation and relevant to their environments, according to critics of Microsoft’s revised approach.

Tighter patching deadlines

Microsoft’s revised vulnerability remediation advice comes in the wake of its work with Anthropic’s Project Glasswing and findings from Microsoft’s own MDASH multi-model agentic scanning harness. Tighter patching deadlines are configurable via Windows Autopatch and Microsoft Intune or update tooling options such as Microsoft Configuration Manager and Windows Server Update Services.

As IT environments become increasingly more complex, inadvertent issues can occur with what appears to be a simple patch.

Unique or complex deployments may not be compatible with a patch, resulting in potential data corruption, system shutdown, or the dreaded “Blue Screen of Death.” Multiple vendors in the operating system and the enterprise software and security market have released patches that have broken products and caused outages, so the issue goes well beyond Windows shops.

Increasing both the volume and the speed of patching is unsustainable for most security teams because organizations are already struggling with successful remediation as it is.

“Many organizations have patch windows, review cycles, and test environments to identify these issues prior to patching production environments,” says Scott Caveza, senior research manager at exposure management and vulnerability assessment firm Tenable. “Organizations lacking the resources for extended validation risk deploying faulty patches that cause downtime or force last-minute configuration changes.”

Caveza adds: “The mitigation steps will vary for each organization, but blindly relying on auto-updates without contextual validation is not a defensible security posture.”

CISA’s Known Exploited Vulnerabilities list and other industry data suggest that only a small fraction of disclosed vulnerabilities are confirmed as exploited in the wild.

“[Enterprises should focus on] identifying vulnerabilities with credible and functional PoCs, verified exploitation, or sustained attention from ransomware groups, threat actors, and botnets,” says Caitlin Condon, vice president of security research at VulnCheck. “Timely exploit intelligence helps organizations identify the bugs that require immediate attention, while allowing lower-risk issues to proceed through appropriate testing and change control.”

Other independent experts are more sympathetic to Microsoft’s argument that AI has made vulnerability discovery and exploit development faster than ever and, as a result, the risks of delaying patches are far greater.

“Organizations sometimes delay patches to protect the uptime of critical systems, and many updates still require a restart,” says Danny Jenkins, CEO and co-founder at endpoint protection technology vendor ThreatLocker. “Some teams also stay one update cycle behind because they are concerned that a new patch could introduce bugs or break an overlooked dependency. Unfortunately, delaying patches to preserve uptime is becoming much harder to justify.”

Jenkins adds: “Organizations should not leave critical systems exposed while waiting for the next maintenance window. Patches should still be tested, but that process needs to move quickly, with the highest priority given to vulnerabilities that are actively exploited or exposed to the internet. A controlled interruption is usually far less costly than a successful attack exploiting a known vulnerability.”

Wider cross-industry impact

Microsoft’s three-day recommendation reflects a fundamental change in the threat landscape. Other vendors might be expected to follow suit and that means CISOs need to revise their approach to vulnerability remediation.

“Organizations should expect faster disclosure-to-exploitation timelines to become the norm, which means security programs must emphasize automation, trusted software supply chains, and continuous visibility rather than relying on periodic maintenance windows,” says Mike Nelson, VP and field CTO at DigiCert.

AI is compressing the time between vulnerability discovery and exploitation, and the industry is moving rapidly from 30-, 60-, and 90-day patching windows toward a matter of days.

However a “blanket three-day requirement for every vulnerability is neither realistic nor safe for most large organizations,” says Jeff Williams, founder and CTO at Contrast Security.

Failing to patch opens up security threats, but rushing an inadequately tested patch into production creates operational risk.

“The goal cannot be to treat every CVE [vulnerability] as an emergency,” according to Williams. “It has to be identifying, within hours, which vulnerabilities are actually exploitable and require immediate action.”

Holistic remediation

Security teams are already facing significant pressure to patch faster and to remediate a rising tide of new vulnerabilities, yet many practitioners are losing ground. Verizon’s Data Breach Investigation Report, published earlier this year, found that the median time to patch had actually increased to 43 days.

Patch deployment in enterprise environments involves configuration changes, reviews, testing, and validation.

Enterprises need to become more proficient at exposure management so that they have a holistic view of their environment that’s necessary to identify which assets are at greatest risk.

“By pinpointing the misconfigurations, identity flaws, and specific vulnerabilities that pose the greatest risk to their environment, security teams can prioritize exactly what to patch first,” Tenable’s Caveza says. “The idea of ‘patch everything’ is really outdated, and ‘patch faster’ isn’t feasible with the rapidly increasing number of vulnerabilities disclosed each day.”

CISOs will have to re-engineer their vulnerability and exposure management processes. “The traditional model of scanning everything, assigning generic severity scores, and tilting at a massive and expanding backlog is no longer fast enough,” says Contrast Security’s Williams.

Organizations need to identify the small number of vulnerabilities that matter, protect against them immediately, and remediate them on a timeline the business can safely support.

Enterprises should prioritize on resolving “internet facing, remotely exploitable vulnerabilities and any of the CISA Known Exploited Vulnerability list,” says Jose Lejin, an IEEE senior member.

Businesses that cannot safely validate and deploy patches within three days still have options, including “compensating controls, reducing an asset’s exposure, or in some cases removing the component entirely, all of which shrink the exploitable risk and buy time to patch properly,” says Brad Hibbert, CSO of vulnerability management provider Brinqa.

Hier gibt es die Midea PortaSplit Donnerstags um 15 Uhr zu kaufen

Thu, 23 Jul 2026 09:03:31 +0200

Ganz Deutschland sucht nach Abkühlung, doch die effizienteste mobile Klimaanlage ist überall vergriffen – fast überall.

Fast alle CISOs vertrauen ihrer Cybersecurity – zurecht?

Thu, 23 Jul 2026 08:55:04 +0200

Moderne Sicherheitsprogramme sind hochaktiv: Assets werden gescannt, Schwachstellen priorisiert, Patches eingespielt, Dashboards aktualisiert. Lange galten diese Aktivitäten als Nachweis für ein hohes Sicherheitsniveau. Aber die Frage ist doch: Messen die Unternehmen tatsächlich ihre Fähigkeit, einen realen Angreifer zu stoppen oder lediglich den Abschluss von Arbeitsprozessen?

France Passes Social Media Ban For Under-15s

Thu, 23 Jul 2026 08:54:58 +0200

French parliament overwhelmingly approves fast-tracked law barring children under 15 from social platforms starting in September This article has been indexed from Silicon UK Read the original article: France Passes Social Media Ban For Under-15s

The post France Passes Social Media Ban For Under-15s appeared first on IT Security News.

Valve kurz vor Bankrott: Wie ein Praktikant das Steam-Unternehmen vor über 20 Jahren rettete

Thu, 23 Jul 2026 08:38:11 +0200

Vor mehr als 20 Jahren stand Valve fast vor dem Bankrott. Der Grund dafür war ein Rechtsstreit, den das Unternehmen fast verloren hätte. Doch ein Praktikant konnte das Aus noch verhindern.weiterlesen auf t3n.de

macOS 27 Public Beta 2 Released: Here’s How to Install It

Thu, 23 Jul 2026 08:17:18 +0200

Apple has released macOS 27 Golden Gate public beta 2 for compatible Mac models. The latest test update arrives shortly after developer beta 4 and focuses on improving stability before the full release later this year. Since this is pre-release software, some apps and features may not work correctly. Back up your Mac before installing the update, especially if you plan to use it on your main computer. How to install Follow these steps to download the update: Visit the Apple Beta Software Program website and sign in with your Apple Account. Enroll your Mac in the public beta program if you have not already done so. Open System Settings on your Mac. Select General and click Software Update. Click the information button next to Beta Updates. Choose macOS 27 Golden Gate Public Beta from the menu. Click Done and return to the Software Update page. Select Update Now or Upgrade Now when macOS 27 public beta 2 appears. Keep your Mac connected to power during the installation. The download size and installation time will depend on your Mac model and internet connection. All changes in macOS 27 public beta 2 Apple has not shared a detailed list of user-facing changes for this public beta. The update appears to focus mainly on bug fixes, performance improvements, and stability changes introduced with the corresponding developer beta. Improved system stability: The release includes additional fixes intended to reduce crashes and unexpected behaviour during daily use. Performance refinements: Apple continues to improve responsiveness across macOS, including animations, app launches, and general navigation. Liquid Glass adjustments: The update contains further interface refinements as Apple improves the appearance and readability of its Liquid Glass design. Siri AI testing: Apple continues testing its updated Siri experience, including more natural conversations, richer answers, and deeper Apple Intelligence features. App compatibility fixes: Public beta 2 should address some issues affecting third-party apps, although users may still encounter software that has not been updated for macOS 27. More changes may appear as users spend time with the update. Apple will continue releasing additional beta versions before macOS 27 Golden Gate becomes available to everyone later this year. If you’ve already installed the update, let us know your experience in the comments.

iPadOS 27 Public Beta 2 Now Available, Here’s How to Install It and What’s New

Thu, 23 Jul 2026 08:17:15 +0200

Apple has released iPadOS 27 public beta 2 for compatible iPad models, one week after the first public beta arrived. The update matches the latest developer beta and brings several smaller features, interface refinements, and bug fixes. Anyone enrolled in the free Apple Beta Software Program can download the update through the Settings app. Since this remains pre-release software, users should back up their iPad before installing it. How to Install iPadOS 27 Public Beta 2 Follow these steps to download the latest public beta: Back up your iPad using iCloud or a Mac. Enroll your Apple Account in the Apple Beta Software Program if you have not already joined. Open the Settings app on your iPad. Select General. Tap Software Update. Choose Beta Updates. Select iPadOS 27 Public Beta. Return to the Software Update page. Tap Download and Install. Keep your iPad connected to Wi-Fi and make sure it has enough battery power before starting the installation. The device will restart after completing the update. What’s New in iPadOS 27 Public Beta 2 Public beta 2 focuses mainly on improving existing iPadOS 27 features rather than introducing major system changes. Automatic downloads in the Apple TV app: A new setting allows the Apple TV app to download upcoming episodes from shows in Continue Watching. It can also remove downloaded episodes after you finish watching them, helping manage storage space. Siri conversation preview controls: The standalone Siri app now includes an option that controls how many lines of preview text appear for previous conversations. This makes the conversation history easier to browse. New AirPods Adaptive mode controls: Control Center now provides more options for adjusting Adaptive Audio when compatible AirPods are connected. Zoom Photos to Fill: The Photos app adds a new setting that automatically enlarges images to fill the screen, reducing empty space around photos with different aspect ratios. Updated introduction screens: New splash screens explain features available through Siri AI and several system apps when users open them for the first time. Bug fixes and performance improvements: The update includes stability fixes, interface refinements, and other changes intended to improve the overall beta experience. Some iPadOS 27 features, including advanced Siri and Apple Intelligence tools, require an iPad model that supports Apple Intelligence. Beta software can also contain app compatibility problems, battery drain, and unexpected crashes, so installing it on a secondary device remains the safer choice. If you’ve already installed the update, let us know your experience in the comments.

Don’t Starve Together: Kultiges Multiplayer-Survival-Game erscheint im App Store

Thu, 23 Jul 2026 08:17:13 +0200

Wohl fast alle Gaming-Fans haben schon einmal vom erstmals im Jahr 2013 erschienenen Indie-Survival-Game Don’t Starve gehört. Der von Klei Entertainment veröffentlichte Einzelspieler-Titel wurde drei Jahre später, genauer gesagt im April 2016, um eine eigenständige Mehrspieler-Erfahrung Don’t Starve Together mit einem Koop-Modus erweitert. Für Don’t Starve gab es nach einiger Vorlaufzeit auch Versionen für iOS […]

Der Beitrag Don’t Starve Together: Kultiges Multiplayer-Survival-Game erscheint im App Store erschien zuerst auf appgefahren.de.

Für weniger als einen Kaffee verkauft Ikea ein Gadget, das eines der nervigsten Probleme am Schreibtisch beendet

Thu, 23 Jul 2026 06:14:13 +0200

Dieses Mini-Gadget kostet fast nichts – löst aber ein Alltagsproblem, das fast jeder kennt, der ein Smartphone besitzt.

Die besten KI-Apps, um Zeit zu sparen

Thu, 23 Jul 2026 06:09:21 +0200

Diese KI-basierten Productivity-App-Perlen helfen wirklich gegen Zeitdruck, Überstunden und repetitive Tasks.

Foto: N Universe | shutterstock.com

Unter den Massen von KI-Tools und -Anwendungen, die aktuell als Mobile-, Desktop- oder Web-App zur Wahl stehen, gibt es nicht wenige, die sich in erster Linie dadurch auszeichnen, dass sie:

Output von fragwürdiger Genauigkeit liefern,
dubiose Texte erzeugen, oder
Bilder generieren, die zum Klick auf den X-Button verleiten.

KI-Tools dieser Art sind vor allem darauf ausgerichtet, vom anhaltenden Generative-AI (GenAI)-Hype zu profitieren – und trüben leider auch den Blick für die echten Anwendungsperlen im Bereich generative KI. Wie etwa die folgenden GenAI-Apps, die Ihre Produktivität im Arbeitsalltag drastisch steigern und damit erhebliche Zeitgewinne realisieren können. Probieren Sie’s aus!

1. ChatPDF

Sie kennen solche Situationen: Jemand schickt Ihnen einen schlanken 300-Seiter im .pdf-Format und bereits nach Seite Zwei stellt sich heraus, dass sich dieser in etwa so faszinierend liest wie eine Steuererklärung. In Zukunft dürfen Sie sich bei solchen und ähnlichen Gelegenheiten auf ChatPDF verlassen und dabei richtig Zeit einsparen.

Dieses rein webbasierte Tool – nicht zu verwechseln mit gleichnamigen Mobile Apps – tut exakt das, was es verspricht: Sie befähigen, mit .pdf-Dateien zu chatten. Darüber hinaus können Sie über das Webportal auch Office-Dokumente im .doc- oder .docx-Format hochladen, um anschließend dank KI-Unterstützung möglichst schnell und einfach Informationen über den Inhalt zu erfragen. Dabei kann es sich konkret um einfache Zusammenfassungen oder spezifische, inhaltsbezogene Fragen handeln. Sie können bei Bedarf sogar mehrere Dokumente einspeisen und diese gemeinschaftlich abfragen. Die Verantwortlichen von ChatPDF versprechen dabei, sämtliche Daten sicher zu speichern, auf Anfrage zu löschen und keinesfalls an Dritte weiterzugeben. Dennoch sollten sensible unternehmensbezogene Dokumente eher nicht diesen Weg nehmen.

ChatPDF verarbeitet davon abgesehen Dokumente in (fast) jeder Sprache – und unterstützt diese auch mit Blick auf die KI-Chat-Funktion. Zwei Dokumente dürfen Sie täglich kostenlos über den Service hochladen und abfragen – wobei die Dateien maximal 120 Seiten lang oder 10 MB groß sein dürfen. Die GenAI-Webanwendung dürfte also in ihrer kostenlosen Variante bereits für die meisten Gelegenheits-User ausreichend sein. Sollten Sie Bedarf haben, der darüber hinausgeht, steht Ihnen die Bezahlversion ChatPDF Plus ab 24,99 Euro pro Monat (oder circa 120 Euro pro Jahr) zur Verfügung.

2. Beautiful.ai

Präsentationen (richtig) zu erstellen, kann zum Pain geraten. Es sei denn, Sie lassen Generative AI den wesentlichen Teil des Gestaltungsprozesses übernehmen. Das funktioniert mit der KI-basierten Präsentationssoftware Beautiful.ai. Das (möglicherweise) größte Defizit dieses ebenfalls webbasierten KI-Tools ist, dass es zwar auch deutschsprachige Prompts verarbeitet, zur Zeit aber nur englischsprachige Präsentationen erstellt. Das tut es dafür aber richtig gut, wie bereits die Mini-Demo auf der offiziellen Webseite zeigt. Die KI-App unterstützt Sie nicht nur beim Design der einzelnen Folien, sondern auch bei der Formatierung von Inhalten und dabei, Brand Guidelines einzuhalten – sowie bei allen anderen Aspekten, die wichtig sind, damit Ihre Präsentation einen möglichst professionellen Eindruck hinterlässt.

Dieses Slide-Set hat Beautiful.ai in wenigen Sekunden zum Thema Arbeit der Zukunft erstellt.

Foto: JR Raphael | IDG

Die generativen KI-Funktionen des Web-Services umfassen auch eine Funktion, um Inhalte zu generieren. Sie können die KI beispielsweise damit beauftragen, eine ganz bestimmte Art von Präsentation zu einem bestimmten Thema zu erstellen. Dazu zieht die Anwendung öffentlich verfügbare Daten heran. Das Ergebnis bedarf zwar sehr wahrscheinlich einer gründlichen Überprüfung, Überarbeitung und Re-Formulierungskur. Dennoch kann es Ihnen eine nützliche erste Grundlage liefern, auf der sich aufbauen und damit potenziell eine Menge Zeit sparen lässt. Beautiful.ai lässt sich mit PowerPoint, Slack, Webex und Dropbox integrieren.

Leider gibt’s den KI-Präsentations-Zauber nicht umsonst. Ein Abonnement für Beautiful.ai kostet für Einzelpersonen 12 Dollar pro Monat. Im Team mit der GenAI-App zu arbeiten, schlägt mit mindestens 40 Dollar pro Nutzer und Monat zu Buche. Einen individuellen Enterprise-Preisplan gibt’s auf Anfrage.

3. Toki

Allen technologiegetriebenen Productivity-Fortschritten zum Trotz bleibt ein Task lästig: mit einem Kalender zu interagieren. Dieser Aufgabe verschreibt sich der KI-Kalenderassistent Toki, der zuvor unter dem Namen Dola bekannt war. Dabei handelt es sich um eine Chatbot-Lösung, die sich in die Messaging-Plattformen WhatsApp, Telegram, Line sowie iMessage einbinden lässt und sich anschließend zum Beispiel mit den Kalender-Apps von Google und Apple verbindet. Da dieses KI-Tool das Netzwerkprotokoll CalDAV nutzt, um auf die Kalenderdaten zuzugreifen, müssen Sie im Fall von Outlook leider den Umweg über ein Drittanbieter-Plugin nehmen.

Ist die Integration erledigt, steht Toki über integrierte Schaltflächen in den Messaging-Apps zur Verfügung, um Termine zu erstellen, zu verschieben – oder direkt Fragen zu freien Terminslots zu stellen. Darüber hinaus kann dieses Tool auch genutzt werden, um Termine mit Infos anzureichern – beispielsweise Vorschläge für beliebte Restaurants in einer bestimmten Gegend oder auch Ideen für den neuen Firmenslogan, der beim Meeting gefunden werden soll.

Der Service ist in so gut wie allen Sprachen verfügbar und in begrenzten Umfang kostenlos nutzbar. Zahlende Benutzer erhalten mehr Features ab 3,99 Dollar pro Monat.

4. Fathom

Dass virtuelle Meetings richtig schlimm werden können, wissen wir wohl alle. Und auch wenn selbst Generative AI Sie (noch) nicht davor bewahren kann, an digitalen Foltersessions teilzunehmen: Es gibt eine KI-App, die das erträglicher macht – Fathom.

Bei dieser Anwendung handelt es sich um einen KI-Assistenten für Videokonferenzen in Form klassischer Software für Windows– oder Mac-Systeme, die wahlweise mit Zoom, Microsoft Teams oder Google Meet integriert wird. Nach der Installation läuft Fathom unauffällig im Hintergrund und transkribiert (über eine Kalender-Integration) entweder automatisch oder auf Knopfdruck sämtliche Videoanrufe. Notizen machen gehört damit in beiden Fällen der Vergangenheit an. Die Zusammenfassungen oder Informationen stehen direkt zur Verfügung und lassen sich gezielt durchsuchen, weiterverarbeiten oder auch in anderen Produktivitäts- und Collaboration-Tools wie Slack nutzen.

Sämtliche Daten werden dabei laut Fathom während der Übertragung und im Ruhezustand verschlüsselt. Außerdem versprechen die Verantwortlichen ausdrücklich, keine KI-Modelle auf Kundendaten zu trainieren. Sämtliche Details zu Security- und Compliance-Themen sind – vorbildlicherweise – über ein dediziertes Trust Center abrufbar.

Fathom realisiert ein umfassendes und sehr fokussiertes Personal-AI-Assistant-Erlebnis.

Foto: JR Raphael | IDG

Die KI-Software unterstützt diverse verschiedene Sprachen, darunter Englisch, Französisch, Spanisch, Italienisch und Deutsch. Noch dazu ist Fathom komplett kostenlos nutzbar – ohne Einschränkungen hinsichtlich der Anzahl oder Länge der aufgezeichneten Videokonferenzen. Erst fortschrittlichere KI-Funktionen lässt sich das Team hinter der GenAI-Anwendung bezahlen.

Die Fathom Team Edition bietet weitergehende, fortschrittliche KI-Funktionen – beispielsweise automatisierte Keyword Alerts, Highlight-Zusammenstellungen oder Team-Management-Funktionen. Die kostenpflichtige Variante ermöglicht darüber hinaus die Integration in Enterprise-Systeme wie HubSpot, Salesforce oder Zapier. Die Preise beginnen bei 15 Dollar pro Monat und User. Die kostenlose Version bietet Premium-Features für fünf Anrufe pro Monat.

5. Whisper Web

Falls Sie bereits Audiodateien besitzen, die beispielsweise im Rahmen von Meetings oder Telefongesprächen entstanden sind und jetzt in Text umgewandelt werden sollen, ist Whisper Web die richtige Adresse – zumindest, wenn es sich um englischsprachige Audioaufnahmen handelt. Diese quelloffene Webanwendung basiert auf der Entwicklungsarbeit von OpenAI und bietet Echzeit-Transkriptionen direkt im Browser. Das Large Language Model, das dazu zum Einsatz kommt, wird über die App heruntergeladen und lokal ausgeführt – die Daten, die Sie der KI übermitteln, verlassen also das Device nicht.

Whisper Web kann Audioinhalte entweder direkt über Ihr Mikrofon erfassen oder aus entsprechenden Audiodateien extrahieren. Laut den Entwicklern ist die KI-App auf mehrsprachige Daten trainiert und unterstützt auch die Transkription anderer Sprachen (zu Englisch). Der Test mit einem deutschsprachigen Audio-File brachte allerdings nicht mehr als undefiniertes Kauderwelsch hervor. Dafür ist das Tool Open Source und komplett kostenlos nutzbar – Sie benötigen dazu auch kein dediziertes Konto.

6. AudioPen

Wenn Sie nicht ohne Ihr Notizbuch (oder eine entsprechende App) auskommen, könnte das KI-Tool AudioPen sich zu Ihrer neuen Lieblings-App mausern. Die Software erfasst auf Knopfdruck Sprachnotizen jeglicher Art und erstellt daraus im Handumdrehen eine schriftliche Zusammenfassung. Und zwar in “schön”: Füllwörter oder Wiederholungen werden automatisiert eliminiert. Jede Aufnahme wandert direkt in das digitale Notizbuch und lässt sich anschließend durchsuchen, teilen oder auch in eine andere Sprache übersetzen. Auch bei AudioPen handelt es sich um eine vollständig webbasierte Applikation, die sich übrigens optional auch in Form einer Progressive Web App installieren lässt.

AudioPen verwandelt selbst die wiederholungsintensivsten Selbstgespräche in prägnante Notizen.

Foto: JR Raphael | IDG

Das KI-Tool für Sprachnotizen ist kostenlos nutzbar, solange Sie sich auf Aufnahmen mit bis zu drei Minuten Länge und maximal zehn Notizen beschränken können. Für Ansprüche, die darüber hinausgehen, steht eine “Prime”-Version der App zur Verfügung, die mindestens 99 Dollar pro Jahr kostet – dafür aber uneingeschränkt nutzbar ist und eine Reihe zusätzlicher Funktionen bietet. (fm)

Dieser Beitrag ist im Original bei unserer Schwesterpublikation Computerworld.com erschienen.

10 Linux-Pflicht-Tools für Netzwerk- und Security-Profis

Thu, 23 Jul 2026 06:09:11 +0200

Wir haben zehn essenzielle Open-Source-Security-Tools für Sie zusammengestellt.

Foto: Omelchenko – shutterstock.com

Eine Wahl zu treffen, wenn Dutzende oder gar Hunderte von Tools zur Verfügung stehen, ist nicht einfach. So dürfte es auch vielen Netzwerk- und Security-Experten gehen, die quelloffene Security Tools für Linux suchen.

In diesem Bereich gibt es eine Vielzahl verschiedener Tools für so gut wie jede Aufgabe (Netzwerk-Tunneling, Sniffing, Scanning, Mapping) und jede Umgebung (Wi-Fi-Netzwerke, Webanwendungen, Datenbankserver). Wir haben einige Experten konsultiert und zehn essenzielle Linux-Sicherheitstools für Sie zusammengestellt.

1. Aircrack-ng

Diese Suite von Software Tools ermöglicht es, drahtlose Netzwerke und WiFi-Protokolle Sicherheitsüberprüfungen zu unterziehen. Sicherheitsprofis verwenden das Tool für die Netzwerkadministration, Hacking und Penetrationstests. Dabei fokussiert Aircrack-ng auf:

Monitoring (Datenpakete erfassen und Daten in Textdateien zur Weiterverarbeitung durch Tools von Drittanbietern exportieren)
Angreifen (Replay-Angriffe, Deauthentication, Packet Injection)
Testing (WiFi-Karten und Treiberfunktionen überprüfen) und
Cracking (WEP und WPA PSK)

Laut der offiziellen Webseite funktionieren alle Tools kommandozeilenbasiert, was eine umfangreiche Skripterstellung ermöglicht. Das Tool funktioniert mit Linux genauso wie mit Windows, macOS, FreeBSD, OpenBSD, NetBSD, Solaris und sogar eComStation.

Preis: kostenlos

2. Burp Suite

Hierbei handelt es sich um eine Testing-Suite für Webanwendungen, die für Security Assessments von Websites eingesetzt wird. Burp Suite arbeitet als lokale Proxy-Lösung, die es Sicherheitsexperten ermöglicht, Anfragen (HTTP/Websockets) und Antworten zwischen einem Webserver und einem Browser

entschlüsseln,
beobachten,
manipulieren und
wiederholen zu können.

Burp Suite hat einen passiven Scanner an Bord, mit dem Security-Profis Webseiten (manuell) auf potenzielle Schwachstellen überprüfen können. Die Pro-Version bietet außerdem einen sehr nützlichen aktiven Web-Schwachstellen-Scanner, mit dem sich weitere Schwachstellen aufspüren lassen. Burp Suite ist über Plugins erweiterbar, so dass Sicherheitsexperten ihre eigenen Erweiterungen entwickeln können.

Preis: Die Professional-Version kostet 475 Euro pro Jahr und Benutzer. Darüber hinaus steht auch eine Enterprise-Version (ab ca. 2.000 Euro jährlich) zur Verfügung, die mehrere gleichzeitige Scans ermöglicht und von Anwendungsentwicklungsteams genutzt werden kann.

3. Impacket

Diese Sammlung von Tools ist für Pen-Tests von Netzwerkprotokollen und -diensten unerlässlich. Impacket wurde von SecureAuth entwickelt und ist eine Sammlung von Python Classes, um mit Netzwerkprotokollen zu arbeiten. Impacket konzentriert sich auf die Bereitstellung von Low-Level-Zugriff auf Pakete und bei einigen Protokollen wie SMB1-3 und MSRPC auf die Protokollimplementierung selbst. Sicherheitsexperten können Pakete von Grund auf neu konstruieren, aber auch auf Grundlage geparster Rohdaten. Die objektorientierte API macht es zudem einfach, mit tiefen Protokollhierarchien zu arbeiten. Impacket unterstützt die folgenden Protokolle:

Ethernet, Linux;
IP, TCP, UDP, ICMP, IGMP, ARP;
IPv4 und IPv6;
Umgänglicher zeigte sich Musk gegenüber den Anzeigenkunden von Twitter. In einem – natürlich auf Twitter geposteten – Brief erklärte der Tesla-Chef, der Grund für die Übernahme sei nicht, damit noch mehr Geld zu verdienen. Vielmehr sei es “wichtig für den Fortbestand der Zivilisation, einen gemeinsamen digitalen Treffpunkt zu haben, auf dem eine breite Palette von Überzeugungen auf gesunde Weise diskutiert werden kann.”
Trotz alledem dürfe Twitter nicht zu einer “für alle Nutzer freien Höllenlandschaft werden, in der alles ohne Konsequenzen gesagt werden kann”, fügte Musk hinzu. Zusätzlich zur Einhaltung der Gesetze müsse die Plattform “warmherzig und einladend” für alle sein und den Nutzern die Möglichkeit bieten, “die gewünschte Erfahrung nach ihren Vorlieben zu wählen” – ähnlich wie man zum Beispiel wählen kann, Filme zu sehen oder Videospiele zu spielen, die für alle Altersgruppen geeignet sind.
Plain-, NTLM- und Kerberos-Authentifizierungen, unter Verwendung von Kennwörtern/Hashes/Tickets/Schlüsseln;
EU-Kommissar Thierry Breton wiederum reagierte auf Musks Teet, dass der Vogel jetzt frei sein, mit der Anmerkung, “dass Twitter in Europa nach unseren Regeln fliegen muss”.

Preis: Kostenlos – Impacket wird unter einer leicht modifizierten Version der Apache Software License bereitgestellt. Die Unterschiede können Sie hier einsehen.

4. Metasploit

Metasploit ist ein Exploit-Framework von Rapid7, das für allgemeine Penetrationstests und Schwachstellenbewertungen verwendet wird. Sicherheitsexperten betrachten es als “Super-Tool”, das funktionierende Versionen fast aller bekannter Exploits enthält. Metasploit ermöglicht Sicherheitsexperten, Netzwerke und Endpunkte auf Schwachstellen zu scannen und anschließend automatisiert mögliche Exploits auszuführen, um Systeme zu übernehmen.

Metasploit erleichtert es mit protokollspezifischen Modulen (die alle unter der Funktion Auxiliary/Server/Capture laufen) Anmeldeinformationen zu erfassen. Sicherheitsexperten können jedes dieser Module einzeln starten und konfigurieren – zudem steht ein Capture-Plug-in zur Verfügung, das diesen Prozess vereinheitlicht.

Preis: Metasploit Pro kostet – inklusive kommerziellem Support durch Rapid7 – ab 12.000 Dollar pro Jahr. Es gibt aber auch eine kostenlose Version.

5. Ncat

Der Nachfolger des beliebten Tools Netcat heißt Ncat und kommt von den Machern von Nmap. Das Tool ermöglicht es, Daten per Kommandozeile über ein Netzwerk zu lesen und zu schreiben, bietet aber auch zusätzlich Funktionen wie SSL-Verschlüsselung. Sicherheitsexperten zufolge ist Ncat unerlässlich geworden, um TCP/UDP-Clients und -Server zu hosten und Daten von Angreifer- und Opfersystemen zu empfangen.

Ncat ist auch ein beliebtes Tool, um eine Reverse Shell einzurichten oder Daten zu exfiltrieren. Es wurde als zuverlässiges Back-End-Tool entwickelt, um Netzwerkverbindungen zu anderen Anwendungen und Benutzern herzustellen.

Preis: kostenlos

6. Nmap

Dieses Netzwerk-Scanning- und Mapping-Tool auf Kommandozeilen-Basis findet zugängliche Ports auf Remote Devices. Viele Sicherheitsexperten halten Nmap für eines der wichtigsten und effektivsten Tools – insbesondere im Bereich Penetration Testing ist es unerlässlich.

Die Skripting-Engine von Nmap erkennt anschließend automatisiert weitere Schwachstellen und nutzt diese aus. Nmap unterstützt Dutzende fortschrittlicher Techniken, um Netzwerke mit IP-Filtern, Firewalls, Routern und anderen Hindernissen abzubilden. Dazu gehören auch zahlreiche Mechanismen, um TCP- und UDP-Ports zu scannen, Betriebssysteme und Versionen sowie Ping-Sweeps zu erkennen.

Preis: kostenlos

7. ProxyChains

Dieses Werkzeug – der De-facto-Standard für Netzwerk-Tunneling – ermöglicht es Sicherheitsexperten, Proxy-Befehle von ihrem angreifenden Linux-Rechner aus über verschiedene kompromittierte Rechner zu senden, um Netzwerkgrenzen und Firewalls zu überwinden und dabei einer Entdeckung zu entgehen.

ProxyChains leitet den TCP-Verkehr von Penetrationstestern durch die folgenden Proxys: TOR, SOCKS und HTTP. ProxyChains ist mit TCP-Aufklärungs-Tools wie NMAP kompatibel und verwendet standardmäßig das TOR-Netzwerk. Sicherheitsexperten verwenden ProxyChains auch bei der IDS/IPS-Erkennung.

Preis: kostenlos

8. Responder

Responder ist ein NBT-NS (NetBIOS Name Service), LLMNR (Link-Local Multicast Name Resolution) und mDNS (Multicast DNS) Poisoner. Penetration Tester nutzen das Tool, um Angriffe zu simulieren, die darauf abzielen, Anmeldeinformationen und andere Daten während des Prozesses der Namensauflösung zu stehlen, wenn der DNS-Server keinen Eintrag findet. Ab Version 3.1.1.0 bietet Responder standardmäßig vollen IPv6-Support.

Preis: kostenlos

9. sqlmap

Das Open-Source-Tool sqlmap richtet sich ebenfalls an Penetrationstester und automatisiert den Prozess, SQL-Injection-Fehler zu erkennen, mit deren Hilfe Datenbankserver kompromittiert werden könnten. Das Tool verfügt über eine leistungsstarke Erkennungs-Engine und bietet zahlreiche Funktionen, darunter Datenbank-Fingerprinting und die Ausführung von Befehlen auf Betriebssystemebene über Out-of-Band-Verbindungen.

Sqlmap unterstützt eine breite Palette von Datenbankservern, darunter:

MySQL,
Oracle,
PostgreSQL,
Microsoft SQL Server,
Microsoft Access,
IBM DB2,
SQLite,
Firebird,
Sybase,
SAP MaxDB und
HSQLDB.

Preis: kostenlos

10. Wireshark

Das Netzwerkprotokoll-Analyse-Tool Wireshark wird auch oft als Network Interface Sniffer bezeichnet. Mit Wireshark können Sicherheitsexperten das Netzwerkverhalten eines Geräts beobachten, um zu sehen, mit welchen anderen Geräten es kommuniziert und warum.

Sicherheitsexperten zufolge eignet sich Wireshark hervorragend, um herauszufinden, wo sich DNS-Server und andere Dienste befinden, mit denen sich ein Netzwerk weiter kompromittieren lässt. Wireshark läuft nicht nur unter Linux, sondern funktioniert mit den allen gängigen Betriebssystemen, einschließlich Windows, MacOs und Unix.

Preis: kostenlos

(fm)

Dieser Beitrag ist im Original bei unserer Schwesterpublikation Networkworld.com erschienen.

Going back to Linux full-time after many years

Thu, 23 Jul 2026 04:21:44 +0200

I've been using PCs since 1998. Around 2000 I started with PCLOS. Because they had the friendliest user forum for noobs. Also back then PCLOS was like the Mint of today. The easiest distro for noobs. I had to buy an external modem to get dialup. And TexStar the creator personally held my hand to get the modem configured.

Back then laptops were outrageously expensive. So I built a desktop. I had removable drawers for the hard drives that I used to clone back & forth. Because I would destroy my W98se installs. I created a PCLOS live CD. I removed both HDD. Booted to PCLOS. I started laughing like crazy because I was using my PC without a HDD! I was gobstruck.

Fast forward to around the present day. I can't remember what year I started using Linux full-time. I'm old & my memory is terrible. I think I lasted around a year then went back to Windows. And the reason I went backwards was so stupid it's shocking. It was because a fast Windows surfing session for me was 1hr-1.5hrs. Where as in Linux it was only 20minutes. I went back to Windows because Linux was too EASY!

Anyways W7 was rock solid for me. After a Vista nightmare I went back to W7. Stayed on W7 till 2023. Then jumped to W11. Since W7 I think I borked 2-3 W installs. That was until W11 25H2. What a POS.

In the last 5 weeks three W11 installs were borked. I blame M$ & 25H2. I know when a bork is my fault. These were not.

I got a new used ThinkPad X1 Carbon Gen 4 16GB 6th Gen i7 6600 2016. Installed Mint Cinnamon for a few days. It was clunky & froze a couple on the ancient hardware.

I then installed Debian LXQT 13.6. Wow Debian is much easier than 15 years ago. LOL. Everything is snappy except Zoom. My hardware won't run the Zoom app. But Zoom worked ok in a browser. I disabled a few unnecessary auto start services now Zoom & everything runs well.

I have one other primary laptop to install Linux.

I hope I don't go backwards to Windows.

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Digitale Selbstbestimmung: OnionHop leitet euren gesamten Datenverkehr durch das Tor-Netzwerk (Linux/Win/Mac)

Thu, 23 Jul 2026 04:14:01 +0200

OnionHop – Überblick

OnionHop — route your traffic through Tor, with clear controls ist ein moderner plattformübergreifender Desktop-Client (für Windows, Linux und macOS) mit starkem Fokus auf Privatsphäre. Er ermöglicht es Nutzern, ihren Datenverkehr über das Tor-Netzwerk zu leiten. Es handelt sich um ein unabhängiges Open-Source-Projekt, das nicht offiziell mit dem Tor Project | Anonymity Online verbunden ist. Die OnionHop Oberfläche bietet 8 Sprachen (Englisch, Deutsch, Französisch, Chinesisch, Russisch, Persisch, Aserbaidschanisch und Sorani Kurdisch).

Melden Sie sich freiwillig als Snowflake-Proxy helfen Sie zensierten Benutzern, Tor direkt von den Einstellungen aus zu erreichen (siehe Einstellungen).

Hauptfunktionen

Proxy-Modus: Leitet den Datenverkehr ressourcenschonend über einen lokalen SOCKS-Proxy um, ohne dass Administratorrechte benötigt werden. Ein Knopfdruck auf "System Proxy: On" und alle gängigen Browser benutzen das Tor Netzwerk.
TUN-Modus (eigene virutelle Netzwerkkarte): Leitet den Datenverkehr des gesamten Systems (alle Apps) mit einem Klick über das Tor-Netzwerk um.
Split-Tunneling: Ermöglicht die individuelle Auswahl, welche Apps über Tor laufen und welche direkte Verbindungen nutzen.
Kill-Switch: Blockiert den ausgehenden Datenverkehr sofort, wenn die Tor-Verbindung abbricht, um ungeschützte Datenlecks zu verhindern.
DNS-Steuerung: Erzwingt DNS-Anfragen über Tor und verhindert Lecks zum Internetanbieter (inklusive QUIC/UDP-Leckschutz).
Länder- & Seiten-Routing: Erlaubt direkte Verbindungen für bestimmte Länder oder das Blockieren von Domains basierend auf automatisch aktualisierten Listen.
CLI-Client: Bietet eine Kommandozeilenversion ohne grafische Oberfläche, ideal für Server und Automatisierungen. Zensurumgehung und Netzwerktechnologien
Smart Connect: Wählt automatisch die optimale Engine, Route und Bridge für das aktuelle Netzwerk.
Integrierter Bridge-Scanner: Sucht und testet automatisch funktionierende Brücken (Bridges) in restriktiven Netzwerken, sodass keine manuelle Eingabe erforderlich ist.
Pluggable Transports: Unterstützt Protokolle wie obfs4, snowflake, webtunnel, conjure, meek, dnstt und vanilla, um Netzwerkblockaden zu umgehen.
Tor-Engines: Nutzt Classic (tor), Arti (A Rust Tor Implementation (Gitlab Changelog) oder https://arti.torproject.org/ für die Nerds Improving Tor using a TCP-over-DTLS Tunnel (PDF, englisch), bald schon mit UDP Unterstützung 339 / 348-udp-app-support - Tor design proposals (Tor Architektur), dies ist die Zukunft von Tor, weg von C mit seinen historisch vielen Buffer Overflows ~65% aller Tor Sicherheitslücken, mit RPC-Schnittstelle (Remote Procedure Call / Methodenaufruf auf entfernten Systemen / Software) und UDP für moderne Anwendungen)

Da Transparenz bei Software für die informationelle Selbstbestimmung das Wichtigste ist, ist das gesamte Projekt quelloffen (Open Source). Ihr könnt euch den Quellcode jederzeit auf GitHub ansehen, ihn selbst kompilieren oder Code-Überprüfungen (Code Reviews) durchführen:

👉 GitHub-Repository: center2055/OnionHop: Privacy-first Desktop app that routes your traffic through Tor - Anonymous browsing made simple

Die Software steht für Linux, Windows und macOS zur Verfügung (es gibt auch tragbare Versionen, die nicht installiert werden müssen). Neben der grafischen Oberfläche gibt es für Automatisierungen auch eine reine Kommandozeilen-Version (CLI).

Systemweite Socks5 Proxy vs. TUN VPN-Technik Modus mit eigener virtuellen Netzwerkkarte (TUN)

Um zu verstehen, warum der TUN oft sicherer ist, hilft ein direkter Vergleich:

Eigenschaft	Systemweiter Vermittlungsserver (System SOCKS5 Proxy)	TUN "Netzwerktunnel"(OSI Layer 3) "virtuellen Netzwerkadapter" nicht TAP wie bei VPN Layer 2
Arbeitsweise	Setzt darauf, dass Programme die Regeln des Betriebssystems respektieren und die Poststelle nutzen.	Zwingt den gesamten Netzwerkverkehr auf tiefer Ebene durch einen Trichter.
Zuverlässigkeit	Manche Programme (z. B. bestimmte Spiele oder Hintergrunddienste) ignorieren diese Einstellungen und funken direkt ins Internet.	Fängt alles ab, völlig unabhängig davon, wie das einzelne Programm programmiert ist.
Schutz vor Datenlecks	Es kann vorkommen, dass Informationen (wie Adressanfragen) ungeschützt nach außen dringen (Datenlecks).	Bietet einen sehr hohen Schutz vor Datenlecks, da kein Datenpaket die Straßensperre umgehen kann. Eigenschaft Systemweiter Vermittlungsserver (SOCK5 Proxy) TUN-ModusArbeitsweise Setzt darauf, dass Programme die Regeln des Betriebssystems respektieren und die Poststelle nutzen. Zwingt den gesamten Netzwerkverkehr auf tiefer Ebene durch einen Trichter. Zuverlässigkeit Manche Programme (z. B. bestimmte Spiele oder Hintergrunddienste) ignorieren diese Einstellungen und funken direkt ins Internet. Fängt alles ab, völlig unabhängig davon, wie das einzelne Programm programmiert ist. Schutz vor Datenlecks Es kann vorkommen, dass Informationen (wie Adressanfragen) ungeschützt nach außen dringen (Datenlecks). Bietet einen sehr hohen Schutz vor Datenlecks, da kein Datenpaket die Straßensperre umgehen kann.

Zusammenfassung: Warum es beide Modi (System SOCKS5 Proxy und TUN) gibt

Genau an diesem Punkt zeigt sich, warum Werkzeuge wie OnionHop unterschiedliche Modi anbieten müssen:

Der System SOCKS5 Proxy-Modus (Anwendung muss Socks5 fähig sein z.B. alle Internet Browser): Er ist sehr ressourcenschonend und leichtgewichtig. Er eignet sich hervorragend, wenn du gezielt nur die Anwendungen über das Tor-Netzwerk leiten möchtest, die diesen Standard unterstützen (wie deinen Browser).
Der TUN-Modus "virtuelle Netzwerkkarte": Er löst exakt das Problem der fehlenden Unterstützung in Programmen. Wie wir zuvor besprochen haben, baut dieser Modus eine **virtuelle Netzwerkkarte (**sichtbar unter "Netzwerkverbindungen" unter Windows, WIN + R = ncpa.cpl) auf und zwingt das Betriebssystem, alles dorthin zu leiten. Hierbei ist es völlig egal, ob ein Programm von Vermittlungsservern weiß oder nicht – die Daten werden auf einer Ebene abgefangen, der sich kein Programm entziehen kann.

Für die Entwickler unter euch: Wie ändert man den System-Proxy eigentlich programmatisch?

Wer schon länger in der Softwareentwicklung tätig ist, kennt das Problem bei der plattformübergreifenden Programmierung: Ein einheitliches Vorgehen gibt es hier leider nicht. Jedes Betriebssystem kocht sein eigenes Süppchen, was bei Werkzeugen wie OnionHop unter der Haube einigen Aufwand bedeutet. Hier ist ein kleiner technischer Einblick, wie der Code das im Hintergrund löst:

1. Windows: Die Registrierungsdatenbank und WinINet Einfach nur Werte in eine Konfigurationsdatei zu schreiben, reicht hier nicht. Zuerst müssen die Werte in der Registrierungsdatenbank (im Zweig HKEY_CURRENT_USER\Software\Microsoft\Windows\CurrentVersion\Internet Settings) manipuliert werden (z. B. ProxyEnable auf 1 und ProxyServer auf 127.0.0.1:9050). Der entscheidende Schritt ist danach aber, das System über diese Änderung zu benachrichtigen, da laufende Anwendungen (wie der Browser) die neuen Werte sonst ignorieren. Dafür muss ein Aufruf der Windows-Systembibliothek wininet.dll erfolgen. Über die Programmierschnittstelle (API) InternetSetOption feuert man die Markierungen (Flags) INTERNET_OPTION_SETTINGS_CHANGED und INTERNET_OPTION_REFRESH ab, um das System zum sofortigen Neuladen zu zwingen.

2. Linux: Die große Fragmentierung Gerade wenn man in hybriden Umgebungen (wie dem Windows-Subsystem für Linux oder mit Container-Lösungen) entwickelt, merkt man schnell: Linux hat keine zentrale Instanz für diese Einstellungen.

Kommandozeile und Hintergrunddienste: Diese reagieren fast ausschließlich auf Umgebungsvariablen wie http_proxy oder ALL_PROXY. Diese müssen durch das Programm systemweit oder in den jeweiligen Startskripten (z. B. ~/.bashrc) gesetzt werden.
GNOME-Desktop: Hier wird die Konfigurationsdatenbank (dconf) genutzt. Programmatisch löst man das über die C-Programmierschnittstelle von GLib oder einfacher durch das Ausführen von Systembefehlen im Hintergrund (z. B. gsettings set org.gnome.system.proxy mode 'manual').
KDE Plasma: Speichert die Konfiguration in Textdateien (~/.config/kioslaverc), die von der Software analysiert und editiert werden müssen, gefolgt von einem Befehl zum Neustart der zuständigen KDE-Dienste.

3. macOS: SystemConfiguration Framework Apple regelt das Netzwerkmanagement streng über die einzelnen Hardware-Schnittstellen (WLAN, Kabelnetzwerk etc.).

Der skriptbasierte Weg: Ein Programm ruft im Hintergrund das vorinstallierte Kommandozeilen-Werkzeug networksetup auf, um den Vermittlungsserver für jede aktive Netzwerkschnittstelle einzeln zu setzen (z. B. networksetup -setsocksfirewallproxy "Wi-Fi" 127.0.0.1 9050).
Der native Weg: Die Software greift direkt über C oder Swift auf das Systemgerüst SystemConfiguration zu. Über die Programmierschnittstelle SCDynamicStore klinkt man sich in den Konfigurationsspeicher ein, schreibt ein Datenverzeichnis mit den neuen Werten in den Pfad State:/Network/Global/Proxies und teilt so dem Kernel die Netzwerkänderung ohne Umwege direkt mit.

Wie Onionhop unter Windows den Datenverkehr über virtuelle Netzwerkschnittstellen (TUN) lenkt

Die Magie passiert über virtuelle Netzwerkschnittstellen (TUN-Modus) und gezielte Manipulation der Wegfindung (Routing).

1. Der virtuelle Netzwerktreiber

Windows nutzt für Netzwerkkarten die sogenannte Network Driver Interface Specification (NDIS). Tools wie Onionhop installieren einen virtuellen Treiber (oft auf Basis von Wintun).

Auf der Ebene des Betriebssystemkerns (Kernel-Ebene) ist dieser Treiber eine vollwertige Netzwerkkarte. Das System sieht absolut keinen Unterschied zu eurem echten WLAN-Modul oder Netzwerkkabel. Dieser Adapter arbeitet auf der Vermittlungsschicht (Layer 3). Er verarbeitet also reine IP-Datenpakete (Internetprotokoll) und simuliert keine Hardware-Adressen (MAC-Adressen) der tieferen Schichten.

2. Die Übernahme der Wegfindung (Routing)

Damit Windows die Daten nicht ans WLAN, sondern an Onionhop schickt, wird die Wegfindungstabelle (Routingtabelle) dynamisch angepasst, sobald die Verbindung steht:

Onionhop fügt eine neue Standardroute (0.0.0.0/0 – also den Weg für "alle unbekannten Ziele im Internet") hinzu, die auf die virtuelle TUN-Schnittstelle zeigt.
Der entscheidende Trick: Diese neue Route bekommt einen niedrigeren Prioritätswert (Metrik) als der echte WLAN-Adapter. Da Windows bei konkurrierenden Routen immer den Weg mit dem niedrigsten Wert wählt, fließt der gesamte ausgehende Datenverkehr des Systems ab sofort in den virtuellen Tunnel.

3. Datenkapselung im Anwendungsbereich (User-Space)

Jetzt landen die Daten (Nutzdaten) bei der Onionhop-Anwendung, die als Hintergrunddienst läuft:

Die Anwendung lauscht an der virtuellen Schnittstelle und fängt die IP-Pakete ab.
Sie verschlüsselt diese Nutzdaten.
Die verschlüsselten Pakete werden nun mit einer neuen Ziel-IP versehen (dem ersten Knotenpunkt im Onion-Netzwerk). Das nennt man Datenkapselung (Encapsulation).
Erst jetzt übergibt Onionhop diese neu verpackten Pakete wieder an den Windows-Netzwerkstapel.

Die Wegfindungstabelle von Windows sieht nun diese spezifische Ziel-IP des Einsteiger-Knotens und weiß: "Ah, diese IP muss über das echte Standard-Gateway des physischen WLAN-Adapters raus."

Der echte WLAN-Adapter dient also nur noch als reines Transportmedium für den bereits gekapselten und verschlüsselten Datenverkehr. Die eigentlichen Programme (wie der Browser) denken währenddessen, sie sprechen mit einer ganz normalen Netzwerkkarte.

Zum Selbstprüfen für die Kommandozeile:

Wer sich das beim Testen von Onionhop live ansehen will, kann die PowerShell nutzen:

Versteckte Schnittstellen anzeigen:
PowerShellGet-NetAdapter -IncludeHidden
Wegfindung und Prioritäten prüfen:
PowerShellGet-NetRoute -DestinationPrefix "0.0.0.0/0"

Was ist die Abgrenzung zu einem "echten" VPN wie z.B. ProtonVPN?

Eigenschaft	OnionHop (Tor-Netzwerk)	Klassisches VPN (z.B. ProtonVPN)
Architektur	Dezentral. Deine Daten fließen über drei zufällige, weltweit verteilte Knotenpunkte.	Zentralisiert. Deine Daten fließen direkt durch einen festen Server des VPN-Anbieters.
Vertrauensmodell	Trustless. Du musst niemandem vertrauen. Der erste Knoten kennt dich (aber nicht das Ziel), der letzte Knoten kennt das Ziel (aber nicht dich).	Vertrauensbasiert. Du musst deinem VPN Anbieter zu 100 % vertrauen, da sie deinen gesamten unverschlüsselten Datenverkehr sehen können.
Protokolle	Tor transportiert prinzipbedingt nur TCP-Verbindungen. UDP (wichtig für Online-Spiele oder VoIP) wird blockiert.	Überträgt TCP, UDP und oft auch ICMP (Ping) vollständig. Eigenschaft OnionHop (Tor-Netzwerk) Klassisches VPN (z.B. ProtonVPN) Architektur Dezentral. Deine Daten fließen über drei zufällige, weltweit verteilte Knotenpunkte. Zentralisiert. Deine Daten fließen direkt durch einen festen Server des VPN-Anbieters. Vertrauensmodell Trustless. Du musst niemandem vertrauen. Der erste Knoten kennt dich (aber nicht das Ziel), der letzte Knoten kennt das Ziel (aber nicht dich). Vertrauensbasiert. Du musst deinem VPN Anbieter zu 100 % vertrauen, da sie deinen gesamten unverschlüsselten Datenverkehr sehen können.Protokolle Tor transportiert prinzipbedingt nur TCP-Verbindungen. UDP (wichtig für Online-Spiele oder VoIP) wird blockiert. Überträgt TCP, UDP und oft auch ICMP (Ping) vollständig.

Das Virtuelle Private Netzwerk (VPN): Tiefer Eingriff auf Schicht 2 und 3

Ein VPN verhält sich für das Betriebssystem wie eine echte, physische Netzwerkkarte – nur eben als virtuelle Variante (Netzwerkschnittstelle). Es greift tief in das System ein und fängt den gesamten Datenverkehr ab, bevor dieser das Gerät verlässt.

Auf der Vermittlungsschicht (Schicht 3 / Network Layer): Hier arbeiten die meisten modernen VPN-Verbindungen (wie WireGuard oder IPsec). Sie verpacken (kapseln) komplette IP-Datenpakete. Das bedeutet, dass die gesamte Netzkopplung (Routing) übernommen wird. Jeder Datenverkehr – egal ob gesicherte Verbindungsaufbauten (TCP), verbindungslose Übertragungen (UDP) oder Diagnoseabfragen (ICMP, wie bei einem Ping) – wird in den verschlüsselten Tunnel gezwungen.
Auf der Sicherungsschicht (Schicht 2 / Data Link Layer): Einige VPN-Lösungen beherrschen auch die sogenannte Netzwerküberbrückung (Bridging, z. B. OpenVPN im TAP-Modus). Hier werden die rohen Datenrahmen (Ethernet Frames) übertragen. Das System verhält sich so, als wären alle Rechner über hunderte Kilometer hinweg an denselben physischen Netzwerkverteiler (Switch) angeschlossen. In diesem Modus werden sogar lokale Netzwerk-Rundrufe (Broadcasts) durch den Tunnel übertragen.

Das Zwiebelnetzwerk (Tor): Aufsatz auf Schicht 4 und 7

Tor arbeitet architektonisch völlig anders und hat mit den unteren Netzwerkschichten (Schicht 2 und 3) primär nichts zu tun. Es erstellt keine virtuelle Netzwerkkarte im Betriebssystem.

Anwendungsschicht (Schicht 7) & Transportschicht (Schicht 4): Das Tor-Programm läuft lokal als Stellvertreter-Dienst (SOCKS-Proxy). Eure Software (z. B. der Browser) muss explizit so konfiguriert werden, dass sie diesen Stellvertreter anspricht. Tor nimmt diese Anfragen entgegen und wickelt den Datenstrom ausschließlich über die Transportschicht ab – und hier auch nur für das TCP-Protokoll.
Keine rohen Pakete: Tor transportiert keine IP-Datenpakete (Schicht 3) und keine Ethernet-Datenrahmen (Schicht 2). Verbindungslose UDP-Pakete oder simple Ping-Abfragen (ICMP) werden vom Tor-Netzwerk schlichtweg nicht weitergeleitet.

Die Konsequenz für die IT-Sicherheit (Datenlecks)

Aus Sicht der Informationssicherheit ergibt sich daraus ein massiver Unterschied im Gefahrenpotenzial:

Da ein klassisches VPN auf der Vermittlungsschicht (Schicht 3) arbeitet, fängt es als Standard-Netzweg (Default Gateway) den gesamten Verkehr des Betriebssystems ein.

Das Zwiebelnetzwerk hingegen ist stark anfällig für Datenlecks (Leakage), da es auf Schicht 4 und 7 operiert. Wenn eine Anwendung auf dem Rechner nicht strikt an den Tor-Stellvertreter (Proxy) gebunden ist, oder wenn sie versucht, über das verbindungslose UDP-Protokoll eine Namensauflösung (DNS-Anfrage) durchzuführen, wandern diese Datenpakete unverschlüsselt am Zwiebelnetzwerk vorbei ins normale Internet. Eure echte IP-Adresse wäre in diesem Fall sofort enttarnt. Um Tor so abzusichern, dass es wie ein VPN den gesamten Rechnerverkehr schützt (auf Schicht 3 erzwingt), bedarf es spezialisierter Betriebssysteme wie Tails oder dedizierter Hardware-Zwischenstationen.

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Chrome for Android Update

Thu, 23 Jul 2026 03:11:23 +0200

Hello Everyone! We've just released Chrome 151 (151.0.7922.47) for Android to a small percentage of users. It'll become available on Google Play over the next few days. You can find more details about early Stable releases here.

This release includes stability and performance improvements. You can see a full list of the changes in the Git log. If you find a new issue, please let us know by filing a bug.

Harry Souders
Google Chrome

Monday.com cuts 20% of its workforce to restructure for the AI era

Thu, 23 Jul 2026 03:02:52 +0200

Monday.com co-founder and co-CEO Eran Zinman today announced the “very difficult decision” to reduce the AI work platform company’s global workforce by about 20%, or 620 people.

A ‘significant opportunity’ in technology

“This wasn’t people’s fault and everyone was frustrated by this,” he said. “Our new org changes ownership to allow people to make decisions and move fast.”

A spokesperson for monday.com declined to comment further on the staff reductions.

Monday.com’s key market advantages

Monday.com certainly isn’t struggling; the company expects 19% to 20% year-over-year growth in 2026.

“Companies in that position do not restructure because they must,” Greyhound’s Gogia noted. “They restructure because they have decided to become something else.”

While the company looks as though it’s trying to do right, that ultimately remains to be seen, she said. “There are often hidden internal bruises that can surface long after layoffs.”

“Their pull is strongest exactly where monday.com wants to grow, in the largest accounts, where control-plane depth and administrative reach decide the deal,” said Gogia.

Actions for the near-term

“It should be a measure of how much smoother, faster, and more effective the operating model becomes when AI is built into the work,” said Brue.

“A license was a known cost,” said Gogia. “A meter is a behavior, and behavior is harder to forecast than headcount.”

This article originally appeared on CIO.com.

Monday.com cuts 20% of its workforce to restructure for the AI era

Thu, 23 Jul 2026 02:50:31 +0200

Monday.com co-founder and co-CEO Eran Zinman today announced the “very difficult decision” to reduce the AI work platform company’s global workforce by about 20%, or 620 people.

A ‘significant opportunity’ in technology

“This wasn’t people’s fault and everyone was frustrated by this,” he said. “Our new org changes ownership to allow people to make decisions and move fast.”

A spokesperson for monday.com declined to comment further on the staff reductions.

Monday’s key market advantages

Monday.com certainly isn’t struggling; the company expects 19% to 20% year-over-year growth in 2026.

“Companies in that position do not restructure because they must,” Greyhound’s Gogia noted. “They restructure because they have decided to become something else.”

While the company looks as though it’s trying to do right, that ultimately remains to be seen, she said. “There are often hidden internal bruises that can surface long after layoffs.”

“Their pull is strongest exactly where monday.com wants to grow, in the largest accounts, where control-plane depth and administrative reach decide the deal,” said Gogia.

Actions for the near-term

“It should be a measure of how much smoother, faster, and more effective the operating model becomes when AI is built into the work,” said Brue.

“A license was a known cost,” said Gogia. “A meter is a behavior, and behavior is harder to forecast than headcount.”

HPR4689: Cheap Yellow Display Project Part 8: Writing the code

Thu, 23 Jul 2026 02:06:01 +0200

This show has been flagged as Clean by the host.

Hello, again. This is Trey.

Welcome to part 8 in my Cheap Yellow Display (CYD) Project series.

If you wish to catch up on earlier episodes, you can find them on my HPR profile page https://www.hackerpublicradio.org/correspondents/0394.html

It is hard to believe that I started this project and the HPR series to document it more than a year ago. Time flies. Life happens. I spent the last 8 months so focused on work related activities that I had to set the project aside. And once I set it aside, it was difficult to get back to again. The one time I tried, I found that my son's old Windows laptop, which I had commandeered to use for the project, was once and truly dead.

We live in a different world now than we did when I began this project. Today, everything is about AI – how it is changing our world, increasing efficiencies, and even displacing certain types of jobs. "Vibe coding" is transforming the way we make software, and now everyone is a developer.

Within my organization, we are all being strongly encouraged to learn more about AI and apply it in our daily work. We are blessed to have access to a wide range of training and to powerful tools which support the process. Several colleagues within my organization and outside my organization have recommended Claude Code -- for development, for organization, for brainstorming, and for much more. My role is not that of a developer, and I have had no need for Claude Code at work. There are plenty of other tools for me to use.

But at home, I thought... I could install Claude Code at home to experiment with and to learn. And then it hit me. I wonder if I could use Claude Code to help me with my stalled CYD project.

"Hello, my name is Trey, and I am a fraud."

OK. I don't think I am a fraud, but having never used such a powerful tool to help me code, I feel a little bit like a fraud, with Claude doing the work for me. Let's talk through what we did.

As I mentioned, I was unable to use the laptop on which I created the original GUI code. But no worries, because It was all on GitHub, right?

So, I began by trying to install Claude Code on one of my Ubuntu machines. That failed miserably, and all the instructions I found online recommended running it on Windows. So I transitioned to installing Claude Code on a freshly built Windows 10 system using some old hardware which will not support Windows 11. Surprisingly, It seems that Claude wants to run within Ubuntu on Windows Subsystem for Linux (WSL).

Once I went through all the hoops for installing and setting up my basic subscription, I got to work. I created a new directory to use for all my project files, and another one for Claude to write all its output files to.

Everything I have read and heard about using Claude Code for a project like this recommended beginning by using Claude to create the project plan. So my first prompt was as follows:

"I am building a project using an ESP32 cheap yellow display. The final goal is to have a device with a graphical user interface which will allow me store several text messages, each assigned to their own specific button. Then, when one of the buttons is pressed, the message will be converted into morse code, and the message will be sent by blinking an onboard LED. I already have the initial design for the GUI coded in my github repository. Please construct a detailed step by step plan for building this application"

I wanted to keep it simple at the beginning, so even though I plan to use an opto-isolated relay for the output, I am starting out using the built in LED.

Claude asked me for the URL for my Github repository, and I provided it. After thinking for a bit, it produced a project plan.

CYD Morse Sender — Step-by-Step Build Plan

What You Already Have

- 3 fully designed LVGL screens (Send, Config, Keyboard)

- 7 send buttons with green styling on the Send Screen

- Speed slider (5–30 WPM) and program slot dropdown on Config Screen

- Text area + LVGL keyboard widget on Keyboard Screen

- Display driver and touchscreen hardware initialized (but touch read was stubbed out)

---

Phase 1: Fix Touchscreen Input

Initialize the XPT2046 on VSPI, replace the hardcoded touched = false with real touch data, calibrate raw ADC coordinates to screen pixels, and verify all three nav buttons transition screens correctly.

Phase 2: Persistent Message Storage

Use Preferences.h / NVS to store 7 messages ("msg0" – "msg6") that survive power cycles. Load on boot and populate send button labels.

Phase 3: Config Screen — Slot Selection & Speed

Wire the Program dropdown to g_selectedSlot, wire the Speed slider to g_wpm with a live WPM label, and wire the Set button to clear the text area and navigate to the Keyboard Screen.

Phase 4: Keyboard Screen — Capture and Save Text

On keyboard OK (LV_EVENT_READY): read the text area, save to NVS, update the send button label, navigate back. On Cancel: navigate back without saving.

Phase 5: Morse Code Engine

Build morse.h with the full A–Z / 0–9 lookup table, a textToMorse() converter, and Paris-standard timing (unit_ms = 1200 / wpm).

Phase 6: Non-Blocking LED Transmission

State machine (IDLE / LED_ON / LED_OFF) driven by millis() in a morseUpdate() function called every loop — no delay(). LED on GPIO 17 (blue).

Phase 7: Wire the Send Buttons

Each button calls startMorse(getMessage(slot)) if not already transmitting. Disable all send buttons during TX; re-enable when the state machine reaches the end.

Phase 8: Integration & Polish

Stop button, WPM persistence, empty-slot guard, serial debug output, final touch calibration, end-to-end test checklist.

---

Recommended order: Phase 1 → 2 → 7 (partial) → 5 → 6 → 7 (full) → 3 → 4 → 8

This was wild! Claude determined exactly how far along I was in the project, and also alerted me to the fact that the "touch read was stubbed out". Ugh. This means that the files I had uploaded to GitHub were my original GUI files (Episode 05 – HPR4532 - https://hackerpublicradio.org/eps/hpr4532/index.html ) and not the ones that I finally got working properly (Episode 07 – HPR4624 - https://hackerpublicradio.org/eps/hpr4624/index.html ). That was my own fault. Did I mention that I don't get Git? I REALLY need to learn to properly use Git!

But, we have a plan, broken down by eight numbered phases. And they seem to address all the functionality I wanted with a few additional things I had not thought about. Interestingly, even though these phases are sequentially numbered, Claud recommended that we approach them in a bizarre order: Phase 1 → 2 → 7 (partial) → 5 → 6 → 7 (full) → 3 → 4 → 8 .

Alright. Let's see what we can do. The first phase is to fix the touchscreen input.

Claude took me through it step-by-step, asking as it needed to read specific project files.

Finally, it wrote a new ui.ino code file to my speficied output directory for me to test. I copied it into the correct file location, said a quick prayer, compiled in Arduino IDE, and downloaded to the CYD.

Well, that is... interesting. The display looked nothing like it was supposed to. There were vertical green bars with smaller dashed green vertical stripes in them. I will include a picture in the show notes so that you can see what it looked like and why it was so difficult to describe.

I spent the next hour or so trying to explain what I was seeing to a chat bot. Claude recommended potential fixes which either did nothing or made the situation worse. I began questioning whether this was a good idea, how people actually gained efficiencies talking to a bot, and even several life choices.

Then I had a thought. I prompted Claude:

If I were to take a picture of the screen on the cheap yellow display and copy it into the output folder, would you be able to analyze it to better determine what is wrong and how to fix it?

Shockingly, Claude answered in the affirmative, and told me to copy the picture to the output folder and let it know when to proceed. It analyzed the picture and more of the supporting files it had copied from my GitHub, asking each time if it could access that file. It determined that my original code was written for a flavor of LVGL version 8 and I was now using LVGL 9.5.

It recommended changes, and then asked permission to make those changes, file by file. .h files & .c files, Finally, I just gave it permission to edit the files in the project folder without asking for permission for each file each time. Claude was still explaining each change, showing me exactly what would be changed, and asking for permission, so that I could review all of the changes. But now it was not asking additional permission to write to each of the impacted files.

Next, Code compiled and downloaded. Different screen, but not right. Again, I took a picture and gave it to Claude to analyze. So, Claude paused and altered the code to generate a specific test pattern overtop of the GUI.

The test pattern was supposed to cover the entire rectangular screen. But parts of the pattern were in a square on the screen and parts were not. Another photograph and analysis, told Claude that there were some rotation/screensize issues.

We repeated this several times. Some resulted in improvement, and others did not.

This is the point where I noticed something interesting. Not about Claude, specifically, or about the app. But I noticed something interesting about myself and about the process.

Previously, when I was working through some of these challenges without Claud, I found myself becoming more and more stressed, frustrated, and angry, until I found a solution. Then another problem would repeat the cycle. Success in the end was great, but the emotional extremes during the process were not always pleasant.

Now, I was effectively managing the project, and relaying information to the resource responsible for fixing the problems -- a very different experience.

But I also ran into another issue. Claude became absolutely certain that the problem revolved around the device not accurately knowing where the 4 corners of the screen were. But in reality, the output of the test pattern was rotated 90 degrees from the actual screen. It took several iterations of me insisting that the problem had to do with screen orientation and not corner coordinates. It was interesting to experience the tool doubling down on an obvious mistake, but we finally resolved that.

Again, while it was frustrating, it was much less stressful.

We proceeded to Phase 2: Persistent Message Storage where we ensured that the button labels on the send screen were stored in the devices persistent storage, so that, when they are edited to contain the message they should send, that information would survive a reboot.

Next, we combined elements of Phase 5: Morse Code Engine , Phase 6: Non-Blocking LED Transmission , and Phase 7: Wire the Send Buttons together. Building the morse code engine was an area I had been thinking about for a while. I already had working parts of something similar in the Arduino practice oscillator I have referenced a few times in this series. The code for the practice oscillator may be found on my GitHub, but it was all based on original code from jmharvey1, with my only contribution being making pin assignments variables so that the code could easily be ported to different devices.

So, I was happy that we were building the morse code engine directly. The code for it may be found in morse.h, which uses a constant character lookup table to define each character. Without any specific direction from me, Claude used the PARIS timing methods I have already described within Episode 6 of this series. It defines timing for DOT, DASH, LETTER_GAP, and WORD_GAP, and all are based on a simple calculation of 1200 ms / the number of words per minute (WPM) we wish to transmit.

Along the way, we discovered that, if we tried to use the delay() function, it would crash the program due to a conflict with the LVGL timer used for touchscreen inputs. Claude altered all the delays accordingly.

Then, Phase 3: Config Screen — Slot Selection & Speed allowed us to configure the WPM we wished to use in addition to selecting a specific Send button to reconfigure. This forced us to work on Phase 4: Keyboard Screen — Capture and Save Text which is used to type the entries for each Send button. At this point, I also decided that we would want to also use the Keyboard Screen to send ad hoc morse as we typed it.

During this phase we discovered several bugs which seemed to cause random freezes. Careful troubleshooting with messages output to the Arduino IDE's serial console helped us narrow down the causes and remedy them.

Finally all the tests worked and I am able to merrily pre-configure macro buttons with custom messages and use the CYD to send the morse code for those messages to the on-board LED at whichever rate I specify.

I have noticed in my presentation of this narrative that I repeatedly slip into the first person plural terms "we" and "us" instead of the first person singular terms "I" and "me". I have unconsciously personified Claud and recognized it as an integral part of my (formerly one person) development team.

I finally configured Claude to connect to my GitHub repo and upload all the files and documentation. We additionally created a CYD-Narrative.md file which describes in more detail all the work which was done on the project. I still do not 100% get git, but we are successfully using it.

You can find all these files in my GitHub repo ( https://github.com/jttrey3/CYD_MorseSender ) where they are shared under a GPL 3.0 license.

There are still several additional steps I plan to complete in the next few months.

1. I will be integrating an opto-isolated relay which will allow me to plug the device into the straight key input on any amateur radio. This will require a battery power source, charge controller, and more hardware.

I... make that "We" (Claude & I) will be modifying the code to support an audio side tone through an attached speaker when sending code
We will add an output selection switch to the config page to choose any combination of speaker, relay, or LED as output.
We will develop a downloadable firmware which I hope to share with the Cheap Yellow Display community.

If you can think of any additional features you would like to see integrated, please drop me an email using the address in my HPR profile.

I may also work with a friend to attempt to 3d print a case for the entire contraption, and I will be sure to record additional episodes sharing the process.

I have learned so much throughout this project, about the CYD, ESP32, GUIs, Claude Code, GitHub, and most of all, about myself.

Does using AI to develop this code make me a fraud? It still feels like it in some ways.

Does it make me more productive? ABSOLUTELY! I made consistent forward progress when I only had 30-60 minutes each day to work on it, and everything discussed in this episode was completed in less than a week. If I had been able to work on it for a few hours uninterrupted, it may have only taken me 3-5 hours.

Does it empower and inspire me to do more projects like this? 100% I feel like I had support working with me the whole way. I was less stressed overall, and it had less of an impact on the amount of and quality of time I spent with my family.

I will be wrapping up this series soon, without any more 6 month gaps, I hope.

Until next time...

Provide feedback on this episode.

German law enforcement claims to have ‘dismantled’ mega phishing-as-a-service group Kratos

Thu, 23 Jul 2026 01:57:56 +0200

A global law enforcement crackdown has seized infrastructure serving the massive phishing-as-a-service (PhaaS) group Kratos, as well resulting in the arrest of an unnamed Kratos “developer and technical administrator” in Indonesia.

The effort was managed by German law enforcement and involved agencies from the US, Indonesia and other countries.

Although a German statement claimed that the Kratos infrastructure “has been completely disabled” and that “Kratos-supported phishing campaigns can no longer be carried out,” cybersecurity analysts and consultants question how much of a dent in enterprise phishing activity will result, and how long it will last.

“A server seizure and a single arrest overseas remove infrastructure, not the intellectual property,” said Frank Dickson, group VP for security at IDC. “PhaaS kits get cloned, forked and resold routinely, and the 1,800 Kratos customers didn’t vanish. They just lost a vendor in a market where vendors get replaced fast.”

He added, “seizing 200-plus servers and arresting the developer pulls a major supplier out of that specific niche. It doesn’t touch the broader phishing economy. For every roach that you squish, there are a hundred that you do not see.”

Noah Kenney, principal consultant at Digital 520, takes an even more pessimistic view, arguing that there might not even be that much of a short-term phishing slowdown.

“What makes this different from a botnet or ransomware takedown is that the people running the attacks were never part of the organization. Kratos was just a vendor,” Kenney said. “The 1,800 customers who bought it still have their target lists, their sending infrastructure and whatever access they had already established. The tooling went dark, but the people phishing your employees last week are still working, shopping for a replacement that already exists. Enterprises should not read this as a drop in (likely) threat volume.”

One thing that the security community seems to agree on is that Kratos was a major player in the lucrative PhaaS space. But precisely determining the percentage of PhaaS activity controlled by Kratos is impossible, given that Kratos sold their kits to others. Security researchers even disagree on what they should call Kratos kits.

“Microsoft tracks this kit as SneakyLog, others tie it to Sneaky 2FA, and KnowBe4 disputes the lineage entirely. When the security industry cannot agree on what a kit is to be called, that is because renaming and reselling is continuous rather than something that happens after a raid,” Kenney said. “What actually changed this time is the arrest and the [shutdown of the] servers. Standing up new hosting is only a weekend of work, but replacing a developer who understood how to keep an adversary in the middle proxy stable and evasive at scale is harder.”

IDC’s Dickson added that the biggest value from the takedown is in the information gleaned from the seized servers.

“Kratos operated in the adversary-in-the-middle category, generating convincing fake Microsoft 365 login pages that harvest session tokens and step past MFA, the exact technique behind a lot of the business email compromise activity of the past two years,” he said. “I would love to see what law enforcement does with the customer list. That, my friend, is gold.”

Regardless, Assaf Morag, a cybersecurity researcher at Flare, dubbed the German crackdown “symbolic,” given Kratos’ reach within phishing circles.

He argued that the very nature of software makes it all but impossible to shut down in a meaningful way.

“Although this is malicious infrastructure, it is still software, and modern development and deployment practices make it relatively quick to rebuild or replicate,” he said. “Demand is likely to shift to competing providers, allowing the ecosystem to recover even if this particular operation has been disrupted.”

Pieter Arntz, malware intelligence researcher at Malwarebytes, agreed that the crackdown is disruptive but not definitive.

“This appears to be more than a routine website seizure. The reporting points to a PhaaS platform with centralized infrastructure, subscription-style customers, and Microsoft 365 session theft / MFA-bypass tooling, so taking down the backend likely hurts many downstream affiliates at once. In that sense, it is a meaningful disruption to the phishing ecosystem, not just one campaign,” Arntz said.

But, he added, “a rebrand or partial re-emergence is plausible, which is the historical pattern for PhaaS operations. Even if the core infrastructure is gone, the code, customer lists, and operator tradecraft can survive.”

This means that customers and affiliates can shift to other phishing kits, he said, so it’s likely that the takedown will create a temporary decline in Kratos-specific activity, but probably not a lasting reduction in phishing overall.

Flavio Villanustre, CISO for the LexisNexis Risk Solutions Group, also concluded that the impact of this crackdown will be short-lived.

“For each criminal organization that is dismantled, ten new ones pop out of nowhere. Unless there is a coordinated international effort by more than a few countries, this is a whack-a-mole exercise,” he said. “These are all loosely connected individuals and akin to a lernaean hydra, with two heads growing whenever you chop off one. Their leadership emerges from their lines organically without a real center of control. This makes it almost impossible to completely eliminate these criminal organizations.”

Now that's one way to block annoying online ads — developer uses a $5 dongle to squeeze in 537,000 domains into just 4MB of flash memory

Thu, 23 Jul 2026 01:32:48 +0200

Egyptian developer ZedAxis has demonstrated his $5 ad-blocking dongle in a YouTube video, with the device said to be capable of blocking over 500,000 domains

v2.1.218

Wed, 22 Jul 2026 23:32:59 +0200

What's changed

Changed /code-review to run as a background subagent, so review work no longer fills your conversation and keeps stacked slash commands as its review target
Added screen-reader announcements of deleted text for word and line deletions (Option+Delete, Ctrl+W, Cmd+Backspace, Ctrl+U, Ctrl+K) in --ax-screen-reader mode
Fixed Windows paths with \u-prefixed segments (like C:\Users\unicorn) being corrupted into CJK characters in tool inputs, which made those files inaccessible
Fixed the left arrow key discarding the conversation with no undo: presses right after editing now ask to confirm, and Esc in the agent view returns to the conversation it backgrounded
Added HTTP status and error text to claude mcp list and /mcp when a server fails to connect, and a warning for MCP config values with hidden leading or trailing whitespace
Fixed multi-line paste collapsing into one line with j in place of newlines in terminals that encode pasted newlines as Ctrl+J
Fixed /context reporting stale pre-compact token usage after compacting from the message picker
Fixed /ultrareview failing on descriptive arguments like "review my auth changes" — they now run a review of your current branch with the text applied as a note to the findings
Fixed /code-review ultra silently running a local review in non-interactive sessions — it now launches the cloud review
Fixed gateway spend metering to price Bedrock application-inference-profile ARNs and other config-mapped upstream model IDs at the configured model's rates
Fixed mojibake when a long IDE selection was truncated mid-emoji, and a case where a tool executor error could be silently dropped
Fixed an engine teardown race that could start and abandon a phantom turn, and made input pushed after close consistently rejected
Fixed spurious "[Request interrupted by user]" messages after interrupted tool calls, and an unpaired tool_use block left in the transcript when a tool aborted mid-response
Fixed VoiceOver reading "new line" instead of echoing the typed space at the end of the input in --ax-screen-reader mode
Fixed plugin and settings panels not moving the terminal cursor to the focused row, so screen readers and magnifiers can follow arrow-key navigation
Fixed crashes (maximum call stack exceeded) when a deeply nested watched directory tree was deleted or moved, and when rendering deeply nested UI trees
Fixed pull request events occasionally being lost when a session exited immediately after creating or linking a PR
Fixed the Bedrock setup wizard failing profile verification for assume-role profiles in partitioned AWS regions and on proxy-only networks
Fixed rare negative or incorrect turn duration measurements after a system clock adjustment by timing turns with a monotonic clock
Fixed the "N MCP servers need authentication" startup notice over-counting claude.ai connectors that aren't connected in claude.ai
Fixed prompt history entries being dropped or duplicated when history writes raced or failed
Fixed a retry loop that re-sent identical doomed requests after a context-overflow error with a large thinking budget; Ctrl+B backgrounding now applies the same background-shell caps as other paths
Fixed agent frontmatter hooks running from untrusted folders: hooks now require the agent file's own folder to have accepted workspace trust
Fixed fork-session lineage being lost after compaction in headless and SDK sessions
Fixed a resumed session failing every turn, or crashing on resume, when its history held a malformed delta attachment
Improved /ultrareview error feedback so Claude can correct an invalid argument instead of retrying it unchanged
Improved auto mode: the dangerous-rm, background-&, and suspicious-Windows-path checks no longer open permission dialogs; the auto-mode classifier adjudicates them instead
Improved sandbox command restrictions for IDE interactions
Improved trust dialogs to name the repository root the grant covers
Changed /deep-research to start only when invoked manually; Claude no longer launches it on its own
Changed plan mode with auto to no longer prompt for Bash commands the static analyzer can't prove read-only; the auto-mode classifier judges them instead
Added an announcement when fast mode changes as a result of switching models via /config model= or Remote Control
Changed server-managed settings so benign feature and cost toggles no longer trigger the settings-approval prompt
Changed agent markdown files to reject agent names containing :, which is reserved for plugin namespacing
Changed skills with context: fork to run in the background by default; opt out per skill with background: false
Added yes/no/on/off/1/0 (case-insensitive) as accepted values for skill and plugin frontmatter booleans, alongside true/false
Fixed remote sessions continuing to send heartbeats after their worker was replaced, which left long-lived desktop and IDE processes retrying a rejected request every few seconds forever

Linux Kernel Team Publishes 432 CVEs In Two Days

Wed, 22 Jul 2026 23:06:12 +0200

Ancient Slashdot reader alanw shares a post from the OSS Security mailing list, where sysadmin Jan Schaumann wonders what to do after the Linux kernel cranked out 432 CVEs in a little over 24 hours: "I understand the position that CVEs were always a flawed way to track or prioritize security changes... But this onslaught really shows it's not feasible to attempt to prioritize individual kernel changes. I'm not sure what to do here going forward." The Register reports: The nixCraft team speculated on social media that AI bug reports are a likely reason for all those kernel CVEs, which wouldn't be without precedent - Linus Torvalds himself said in May that the Linux kernel security mailing list had become "almost entirely unmanageable" due to AI-assisted bug hunting. Nonetheless, Torvalds has described AI as a useful tool for Linux development while still noting it can be a drag for maintainers, both from a workload standpoint and the fact "it keeps finding embarrassing bugs." [...] Unfortunately for Linux sysadmins, the position in which they find themselves in this current mess isn't one that's readily solved. CVEs might be a messy way to track and prioritize security updates, especially when hundreds of them are published over a short period, but without something better, it falls to IT and security teams to determine which vulnerabilities affect their systems and which kernel updates they need to deploy. Senior kernel maintainer Greg Kroah-Hartman replied to Jan's post, pushing back on the idea that the kernel's CVE volume is uniquely unmanageable. The kernel isn't special, he argues -- companies everywhere are finally realizing they need to re-evaluate how they update all of their systems and devices, something that's traditionally been "woefully ignored." On the "just always update" approach, Greg says that's precisely what the kernel community endorses: "This is what the kernel developer community recommends and supports. If you want support from us, do this." Can't manage it yourself? Pay a company for support, or "just use Debian or Yocto as their security practices are amazing." He points to Android as proof the approach scales, calling it "the largest deployment of software in the world" -- billions of devices kept updated "with one very-overworked developer guiding it all." As for reviewing every CVE individually, he notes this can be largely automated by intersecting the files a CVE touches with the files you actually build, which typically trims the relevant set "down to about 10% of the overall total" -- the approach enterprise distros already take for their customers. Panic-mode selective patching gets a blunt "Good luck with that!" -- regulations like the EU's Cyber Resilience Act are set to legislate that habit away ("rightfully so," in his view), and "your insurance company might wish to have a talk with you as well." Greg also warns the flood isn't over: "The number of llm-found issues is only on the rise right now, it's going to be a very long 18 months at the least to dig ourselves out of this mess, and people had BETTER be updating their systems all along the way if they expect to be secure in any way." As for the 432-CVE burst itself, he explains it was simply him catching up on a weeks-old, publicly visible review queue over the weekend -- delayed by "a perfect storm of 6 weeks straight of conferences and vacations" -- so it shouldn't have come as a surprise to anyone watching the public git repo.

Read more of this story at Slashdot.

Inflection AI returns to consumer market with Pi Journeys after Microsoft upheaval

Wed, 22 Jul 2026 22:58:21 +0200

Inflection AI, the Palo Alto startup that two years ago became Silicon Valley's most famous cautionary tale about the brutal economics of frontier AI, announced Tuesday that it is returning to the consumer market with a new research division and an experimental product built around a provocative thesis: the next competitive battleground in AI won't be raw intelligence, but relationships.

The company launched Inflection AI Labs, a public-facing research and experimentation arm, alongside Pi Journeys, the lab's first product experiment — an AI experience designed to adapt to a user's life stage, whether that's becoming a parent, taking on caregiving duties, changing careers, or aging. The announcement arrived with a research report on consumer AI habits and a substantial update to Pi, the company's flagship chatbot, adding improved voice, memory, and new agentic tools for reminders, to-do lists, and shopping.

"Inflection AI is the company. Pi is our flagship consumer product. Inflection AI Labs is where we experiment, explore personal intelligence and share more publicly. Pi Journeys is the first public experiment from Inflection AI Labs," CEO Sean White told VentureBeat in an exclusive interview.

Behind the tidy org chart is a far more interesting story: a company attempting one of the more unusual second acts in the AI industry, powered by an argument that the entire market is optimizing for the wrong thing.

Why Inflection AI believes the chatbot era's biggest flaw is that it's transactional

White's central claim is that today's AI assistants — including the industry's most capable models — are fundamentally transactional. You ask, they answer, the session ends. He believes that architecture misses most of what people actually need from artificial intelligence in their daily lives.

"One of the things that really struck us in particular, and this showed up in the research, was that a lot of the work is very transactional, and you'll hear me say a lot that we've been shifting all this from transactional to relational systems," White said. "Not everything is going to be: I do a single turn, I utter a question, I get a search response back."

White frames the industry's evolution as a progression through four kinds of intelligence. First came raw IQ — the foundation model race. Then emotional intelligence, which Inflection made its signature with Pi's famously warm conversational style. Then agentic intelligence — AI that acts rather than just talks — which White says Inflection absorbed from its enterprise work. The fourth, and the one Inflection is now staking its future on, is what the company calls relational intelligence: AI that understands not just you, but the web of people around you.

"There's so much fear about these things pushing people into loneliness,” White said. “If we design these pro-social systems as another design criteria, that actually makes a huge difference."

That design philosophy is a pointed counter-narrative to one of the loudest anxieties in consumer AI right now: that emotionally engaging chatbots deepen isolation by substituting for human contact. Inflection argues the opposite is possible — that an AI with structured knowledge of your relationships can push you back toward people rather than away from them.

Inside Pi Journeys, the AI companion that maps your relationships and life stages

Pi Journeys makes that idea concrete. When users first open the product, it asks about their life stage — caregiver, household manager, midlife transition — and then builds what White describes as specially structured memory around the people who matter in that context. From there, the system becomes proactive.

"It starts to build up memories around that, and it acts as a memory prosthetic — but in a pro-social way," White said. "It doesn't get in the way of your interactions with other people; it really helps facilitate them." The system might remind a user, for example, that a friend deserves a call, or resurface what was last discussed with a family member involved in a parent's care.

White, who spent years as chief R&D officer at Mozilla before taking Inflection's helm, was quick to flag the obvious privacy implications of an AI that maps your social graph. "We've built a lot of privacy systems into this," he said, noting users can delete and manage the people recorded in their profile. Whether consumers will trust a venture-backed AI company with a structured database of their most important relationships remains one of the biggest open questions hanging over the product — and one that enterprise buyers evaluating Inflection's technology will watch closely.

Asked why this was the first Labs experiment, White was direct: "Pi Journeys takes into account people's life stages and experiences because we have heard from users that we can provide more value in helping them navigate their lives. Pi Journeys lets us experiment with the early stages of prosocial and relational intelligence because life isn't single-player."

The product has been tested internally and with small closed groups, White said, and is now being released more broadly as an experiment rather than a finished product — a posture the Labs branding is designed to make explicit.

What Inflection's consumer AI research reveals about how people actually use chatbots

Inflection Labs' first publication, the State of Consumer AI Research Report, offers the empirical scaffolding for the strategy. The average consumer now uses roughly two different AI tools every day and three per week, the company found — evidence, in Inflection's reading, that no single assistant has locked up consumer loyalty and that the market remains contestable.

More telling is why people choose the tools they do. Respondents cited personalization, style and tone, context awareness, and — notably — emotional understanding as deciding factors. They also said they want AI to be more than a productivity engine: a coach or mentor to motivate them, a chef to suggest recipes, a DJ to curate playlists.

"One thing we're certainly finding is that a lot of that also is in work, not so much in everyday life," White said. "That's our focus right now — the everyday life part."

This is a shrewd reading of the competitive map. The best-funded AI labs are pouring resources into coding tools, enterprise agents, and developer platforms, leaving everyday consumer use cases comparatively underserved. White sees the gap clearly. "We see a lot of products that are being aimed more and more at the enterprise," he said. "As a computer scientist by training, I kind of love the IDEs as this tool, but it's not really great for everybody. There's so much regular everyday use from folks that is either purely voice or that is purely mobile."

He recalled a conversation with a conference staffer who told him she owned only a phone, no laptop — exactly the kind of user, he argued, that the industry's developer-centric product roadmaps have left behind.

How the $650 million Microsoft deal hollowed out Inflection — and set up its second act

To understand why any of this is remarkable, you have to rewind to March 2024. Inflection was then one of the hottest startups in AI, having raised $1.3 billion in mid-2023 in a round backed by Microsoft, Nvidia, Bill Gates, and Reid Hoffman — more than $1.5 billion in total. Pi had crossed one million daily active users, per Reuters.

Then, in a deal that reshaped how the industry thinks about acqui-hires, Microsoft hired away co-founder and CEO Mustafa Suleyman, chief scientist Karén Simonyan, and most of the company's roughly 70 employees, paying Inflection about $650 million largely to license its technology, as Reuters reported. Suleyman now runs Microsoft's consumer AI business. The structure of the deal drew scrutiny from the FTC and Britain's competition regulator, though the UK's Competition and Markets Authority cleared it in September 2024 and EU regulators declined to act.

White, installed as CEO in the aftermath, steered the remnant company hard toward enterprise, acquiring three startups in late 2024 — Jelled.AI, BoostKPI, and the European consulting firm Boundaryless — and telling TechCrunch that November that Inflection had no intention of competing with companies building 100,000-GPU frontier systems.

Tuesday's announcement doesn't reverse that position so much as complicate it. Asked how to think about the company today, White called it "a consumer-first strategy that bridges both consumer and enterprise efforts" — and he insists the two sides feed each other.

Enterprise deployments, including a partnership with Intel that is among the few he can name publicly, taught Inflection how to run models inside complex infrastructure. Consumer products, meanwhile, let the company iterate at speed. "The part I also like about the consumer side, and this has always been true, is that we can move faster, experiment faster, and try and learn faster," White said.

The six-month prediction: relationship-aware AI is coming to the enterprise

Buried in White's consumer pitch is the claim that should matter most to technical decision-makers. "Normally I'd say like a year, but let's call it six months," he said. "You're going to start to see a bunch of enterprises care a lot more about the relationships that are inside the enterprises and what that picture is, not just the workflows."

If White is right, the wave of workflow-automation agents currently flooding the enterprise market is only the first phase of business AI adoption — with relationship-aware systems, tested first on consumers, following close behind. Inflection is essentially using its consumer products as a live laboratory for capabilities it plans to sell into companies. It's a capital-efficient strategy for a firm that can no longer outspend rivals on training runs, and a risky one, since it depends on consumers showing up in numbers large enough to generate the learning.

The technical substance underneath is equally pragmatic. Pi today runs not on a single proprietary frontier model but on an orchestration layer routing across many models — some descended from Inflection's original fully trained cores, some fine-tuned, some open source, including work with Nvidia that White says gives Inflection access to unreleased cutting-edge models. He also took a swipe at the industry's loose vocabulary around ownership: "When people say that the model is their own, most of the time nowadays — I guess I won't name names — a lot of companies will actually take a checkpoint, and then they will fine-tune from that checkpoint. But very few people actually start from that beginning core."

That candor extends to open source, where White carefully hedged. "We're not ready to promise what I think of as true open source, and by that I mean everything," he said, invoking his Mozilla years overseeing genuinely open projects like Rust and WebAssembly.

Weights without training data and pipelines, he argued, often leave developers unable to do anything meaningful with a supposedly "open" model. "We are a PBC, and there's still a C in there," he added — a reminder that public benefit corporations still have businesses to protect. The Labs will collaborate with academic researchers, including Stanford professors who visited the company's Palo Alto office this week, and continue contributing to open projects such as PyTorch.

Can a diminished Inflection compete with AI giants spending billions?

Reid Hoffman, the LinkedIn co-founder who co-founded Inflection and stayed on through the Microsoft upheaval, framed the announcement in the sweeping terms of his recent writing on AI and human agency. "Humans should be amplified by AI, not replaced. That's the principle Pi was built on," Hoffman said in the announcement. "When that kind of agency is available to everyone, you get superagency."

The skeptic's case is easy to make. Inflection is a fraction of its former size, competing for consumer attention against products from companies spending tens of billions of dollars a year. Pi's model was state of the art in 2023; it is not in 2026. And "relational intelligence" is, for now, a brand claim awaiting proof.

But the bull case is not crazy either. Inflection's own research shows consumers already juggle multiple AI tools and choose them for qualities — tone, emotional understanding, personalization — that frontier labs treat as afterthoughts. The company kept its technology, its Microsoft licensing windfall, and a defensible enterprise niche in on-premise, emotionally intelligent deployments. And it is targeting the one consumer segment — everyday, mobile-first, voice-first life management — that the coding-obsessed giants have largely ignored.

Asked what success looks like twelve months from now, White declined to talk numbers. "It's less about scale for scale's sake and more about scaling for impact by empowering people and improving their lives," he said. "Over the next year, success means leading the market towards relational intelligence and transforming AI interactions from transactional to relational."

Two years ago, Microsoft walked away with Inflection's founders, its staff, and its shot at the frontier — but it left behind the one idea the giants still haven't figured out how to build: an AI that knows the people in your life matter more than the tasks on your list. Inflection is betting the company, again, that the idea was the valuable part all along.

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Two Ways To Mess Up Your JWT Safety Net In Your Own Lab.

Wed, 22 Jul 2026 20:59:45 +0200

In the lab we’re going to build today, we’ll talk about JWTs and how they can affect the security of your website. This is a lab that’s been requested, and I could not be happier that the community is actually suggesting things for me to build. It’s a privilege to do this.

JWT As Security Measure

JSON Web Tokens are used a lot by developers. They provide a way to send ‘data’ with them (e.g., a role from a user), to check for authorization. A JWT consists of 3 parts, separated by a ..

Let’s take this JWT as an example.

eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIxMjM0NTY3ODkwIiwibmFtZSI6IkpvaG4gRG9lIiwiYWRtaW4iOnRydWUsImlhdCI6MTUxNjIzOTAyMn0.KMUFsIDTnFmyG3nMiGM6H9FNFUROf3wh7SmqJp-QV30

The first part eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9 is called 'the header'. It holds information on the type of the JWT. The second part eyJzdWIiOiIxMjM0NTY3ODkwIiwibmFtZSI6IkpvaG4gRG9lIiwiYWRtaW4iOnRydWUsImlhdCI6MTUxNjIzOTAyMn0 is the payload, which holds the claims. This is where that 'data' lives. If you decode this with Base64url you'll learn that this token belongs to John Doe and that he is an administrator. The last part is the important part. This is the signature. Each JWT should be signed with a strong password. Ideally that password is saved in a .env file on the server and not in the code.

So now you might wonder, well if it’s signed, nothing can go wrong right? Well, if that were true, I wouldn’t spend my evenings writing these kinds of blog posts now would I 😅.

Let’s Build The Lab Already

You’re right to think this by now. Enough theory, let’s do some hands on keyboard. As always in this series, I’ll provide you with the lab structure and some code to get us started.

Lab Tree

/mediumLabs
└── /JWT
    ├── server.js
    ├── login.html
    ├── index.html
    ├── admin.html
    ├── /node_modules
    ├── /package-lock.json
    └── /package.json

Code

server.js

const express = require('express');
const jwt = require('jsonwebtoken');
const path = require('path');
const Database = require('better-sqlite3');
const app = express();
const JWT_SECRET = 'secret';
const db = new Database(':memory:');
db.exec(`
  CREATE TABLE users (
    id INTEGER PRIMARY KEY,
    username TEXT,
    password TEXT,
    role TEXT
  )
`);
db.prepare("INSERT INTO users (username, password, role) VALUES ('user', 'password', 'user')").run();
app.use(express.urlencoded({ extended: false }));

app.get('/', (req, res) => res.sendFile(path.join(__dirname, 'login.html')));
function getToken(req) {
  const match = (req.headers.cookie || '').match(/token=([^;]+)/);
  return match ? match[1] : null;
}
app.post('/login', (req, res) => {
  const { username, password } = req.body;
  const user = db.prepare('SELECT * FROM users WHERE username = ? AND password = ?').get(username, password);
  if (!user) return res.redirect('/?error=Invalid+credentials');
  const token = jwt.sign({ username: user.username, role: user.role }, JWT_SECRET);
  res.setHeader('Set-Cookie', `token=${token}; Path=/`);
  res.redirect('/index.html');
});
app.get('/index.html', (req, res) => {
  try {
    res.sendFile(path.join(__dirname, 'index.html'));
  } catch {
    res.redirect('/');
  }
});
app.get('/admin', (req, res) => {
});

app.listen(3000, () => console.log('Listening on http://localhost:3000'));

login.html




    
    
    Login


    Login

    
        
            Username: 
        

        
            Password:

index.html

Home

Welcome!

You are logged in as .

Go to Admin Panel

admin.html

Admin Panel

Welcome, admin. Here is the secret flag:

FLAG{JWT}

Back to Home

Let’s Add Some Vulnerabilities

Ok, so now we have our base code, and we need to implement the functionality that will make sure only people with the role admin can navigate to /admin. First off, run npm run dev to see your code actually start.

In a very, very wrong way we already tried implementing security in admin.html. When you look inside the

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