Team IT Security - 📰 Alle Kategorien

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.

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.
Want to join?

The May 2026 Security Update Review

Sat, 25 Jul 2026 19:02:56 +0200

I’m currently in Berlin helping set up for Pwn2Own Berlin, but that doesn’t stop Patch Tuesday from coming, and it’s another big one. At least nothing is listed as being in the wild – for now. Take a break from your regularly scheduled activities and let’s take a look at the latest security patches from Adobe and Microsoft. Due to technical difficulties, there will not be a video companion for this month.

Adobe Patches for May 2026

For May, Adobe released 10 bulletins addressing 52 unique CVEs in Adobe Commerce, After Effects, Adobe Connect, Illustrator, Media Encoder, Premiere Pro, Substance 3D Painter, Substance 3D Sampler, Content Authenticity SDK, and the Adobe Substance 3D Designer. Here’s this month’s overview table:

Bulletin ID	Product	CVE Count	Highest Severity	Highest CVSS	Exploited	Deployment Priority
APSB26-49	Adobe Commerce	15	Critical	8.7	No	2
APSB26-48	Adobe After Effects	4	Critical	7.8	No	3
APSB26-50	Adobe Connect	2	Critical	9.6	No	3
APSB26-51	Adobe Illustrator	4	Critical	7.8	No	3
APSB26-47	Adobe Media Encoder	2	Critical	7.8	No	3
APSB26-46	Adobe Premiere Pro	3	Critical	7.8	No	3
APSB26-55	Adobe Substance 3D Painter	2	Critical	7.8	No	3
APSB26-54	Adobe Substance 3D Sampler	1	Critical	7.8	No	3
APSB26-53	Content Authenticity SDK	14	Critical	7.5	No	3
APSB26-52	Adobe Substance 3D Designer	5	Important	6.3	No	3
TOTAL	10 bulletins	52

The obvious priority this month is the patch for Commerce, with its 15 bugs and deployment priority of 2. The Connect fix should also rank up there since both of its CVEs are CVSS 9s. Beyond those, it’s a pretty typical month for Adobe, with most of the bugs either being cross-site scripting (XSS) or open-and-own code executions.

Microsoft Patches for May 2026

This month, Microsoft released a whopping 138 new CVEs in Windows and Windows components, Office and Office Components, Microsoft Edge (Chromium-based), Azure, .NET and Visual Studio, Copilot Chat, Github Copilot, M365 Copilot, SQL Server, TCP/IP, and the Telnet Client – yes, the Telnet client. Two of these bugs were reported through the TrendAI ZDI program. 30 of these bugs are rated Critical, three are rated as Moderate, one is rated Low, and the rest are rated Important in severity.

This large volume of fixes follows the largest monthly release in Microsoft’s history and reflects the trend across the industry of a high number of submissions. While not all of these bugs were found by AI, it’s likely they had an AI-related component – even if it was just AI writing the submission. I should also point out the Pwn2Own Berlin occurs in just a few days, and it’s typical for vendors to patch as much as they can before the event.

None of the bugs patched by Microsoft this month are listed as publicly known or under active attack at the time of release, so we’ve got that going for us. Let’s take a closer look at some of the more interesting updates for this month, starting with a nasty-looking bug in DNS:

- CVE-2026-41096 - Windows DNS Client Remote Code Execution Vulnerability
This patch fixes a heap-based buffer overflow in the DNS Client triggered by a malicious DNS response. No authentication or user interaction needed, and since the DNS Client runs on virtually every Windows machine, the attack surface is enormous. An attacker with a position to influence DNS responses (MitM, rogue server) could achieve unauthenticated RCE across your enterprise.

- CVE-2026-41089 - Windows Netlogon Remote Code Execution Vulnerability
This update covers another CVSS 9.8 bug, which is a stack-based buffer overflow that lets an unauthenticated remote attacker execute code on a domain controller by sending a specially crafted network request — no credentials, no user interaction required. Yup – that makes it wormable. This is the highest-impact bug that requires immediate patching: a compromised domain controller is a compromised domain.

- CVE-2026-42898 - Microsoft Dynamics 365 On-Premises Remote Code Execution Vulnerability
This bug rates a CVSS 9.9(!) and represents a code injection in Dynamics 365. It allows any authenticated user to execute code with a scope change, meaning exploitation can break out and affect resources beyond the vulnerable component itself. Scope changes are pretty rare, so if you’re running Dynamics 365 On-Prem, definitely test and deploy this patch quickly.

- CVE-2026-40415 - Windows TCP/IP Remote Code Execution Vulnerability
This bug in the TCP/IP stack results from a use-after-free (UAF) and could allow a remote, unauthenticated threat actor to execute code without user interaction. That makes this another wormable bug. However, this one is much less likely to be exploited. The target needs to be under sustained low-memory (memory pressure) conditions, which is pretty rare. Still, no need to tempt fate here. Test and deploy this one quickly.

Here’s the full list of CVEs released by Microsoft for May 2026:

CVE	Title	Severity	CVSS	Public	Exploited	Type
CVE-2026-35435	Azure AI Foundry Elevation of Privilege Vulnerability	Critical	8.6	No	No	EoP
CVE-2026-35428	Azure Cloud Shell Spoofing Vulnerability	Critical	9.6	No	No	Spoofing
CVE-2026-42826	Azure DevOps Information Disclosure Vulnerability	Critical	10	No	No	Info
CVE-2026-32207	Azure Machine Learning Notebook Spoofing Vulnerability	Critical	8.8	No	No	Spoofing
CVE-2026-33109	Azure Managed Instance for Apache Cassandra Remote Code Execution Vulnerability	Critical	9.9	No	No	RCE
CVE-2026-33844	Azure Managed Instance for Apache Cassandra Remote Code Execution Vulnerability	Critical	9	No	No	RCE
CVE-2026-41105	Azure Monitor Action Group Notification System Elevation of Privilege Vulnerability	Critical	8.1	No	No	EoP
CVE-2026-33111	Copilot Chat (Microsoft Edge) Information Disclosure Vulnerability	Critical	7.5	No	No	Info
CVE-2026-26129	M365 Copilot Information Disclosure Vulnerability	Critical	7.5	No	No	Info
CVE-2026-26164	M365 Copilot Information Disclosure Vulnerability	Critical	7.5	No	No	Info
CVE-2026-33821	Microsoft Dynamics 365 Customer Insights Elevation of Privilege Vulnerability	Critical	7.7	No	No	EoP
CVE-2026-42898	Microsoft Dynamics 365 On-Premises Remote Code Execution Vulnerability	Critical	9.9	No	No	RCE
CVE-2026-40379	Microsoft Enterprise Security Token Service (ESTS) Spoofing Vulnerability	Critical	9.3	No	No	Spoofing
CVE-2026-40363	Microsoft Office Remote Code Execution Vulnerability	Critical	8.4	No	No	RCE
CVE-2026-40358	Microsoft Office Remote Code Execution Vulnerability	Critical	8.4	No	No	RCE
CVE-2026-34327	Microsoft Partner Center Spoofing Vulnerability	Critical	8.2	No	No	Spoofing
CVE-2026-40365	Microsoft SharePoint Server Remote Code Execution Vulnerability	Critical	8.8	No	No	RCE
CVE-2026-41103	Microsoft SSO Plugin for Jira & Confluence Elevation of Privilege Vulnerability	Critical	9.1	No	No	EoP
CVE-2026-33823	Microsoft Team Events Portal Information Disclosure Vulnerability	Critical	9.6	No	No	Info
CVE-2026-40364	Microsoft Word Remote Code Execution Vulnerability	Critical	8.4	No	No	RCE
CVE-2026-40366	Microsoft Word Remote Code Execution Vulnerability	Critical	8.4	No	No	RCE
CVE-2026-40361	Microsoft Word Remote Code Execution Vulnerability	Critical	8.4	No	No	RCE
CVE-2026-40367	Microsoft Word Remote Code Execution Vulnerability	Critical	8.4	No	No	RCE
CVE-2026-42831	Office for Android Remote Code Execution Vulnerability	Critical	7.8	No	No	RCE
CVE-2026-41096	Windows DNS Client Remote Code Execution Vulnerability	Critical	9.8	No	No	RCE
CVE-2026-35421	Windows GDI Remote Code Execution Vulnerability	Critical	7.8	No	No	RCE
CVE-2026-40403	Windows Graphics Component Remote Code Execution Vulnerability	Critical	8.8	No	No	RCE
CVE-2026-40402	Windows Hyper-V Elevation of Privilege Vulnerability	Critical	9.3	No	No	EoP
CVE-2026-32161	Windows Native WiFi Miniport Driver Remote Code Execution Vulnerability	Critical	7.5	No	No	RCE
CVE-2026-41089	Windows Netlogon Remote Code Execution Vulnerability	Critical	9.8	No	No	RCE
CVE-2026-32175	.NET Core Tampering Vulnerability	Important	4.3	No	No	Tampering
CVE-2026-32177	.NET Elevation of Privilege Vulnerability	Important	7.3	No	No	EoP
CVE-2026-35433	.NET Elevation of Privilege Vulnerability	Important	7.3	No	No	EoP
CVE-2025-54518 *	AMD: CVE-2025-54518 CPU OP Cache Corruption	Important		No	No	RCE
CVE-2026-42899	ASP.NET Core Denial of Service Vulnerability	Important	7.5	No	No	DoS
CVE-2026-40381	Azure Connected Machine Agent Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-42823 †	Azure Logic Apps Elevation of Privilege Vulnerability	Important	9.9	No	No	EoP
CVE-2026-33833	Azure Machine Learning Notebook Spoofing Vulnerability	Important	8.2	No	No	Spoofing
CVE-2026-32204	Azure Monitor Agent Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-42830	Azure Monitor Agent Metrics Extension Elevation of Privilege Vulnerability	Important	6.5	No	No	EoP
CVE-2026-33117	Azure SDK for Java Security Feature Bypass Vulnerability	Important	9.1	No	No	SFB
CVE-2026-41109	GitHub Copilot and Visual Studio Code Security Feature Bypass Vulnerability	Important	8.8	No	No	SFB
CVE-2026-35424	Internet Key Exchange (IKE) Protocol Denial of Service Vulnerability	Important	7.5	No	No	DoS
CVE-2026-41614	M365 Copilot for Desktop Spoofing Vulnerability	Important	6.2	No	No	Spoofing
CVE-2026-41100	Microsoft 365 Copilot for Android Spoofing Vulnerability	Important	4.4	No	No	Spoofing
CVE-2026-40377	Microsoft Cryptographic Services Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-41094	Microsoft Data Formulator Remote Code Execution Vulnerability	Important	8.8	No	No	RCE
CVE-2026-40417	Microsoft Dynamics 365 Business Central Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-42833	Microsoft Dynamics 365 On-Premises Remote Code Execution Vulnerability	Important	9.1	No	No	RCE
CVE-2026-42838	Microsoft Edge (Chromium-based) Elevation of Privilege Vulnerability	Important	5.4	No	No	EoP
CVE-2026-40360	Microsoft Excel Information Disclosure Vulnerability	Important	7.8	No	No	Info
CVE-2026-40359	Microsoft Excel Remote Code Execution Vulnerability	Important	7.8	No	No	RCE
CVE-2026-40362	Microsoft Excel Remote Code Execution Vulnerability	Important	7.8	No	No	RCE
CVE-2026-42832	Microsoft Excel Spoofing Vulnerability	Important	7.7	No	No	Spoofing
CVE-2026-34329	Microsoft Message Queuing (MSMQ) Remote Code Execution Vulnerability	Important	8.8	No	No	RCE
CVE-2026-40419	Microsoft Office Click-To-Run Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-40418	Microsoft Office Click-To-Run Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-35436	Microsoft Office Click-To-Run Elevation of Privilege Vulnerability	Important	8.8	No	No	EoP
CVE-2026-40420	Microsoft Office Click-To-Run Elevation of Privilege Vulnerability	Important	8.8	No	No	EoP
CVE-2026-42893	Microsoft Outlook for iOS Tampering Vulnerability	Important	7.4	No	No	Tampering
CVE-2026-40374	Microsoft Power Automate Desktop Information Disclosure Vulnerability	Important	6.5	No	No	Info
CVE-2026-41102	Microsoft PowerPoint for Android Spoofing Vulnerability	Important	7.1	No	No	Spoofing
CVE-2026-35439	Microsoft SharePoint Server Remote Code Execution Vulnerability	Important	8.8	No	No	RCE
CVE-2026-40368	Microsoft SharePoint Server Remote Code Execution Vulnerability	Important	8	No	No	RCE
CVE-2026-33110	Microsoft SharePoint Server Remote Code Execution Vulnerability	Important	8.8	No	No	RCE
CVE-2026-33112	Microsoft SharePoint Server Remote Code Execution Vulnerability	Important	8.8	No	No	RCE
CVE-2026-40357	Microsoft SharePoint Server Remote Code Execution Vulnerability	Important	8.8	No	No	RCE
CVE-2026-32185	Microsoft Teams Spoofing Vulnerability	Important	5.5	No	No	Spoofing
CVE-2026-41101	Microsoft Word for Android Spoofing Vulnerability	Important	7.1	No	No	Spoofing
CVE-2026-35440	Microsoft Word Information Disclosure Vulnerability	Important	5.5	No	No	Info
CVE-2026-40421	Microsoft Word Information Disclosure Vulnerability	Important	4.3	No	No	Info
CVE-2026-41097	Secure Boot Security Feature Bypass Vulnerability	Important	6.7	No	No	SFB
CVE-2026-40370 †	SQL Server Remote Code Execution Vulnerability	Important	8.8	No	No	RCE
CVE-2026-41613	Visual Studio Code Elevation of Privilege Vulnerability	Important	8.8	No	No	EoP
CVE-2026-41612	Visual Studio Code Information Disclosure Vulnerability	Important	5.5	No	No	Info
CVE-2026-41611	Visual Studio Code Remote Code Execution Vulnerability	Important	7.8	No	No	RCE
CVE-2026-41610	Visual Studio Code Security Feature Bypass Vulnerability	Important	6.3	No	No	SFB
CVE-2026-33839	Win32k Elevation of Privilege Vulnerability	Important	7	No	No	EoP
CVE-2026-33840	Win32k Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-34330	Win32k Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-34331	Win32k Elevation of Privilege Vulnerability	Important	7	No	No	EoP
CVE-2026-35423	Windows 11 Telnet Client Information Disclosure Vulnerability	Important	5.4	No	No	Info
CVE-2026-35438	Windows Admin Center Elevation of Privilege Vulnerability	Important	8.3	No	No	EoP
CVE-2026-41086	Windows Admin Center in Azure Portal Elevation of Privilege Vulnerability	Important	8.8	No	No	EoP
CVE-2026-34344	Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-34345	Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability	Important	7	No	No	EoP
CVE-2026-35416	Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability	Important	7	No	No	EoP
CVE-2026-41088	Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-34343	Windows Application Identity (AppID) Subsystem Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-35418	Windows Cloud Files Mini Filter Driver Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-33835	Windows Cloud Files Mini Filter Driver Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-34337	Windows Cloud Files Mini Filter Driver Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-40407	Windows Common Log File System Driver Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-40397	Windows Common Log File System Driver Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-42896	Windows DWM Core Library Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-35419	Windows DWM Core Library Information Disclosure Vulnerability	Important	5.5	No	No	Info
CVE-2026-34336	Windows DWM Core Library Information Disclosure Vulnerability	Important	7.8	No	No	Info
CVE-2026-33834	Windows Event Logging Service Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-32209	Windows Filtering Platform (WFP) Security Feature Bypass Vulnerability	Important	4.4	No	No	SFB
CVE-2026-33841	Windows Kernel Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-35420	Windows Kernel Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-40369	Windows Kernel Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-34332	Windows Kernel-Mode Driver Remote Code Execution Vulnerability	Important	8	No	No	RCE
CVE-2026-34339	Windows Lightweight Directory Access Protocol (LDAP) Denial of Service Vulnerability	Important	5.5	No	No	DoS
CVE-2026-34341	Windows Link-Layer Discovery Protocol (LLDP) Elevation of Privilege Vulnerability	Important	7	No	No	EoP
CVE-2026-33838	Windows Message Queuing (MSMQ) Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-34342	Windows Print Spooler Elevation of Privilege Vulnerability	Important	7	No	No	EoP
CVE-2026-41095	Windows Projected File System Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-34340	Windows Projected File System Elevation of Privilege Vulnerability	Important	7	No	No	EoP
CVE-2026-40398	Windows Remote Desktop Services Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-21530	Windows Rich Text Edit Elevation of Privilege Vulnerability	Important	6.7	No	No	EoP
CVE-2026-32170	Windows Rich Text Edit Elevation of Privilege Vulnerability	Important	6.7	No	No	EoP
CVE-2026-40410	Windows SMB Client Elevation of Privilege Vulnerability	Important	7	No	No	EoP
CVE-2026-35415	Windows Storage Spaces Controller Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-34350	Windows Storport Miniport Driver Denial of Service Vulnerability	Important	6.5	No	No	DoS
CVE-2026-40405	Windows TCP/IP Denial of Service Vulnerability	Important	7.5	No	No	DoS
CVE-2026-40414	Windows TCP/IP Denial of Service Vulnerability	Important	7.4	No	No	DoS
CVE-2026-40401	Windows TCP/IP Denial of Service Vulnerability	Important	6.2	No	No	DoS
CVE-2026-40413	Windows TCP/IP Denial of Service Vulnerability	Important	7.4	No	No	DoS
CVE-2026-35422	Windows TCP/IP Driver Security Feature Bypass Vulnerability	Important	6.5	No	No	SFB
CVE-2026-34351	Windows TCP/IP Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-40399	Windows TCP/IP Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-34334	Windows TCP/IP Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-40406	Windows TCP/IP Information Disclosure Vulnerability	Important	7.5	No	No	Info
CVE-2026-33837	Windows TCP/IP Local Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-40415	Windows TCP/IP Remote Code Execution Vulnerability	Important	8.1	No	No	RCE
CVE-2026-42825	Windows Telephony Service Elevation of Privilege Vulnerability	Important	7	No	No	EoP
CVE-2026-34338	Windows Telephony Service Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-40382	Windows Telephony Service Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-40380	Windows Volume Manager Extension Driver Remote Code Execution Vulnerability	Important	6.2	No	No	RCE
CVE-2026-40408	Windows WAN ARP Driver Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-34333	Windows Win32k Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-34347	Windows Win32k Elevation of Privilege Vulnerability	Important	7	No	No	EoP
CVE-2026-35417	Windows Win32k Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-42891	Microsoft Edge (Chromium-based) for Android Spoofing Vulnerability	Moderate	6.5	No	No	Spoofing
CVE-2026-35429	Microsoft Edge (Chromium-based) for Android Spoofing Vulnerability	Moderate	4.3	No	No	Spoofing
CVE-2026-41107	Microsoft Edge (Chromium-based) Information Disclosure Vulnerability	Moderate	7.4	No	No	Info
CVE-2026-40416	Microsoft Edge (Chromium-based) for Android Spoofing Vulnerability	Low	4.3	No	No	Spoofing

* Indicates this CVE had been released by a third party and is now being included in Microsoft releases.

† Indicates further administrative actions are required to fully address the vulnerability.

Looking at the other Critical-rated bugs in this month’s release, there are quite a few scary-looking bugs (including a CVSS 10!), but there’s no action for the end user as Microsoft has already mitigated these bugs and is just now documenting them. There’s also this month’s crop of Office bugs where the Preview Pane is an attack vector. However, the bug in Office for Android does not have the Preview Pane vector; it’s simple open and own. The bug in the WiFi driver needs a network adjacent attacker. The SharePoint bug requires authentication, but anyone with site privileges has the authentication needed. The bug in SSO Plugin for Jira & Confluence should really be called an authentication bypass, since it allows an unauthenticated attacker to gain access to a system.

Looking at the other code execution bugs, most are of the open and own variety as expected. The bug in Dynamic 365 (On Prem) requires high privileges. The Message Queueing bug requires an adjacent attacker. The bug in SQL Server requires authentication, but as usual, patching won’t be straightforward. Finally, there’s a bug in the kernel that leads to code execution. Most kernel bugs are privilege escalations, but this one could allow code execution if an attacker sends specially crafted NVMe over Fabrics (NVMe‑oF) response messages during the connection handshake process that contains an invalid header length value. Neat.

As usual, the vast majority of the Microsoft release fixes Elevation of Privilege (EoP) bugs. Also as usual, most simply lead to local attackers executing their code at SYSTEM-level privileges or administrative privileges, so there’s not much to add without further technical details about the bugs themselves. There are also a few bugs that just state the attacker could “gain ELEVATED privileges.” How obtuse. The bugs in Azure allow an attacker to access data otherwise hidden from them. The Edge bug allows threat actors to elevate to the privileges of the running application. The bug in Visual Studio allows attackers to get permissions associated with the MCP Server’s managed identity. Finally, there are a couple of sandbox escapes, too, which are always useful.

This month's update includes six Security Feature Bypass vulnerabilities. The most severe is in the Azure SDK for Java (CVSS 9.1). An attacker over the network can bypass the integrity protection provided by authentication tags on encrypted data, effectively manipulating encrypted input in a way that slips past integrity checks during decryption. Close behind is the bypass affecting the GitHub Copilot integration in Visual Studio Code (CWE-74). This one requires a user interaction, but it allows an attacker to circumvent the path validation safeguards that normally control which files Copilot is permitted to modify. The other Visual Studio Code bypass involves cross-site scripting, improper link resolution, and information exposure triggered when a user opens or views a maliciously crafted notebook. On the Windows networking side there are two bypasses. The first hits the Windows TCP/IP driver via an authentication bypass using an alternate channel. The other impacts the Windows Filtering Platform through improper access control, allowing a local, low-privileged attacker to bypass FQDN-based network security rules. Finally, there’s a Secure Boot bypass that, you guessed it, bypasses secure boot features.

Moving on to the Information Disclosure bugs fixed this month, we have 15 different CVEs. As usual, the majority of these simply result in info leaks consisting of unspecified memory contents or memory addresses. The bug in Power Automate could expose data marked “Sensitive” within Power Automate Desktop flows. One of the Word bugs could disclose NLTM hashes. The bug in Edge could disclose your cookies, which seems rude. The bug in Visual Studio could expose file path information. Finally, there’s a bug in Telnet for Windows 11 that leaks information being used by Telnet at the time. I didn’t even realize Windows 11 still had a telnet client.

The May release contains 10 spoofing bugs (plus the ones already addressed by Microsoft). The bug in Azure Machine Learning Notebooks vulnerability requires user interaction, but it could expose info through the Azure ML web interface to the attacker. There’s a cluster of fixes for Microsoft's mobile Office suite on Android. Excel, Word, and PowerPoint for Android all carry spoofing flaws rooted in improper access control. Two Copilot products are also affected by spoofing vulns. The M365 Copilot for Desktop has no details provided. The M365 Copilot for Android variant requires low privileges and producing only limited impact on confidentiality and integrity. Microsoft Teams for Android rounds out the mobile app spoofing bugs. Three Edge bugs close things out, all involving misrepresentation of information in the browser UI.

There are two Tampering bugs in this month’s release. The one in .NET Core allows threat actors to write files to an affected system. The other is in Outlook for iOS and manifests as a command injection bug.

There are eight DoS bugs in the May release, but as always, Microsoft provides little to no actionable information about the vulnerabilities. The most interesting from a practical standpoint are two TCP/IP bugs that allow a low-privilege Hyper-V guest to crash the host. Both are triggered from the adjacent network. On the broader network-exposure side, the ASP.NET Core bug is a straightforward infinite loop condition — an unauthenticated attacker sends a crafted request over the network and the server stops responding.

No new advisories are being released this month.

Looking Ahead

Assuming I survive Pwn2Own Berlin (which is looking iffy at the moment), I’ll return on June 9th on what will hopefully be a smaller release than this one. Until then, stay safe, happy patching, and may all your reboots be smooth and clean!

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.

The February 2026 Security Update Review

Sat, 25 Jul 2026 19:00:44 +0200

I have survived the biggest Pwn2Own ever, but I’m back in Tokyo for the second Patch Tuesday of 2026. My location never stops Patch Tuesday from coming, so let’s take a look at the latest security patches from Adobe and Microsoft. If you’d rather watch the full video recap covering the entire release, you can check it out here:

Adobe Patches for February 2026

For February, Adobe released nine bulletins addressing 44 unique CVEs in Adobe Audition, After Effects, InDesign, Substance 3D Designer, Substance 3D Stager, Adobe Bridge, Substance 3D Modeler, Lightroom Classic, and the Adobe DNG Software Development Kit (SDK). The largest update here is for After Effects, which fixes 13 Critical and two Important rated bugs. The patch for Substance 3D Designer is on the larger side with seven fixes, but only two of those are Critical. On the other hand, the fix for Substance 3D Stager corrects five Critical-rated bugs that could lead to code execution. The Audition patch fixes six bugs, but only one is Critical.

The other patches are smaller in size. The fix for the Adobe DNG Software Development Kit (SDK) corrects two Critical and two Important-rated bugs. The InDesign patch fixes three bugs, but only one is Critical. The update for Adobe Bridge fixes two Critical bug that could lead to code execution. The patch for Lightroom Classic addresses a single Critical bug, and the release is wrapped up with a patch for Substance 3D Modeler that fixes a single, Important-rated memory link.

None of the bugs fixed by Adobe this month are listed as publicly known or under active attack at the time of release, and all of the updates released by Adobe this month are listed as deployment priority 3.

Microsoft Patches for February 2026

This month, Microsoft drops 58 new CVEs in Windows and Windows components, Office and Office Components, Azure, Microsoft Edge (Chromium-based), .NET and Visual Studio, GitHub Copilot, Mailslot FS, Exchange Server, Internet Explorer (!), Power BI, Hyper-V Server, and the Windows Subsystem for Linux. Counting the third-party and Chromium updates listed in the release, it brings the total number of CVEs to 62. One of the bugs in the Windows Graphics component was submitted through the ZDI program. Five of these bugs are rated Critical, two are rated Moderate, and the rest are rated Important in severity.

It’s typical to see this number of CVEs released in February, but the number of bugs under active attack is extraordinarily high. Microsoft lists six bugs being exploited at the time of release, with three of these listed as publicly known. Last month only had a single bug being exploited, although there were twice as many CVEs patched. We’ll see if we’re on our way to another “hot exploit summer” as we saw a few years ago or if this is just an aberration.

Let’s take a closer look at some of the more interesting updates for this month, starting with the bugs under active attack:

- CVE-2026-21510 - Windows Shell Security Feature Bypass Vulnerability
This bug is listed as a security feature bypass, but it could also be classified as code execution. An attacker can bypass Windows SmartScreen and Windows Shell security prompts to execute code on a target system. This bug is also listed as publicly known, but Microsoft doesn’t say where. There is user interaction here, as the client needs to click a link or a shortcut file. Still, a one-click bug to gain code execution is a rarity. Definitely test and deploy this fix quickly.

- CVE-2026-21514 - Microsoft Word Security Feature Bypass Vulnerability
This bug also requires user interaction in the form of opening a Word document, but that’s all that’s required to bypass protections to dangerous COM/OLE controls. Thankfully, the Preview Pane is not an attack vector here. However, users are well known to open lots of documents they receive in e-mail. This bypass could also result in code execution if the right COM/OLE control is hit. This is also listed as publicly known, so add this to the list to test and deploy quickly.

- CVE-2026-21519 - Desktop Window Manager Elevation of Privilege Vulnerability
This is the second month in a row that a DWM was listed as being exploited in the wild. That leads me to believe the first patch didn’t completely resolve the vulnerability. Same as last month, this bug allows attackers to run code with SYSTEM privileges. Bugs of this type are typically paired with a code execution bug to take over a system. As always, Microsoft offers no indication of how widespread these exploits may be.

- CVE-2026-21533 - Windows Remote Desktop Services Elevation of Privilege Vulnerability
Don’t let the word “Remote” in the title fool you – this is a local bug that allows attackers to run code with SYSTEM privileges. It’s interesting that Microsoft lists “Improper privilege management” as the root cause for this issue. If the system is running Remote Desktop Services, it’s probably a juicy target for attackers to move laterally after an initial breach. Add this one to the list of patches to test and deploy immediately.

- CVE-2026-21513 - Internet Explorer Security Feature Bypass Vulnerability
Although long gone by many measurements, IE does still exist on Windows systems, and calling it always results in a vulnerability somehow. This bug manifests similarly to the Shell bug above, as it requires user interaction but could result in code execution. The bypass here is simply the ability to reach IE, which shouldn’t be possible. Again, test and deploy this fix quickly.

- CVE-2026-21525 - Windows Remote Access Connection Manager Denial of Service Vulnerability
It’s unusual to see DoS bugs being used in active attacks, but that’s what we have here. A null pointer deref in the Windows Remote Access Connection Manager allows an unauthorized attacker to deny service locally. Most null pointer derefs cause the application or service to crash, but it’s not clear if it will automatically restart. I would exercise caution and patch quickly either way.

Here’s the full list of CVEs released by Microsoft for February 2026:

CVE	Title	Severity	CVSS	Public	Exploited	TYPE
CVE-2026-21514	Microsoft Word Security Feature Bypass Vulnerability	Important	7.8	Yes	Yes	SFB
CVE-2026-21510	Windows Shell Security Feature Bypass Vulnerability	Important	8.8	Yes	Yes	SFB
CVE-2026-21513	Internet Explorer Security Feature Bypass Vulnerability	Important	8.8	Yes	Yes	SFB
CVE-2026-21519	Desktop Window Manager Elevation of Privilege Vulnerability	Important	7.8	No	Yes	EoP
CVE-2026-21533	Windows Remote Desktop Services Elevation of Privilege Vulnerability	Important	7.8	No	Yes	EoP
CVE-2026-21525	Windows Remote Access Connection Manager Denial of Service Vulnerability	Moderate	6.2	No	Yes	DoS
CVE-2026-21511	Microsoft Outlook Spoofing Vulnerability	Important	7.5	No	No	Spoofing
CVE-2023-2804 *	Red Hat, Inc. CVE-2023-2804: Heap Based Overflow libjpeg-turbo	Important	6.5	Yes	No	RCE
CVE-2026-24302	Azure Arc Elevation of Privilege Vulnerability	Critical	8.6	No	No	EoP
CVE-2026-24300	Azure Front Door Elevation of Privilege Vulnerability	Critical	9.8	No	No	EoP
CVE-2026-21532	Azure Function Information Disclosure Vulnerability	Critical	8.2	No	No	Info
CVE-2026-21522	Microsoft ACI Confidential Containers Elevation of Privilege Vulnerability	Critical	6.7	No	No	EoP
CVE-2026-23655	Microsoft ACI Confidential Containers Information Disclosure Vulnerability	Critical	6.5	No	No	Info
CVE-2026-21218	.NET and Visual Studio Spoofing Vulnerability	Important	7.5	No	No	Spoofing
CVE-2026-21512	Azure DevOps Server Cross-Site Scripting Vulnerability	Important	6.5	No	No	XSS
CVE-2026-21529 †	Azure HDInsight Spoofing Vulnerability	Important	5.7	No	No	Spoofing
CVE-2026-21528	Azure IoT Explorer Information Disclosure Vulnerability	Important	6.5	No	No	Info
CVE-2026-21228	Azure Local Remote Code Execution Vulnerability	Important	8.1	No	No	RCE
CVE-2026-21531	Azure SDK for Python Remote Code Execution Vulnerability	Important	9.8	No	No	RCE
CVE-2026-21251	Cluster Client Failover (CCF) Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-20846	GDI+ Denial of Service Vulnerability	Important	7.5	No	No	DoS
CVE-2026-21523	GitHub Copilot and Visual Studio Code Remote Code Execution Vulnerability	Important	8	No	No	RCE
CVE-2026-21518	GitHub Copilot and Visual Studio Code Security Feature Bypass Vulnerability	Important	6.5	No	No	SFB
CVE-2026-21257	GitHub Copilot and Visual Studio Elevation of Privilege Vulnerability	Important	8	No	No	EoP
CVE-2026-21256	GitHub Copilot and Visual Studio Remote Code Execution Vulnerability	Important	8.8	No	No	RCE
CVE-2026-21516	GitHub Copilot for Jetbrains Remote Code Execution Vulnerability	Important	8.8	No	No	RCE
CVE-2026-21253	Mailslot File System Elevation of Privilege Vulnerability	Important	7	No	No	EoP
CVE-2026-21537 †	Microsoft Defender for Endpoint Linux Extension Remote Code Execution Vulnerability	Important	8.8	No	No	RCE
CVE-2026-21259	Microsoft Excel Elevation of Privilege Vulnerability	Important	7.3	No	No	EoP
CVE-2026-21258	Microsoft Excel Information Disclosure Vulnerability	Important	5.5	No	No	Info
CVE-2026-21261	Microsoft Excel Information Disclosure Vulnerability	Important	5.5	No	No	Info
CVE-2026-21527	Microsoft Exchange Server Spoofing Vulnerability	Important	6.5	No	No	Spoofing
CVE-2026-21260	Microsoft Outlook Spoofing Vulnerability	Important	7.5	No	No	Spoofing
CVE-2026-21229	Power BI Remote Code Execution Vulnerability	Important	8	No	No	RCE
CVE-2026-21236	Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-21238	Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-21241	Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability	Important	7	No	No	EoP
CVE-2026-21517	Windows App for Mac Installer Elevation of Privilege Vulnerability	Important	7	No	No	EoP
CVE-2026-21234	Windows Connected Devices Platform Service Elevation of Privilege Vulnerability	Important	7	No	No	EoP
CVE-2026-21235	Windows Graphics Component Elevation of Privilege Vulnerability	Important	7.3	No	No	EoP
CVE-2026-21246	Windows Graphics Component Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-21232	Windows HTTP.sys Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-21240	Windows HTTP.sys Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-21250	Windows HTTP.sys Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-21244	Windows Hyper-V Remote Code Execution Vulnerability	Important	7.3	No	No	RCE
CVE-2026-21247	Windows Hyper-V Remote Code Execution Vulnerability	Important	7.3	No	No	RCE
CVE-2026-21248	Windows Hyper-V Remote Code Execution Vulnerability	Important	7.3	No	No	RCE
CVE-2026-21255	Windows Hyper-V Security Feature Bypass Vulnerability	Important	8.8	No	No	SFB
CVE-2026-21231	Windows Kernel Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-21239	Windows Kernel Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-21245	Windows Kernel Elevation of Privilege Vulnerability	Important	7.8	No	No	EoP
CVE-2026-21222	Windows Kernel Information Disclosure Vulnerability	Important	5.5	No	No	Info
CVE-2026-21243	Windows Lightweight Directory Access Protocol (LDAP) Denial of Service Vulnerability	Important	7.5	No	No	DoS
CVE-2026-20841	Windows Notepad App Remote Code Execution Vulnerability	Important	8.8	No	No	RCE
CVE-2026-21249	Windows NTLM Spoofing Vulnerability	Important	3.3	No	No	Spoofing
CVE-2026-21508	Windows Storage Elevation of Privilege Vulnerability	Important	7	No	No	EoP
CVE-2026-21237	Windows Subsystem for Linux Elevation of Privilege Vulnerability	Important	7	No	No	EoP
CVE-2026-21242	Windows Subsystem for Linux Elevation of Privilege Vulnerability	Important	7	No	No	EoP
CVE-2026-1861 *	Chromium: CVE-2026-1861 Heap buffer overflow in libvpx	High	N/A	No	No	RCE
CVE-2026-1862 *	Chromium: CVE-2026-1862 Type Confusion in V8	High	N/A	No	No	RCE
CVE-2026-0391	Microsoft Edge (Chromium-based) for Android Spoofing Vulnerability	Moderate	6.5	No	No	Spoofing

* Indicates this CVE had been released by a third party and is now being included in Microsoft releases.

† Indicates further administrative actions are required to fully address the vulnerability.

Moving on to the Critical-rated bugs, the patch for Azure Front Door sounds frightening, but Microsoft has already fixed the bug and is just now documenting it. That’s also true for the bugs in Azure Arc and Azure Function. There are two Critical-rated bugs in the ACI Confidential Containers. The first allows a container escape while the second discloses secret tokens and keys. Either way, you’ll want to handle those quickly.

Taking a look at the other code execution vulnerabilities in this month’s release, we start with a frightening looking bug in Azure SDK for Python that has the highest CVSS this month of 9.8. A remote, unauthenticated attacker code gain code execution on an affected system via a maliciously crafted continuation token. It’s not clear why this isn’t rated Critical, but I would treat it as such. The three bugs in Hyper-V are actually local open-and-own bugs that require a user to open a malicious file on an affected system. That’s also true for the bug in Notepad. The bug in Power BI is confusing, because Microsoft says it requires authentication and could lead to an attacker running code as an authenticated user. There’s the poorly named “Azure Local Remote Code Execution Vulnerability”, but it requires a machine-in-the-middle (MitM) to exploit. The bug in Defender for Endpoint Linux is restricted to local subnets, but you’ll need to enable auto provisioning to get the patch. The final code execution bugs addressed this month are in GitHub Copilot. Two are command injections and the other is a Time-of-check time-of-use (toctou) race condition, but both could end up in code execution on affected systems.

Patches for Elevation of Privilege (EoP) bugs make up nearly 50% of this release, but most simply lead to local attackers executing their code at SYSTEM-level privileges or administrative privileges. There are only two of note. The first is a command injection bug in GitHub Copilot that leads to executing code at the level of the targeted application. The second is a bug in a kernel that leads to SYSTEM but could also be used for a sandbox escape.

There’s a unusually high number of spoofing bugs in this month’s release, and the ones for Outlook are the most troubling. First, the Preview Pane is an attack vector. Secondly, the bugs could be used to relay NTLM credentials via just an email, which could result in credential disclosure. And you’ll need multiple patches to fully address these bugs. At least they can be applied in any order. There’s a UI misrepresentation bug in Exchange Server that could allow an attacker to either view some sensitive information or “make changes to disclosed information”. At what point does data become disclosed? That odd phrasing makes me think they are using AI to right some of their descriptions. The phrasing also appears in the patch for NTLM. That bug is triggered by opening a specially crafted Office doc, and while they explicitly say it could be used to relay NTLM creds, it sure seems that way. The patch for .NET and Visual Studio fixes a bug that allows attackers to bypass header validation, resulting in the service accepting a message it should reject. Finally, the bug in Azure HDInsight is really just a cross-site scripting (XSS) bug. The caveat here is that you need to restart Ambari server in both of the head nodes to have this fix updated. There is also an XSS in Azure Devops Server, but at least it is labelled as such.

There are a couple of additional security feature bypass bugs to discuss. The first is in Hyper-V and bypasses the Virtualization-based Security feature. The other is in GitHub Copilot and Visual Studio Code. It’s another command injection, but this one can be used to bypass authentication. Neat.

Looking at the remaining info disclosure bugs getting patched this month, most simply result in info leaks consisting of unspecified memory contents or memory addresses. The exception is the bug in Azure IoT Explorer. This bug could be used to view the contents of the target user’s local file system.

We end this month’s release with two DoS bugs: one in LDAP and one in GDI+. Neither descriptions from Microsoft provide any usable information.

No new advisories are being released this month.

Looking Ahead

I plan on being back home for the March release but wherever I’m at, you can rest assured that March 10, I’ll be here to provide my assessment of the release. Until then, stay safe, happy patching, and may all your reboots be smooth and clean!

18 Enterprise-Architecture-Tools

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

Diese Enterprise Architecture Tools unterstützen Sie nicht nur bei der digitalen Transformation Ihres Unternehmens.

Foto: I Believe I Can Fly – shutterstock.com

Enterprise Architecture (EA) Tools unterstützen Unternehmen und Organisationen dabei, mit ihren IT-Strategien die Geschäftsziele optimal zu unterstützen. Sie sorgen ebenfalls dafür, dass Unternehmen ihre Roadmaps für die digitale Transformation geordnet vorantreiben können. EA Tools bieten dafür unter anderem Collaboration-, Reporting-, Testing- und Simulationsfunktionen. Mit deren Hilfe lassen sich Modelle implementieren, die Geschäfts- und IT-Prozesse gezielt verbessern.

Um die beste Lösung für Ihr Unternehmen zu finden, sollten Sie zuerst prüfen, ob sich das jeweilige Tool mit Ihrem Technologie-Stack integrieren lässt. Anschließend gilt es abzuwägen, ob die Informationen, Diagramme und Tabellen, die die Software zur Verfügung stellt, für das Unternehmen auch einen echten Nutzwert haben.

Empfehlenswerte Enterprise-Architecture-Tools

Nachfolgend finden Sie einen Überblick über die wichtigsten Enterprise-Architecture-Tools – in alphabetischer Reihenfolge. Sie stellen einen Mix aus Visualisierungs-, Collaboration- und Project-Management-Funktionen bereit und unterstützen eine Vielzahl von Enterprise Architecture Frameworks.

Ardoq

Nachdem zuerst über einfache Formulare Informationen von Usern, Entwicklern und sonstigen Stakeholdern im Unternehmen eingesammelt wurden, lässt sich mithilfe von Ardoq ein digitaler Zwilling der gesamten Organisation erstellen. Der Ansatz setzt also darauf, die Menschen, die in ihren Rollen mit den verschiedensten Systemen arbeiten, realistisch in ihrer Arbeitswelt abzubilden.

Jede Mitarbeiterin und jeder Mitarbeiter im Unternehmen kann später von den Netzwerkvisualisierungen und Datenfluss-Diagrammen profitieren, um seine eigene Rolle optimal zu unterstützen und den Arbeitsplatz immer wieder anzupassen und zu modernisieren. Das Tool lässt sich mit den wichtigsten Cloud-Plattformen integrieren. Es bietet eine API, die individuelle Anpassungen in allen wichtigen Programmiersprachen (Python, C#, Java, etc.) ermöglicht.

Wichtigste Use Cases:

“Architektonischen Stress” bei Lastspitzen simulieren, falls größere Veränderungen bevorstehen;
Verstehen, wie verändertes Nutzerverhalten neue Anforderungen generiert;
Application Portfolio Management, um besser strategisch zu planen.

Atoll Group SAMU

Das EA-Tool SAMU macht die Enterprise Architecture sichtbar, indem es tiefe Verknüpfungen zwischen On-Premises-Systemen, dem Cloud-Layer und Tools für das Business Process Management aufzeigt. Das Tool der Atoll Group bietet vielfältige Integrationsmöglichkeiten, zum Beispiel mit Monitoring-Tools (etwa Tivoli, ServiceNow), Configuration-Management-Datenbanken (zum Beispiel CA, BMC) oder Service-Organisations-Tools (BMC, HPE). Alle Informationen fließen in ein zentrales Datenmodell ein, das um den zusätzlichen Input der Stakeholder weiter angereichert wird.

Wichtigste Use Cases:

Enterprise-Architektur visualisieren;
strategische Planungsprozesse und Architektur-Reviews mit Informationen unterfüttern;
mithilfe einer visuellen Verständnisgrundlage die Kommunikation verbessern.

Avolution Abacus

Dieses Tool erfasst die Breite und den Umfang der Unternehmensarchitektur mit Hilfe eines auf Diagrammen basierenden Dashboards. Die Integration mit gängigen Tools wie SharePoint, Excel, Visio, Google Sheets, Technopedia oder ServiceNow vereinfacht die Nutzung. Abacus wurde inzwischen auch um einen Machine-Learning-Layer ergänzt, der es Anwendern ermöglicht, ein Modell zu trainieren, das ihnen beispielsweise hilft zu erkennen, wer im Unternehmen für welches System verantwortlich ist.

Wichtigste Use Cases:

die IT für das gesamte Unternehmen “öffnen”, um ein allgemeines Verständnis der Datenflüsse zu erzeugen;
umfassendes Enterprise Modeling, um eine Roadmap für künftige Entwicklungen zu erstellen;
Business-Metriken tracken, die mit der Unternehmens-Performance zusammenhängen.

BOC Group ADOIT

ADOIT soll Teams dabei unterstützen, Ressourcen zu verwalten, Bedarfe vorherzusagen und Assets zu tracken. Dazu mappt das Tool jedes System oder Softwarepaket mit einem Objekt. Die Datenflüsse zwischen den Systemen werden in Beziehungen umgewandelt, die von diesen Objekten mithilfe eines anpassbaren Metamodells erfasst werden. Geschäftsprozesse können auf ähnliche Weise über ein gut integriertes Begleitprodukt namens ADONIS modelliert werden. ADOIT ist Web-basiert und lässt sich auch mit Tools wie Atlassian Confluence integrieren, um die Datenerfassung und -entwicklung zu beschleunigen.

Wichtigste Use Cases:

ein unternehmensweites Modell erstellen, das bei sämtlichen Teammitgliedern ein Verständnis über den Stack schafft – und wie man diesen verbessern kann;
vollständiger Zugriff auf EA-Daten über eine Mobile-Anwendung;
bei Fusionen und Übernahmen den Tech-Bereich durch genaues Asset-Mapping orchestrieren.

Bizzdesign Hopex

Nach der Übernahme von Mega International zählt die Hopex-Plattform zum Portfolio von Bizzdesign. Sie soll dabei unterstützen, Unternehmensanwendungen zu modellieren und dabei ein Verständnis der von ihnen unterstützten Geschäfts-Workflows schaffen. Dabei liegt ein Schwerpunkt auf den Bereichen Data Governance und Risikomanagement. Hopex basiert auf Microsoft Azure und stützt sich auf eine Reihe offener Standards wie GraphQL und REST Queries, um Informationen aus Komponentensystemen zu sammeln. Das Reporting ist mit den Office-Tools von Microsoft sowie mit grafischen Lösungen wie Tableau und Qlik integriert.

Wichtigste Use Cases:

datengestützte Erkenntnisse herbeiführen, um Cloud- und Anwendungsbereitstellung zu steuern;
akkurate Nutzungsmodelle erstellen, um Architekturanforderungen zu verstehen;
eine Bedarfsschätzung mit Umfragen und anderen Tools vornehmen, um für die Zukunft zu planen.

Bizzdesign Horizzon

Das Tool dient dazu, Business Workflows und den zugrundeliegenden Tech-Stack zu modellieren. Dazu bietet Horizzon ein Graph-basiertes Modell, das Daten von sämtlichen Stakeholdern einsammelt und diese an eine Analytics-Engine weitergibt. Im Ergebnis entstehen Diagramme, die den aktuellen Systemzustand widerspiegeln. Wichtige Schwerpunkte dieses Tools sind Change Management und Zukunftsplanung: Horizzon ist nicht zuletzt dafür konzipiert worden, die Risiken eines Redesigns zu minimieren. Das Toolset unterstützt die wichtigsten Frameworks ArchiMate, TOGAF und BPMN. Neben Mega hat Bizzdesign im Januar 2025 auch den EA-Geschäftsbereich der Software AG – Alfabet – übernommen.

Wichtigste Use Cases:

Vorhersage zukünftiger Anforderungen durch Predictive Modeling;
Orchestrieren von Workflows auf der Basis der technischen und der Business-Architektur;
Antizipieren von Risiken sowie Security- und Governance-Problemen durch die Modellierung von Datensicherheitsanforderungen.

Capstera

Das Tool von Capstera fokussiert darauf, die Business Architecture selbst abzubilden. Value und Process Maps helfen dabei, die Rollen der verschiedenen Unternehmensbereiche zu definieren und nachzuverfolgen. Dabei können im laufenden Prozess Verknüpfungen mit den zugrundeliegenden Softwarprodukten und Tools hinzugefügt werden.

Wichtigste Use Cases:

Reports erstellen, die sich erst einmal mit der Business-Architektur selbst beschäftigen;
Beziehungen zwischen Menschen, Abteilungen und Rollen analysieren;
die langfristige strategische Planung vorantreiben.

Clausmark Bee360

Teammitglieder, die Clausmarks Flaggschiffprodukt Bee360 (früher Bee4IT) verwenden, wollen eine einfache “Single Source of Truth” über die Workflows im Unternehmen. Ziel ist es, verschiedenen betrieblichen Rollen intelligentere Entscheidungen zu ermöglichen. Das Modul Bee360 FM (Finanzmanagement) bietet etwa die Möglichkeit, Kosten nachzuvollziehen und zuzuordnen. Die Anwender können verschiedene solcher Module miteinander verknüpfen, um EAM, Finanzmanagement, Portfolio Management und Agile Planning nahtlos zu integrieren – bei maximaler Transparenz.

Wichtigste Use Cases:

C-Suite-Ebene befähigen, Projekte zu managen und Assets zuzuweisen;
präzise digitale Zwillinge entwickeln, um ein Verständnis über Datenflüsse zu schaffen und künftige Erweiterungen zu planen;
integrierte Wissensdatenbank aufbauen, um alle digitalen Workflows zu tracken.

EAS

Das Essential-Paket von EAS (Enterprise Architecture Solutions) nahm als Open-Source-Projekt seinen Anfang und hat sich inzwischen zu einer kommerziell verfügbaren Cloud-Lösung weiterentwickelt. Das Tool erstellt ein Metamodell, das die Interaktionen zwischen Systemen und Geschäftsprozessen beschreibt. Ebenfalls enthalten sind Pakete, um gängige Business Workflows wie Datenmanagement oder DSGVO-Compliance zu tracken.

Wichtigste Use Cases:

den technischen Reifegrad der eigenen Architektur evaluieren;
Sicherheit und Governance durch besseres Asset Tracking optimieren;
wachsende Systemkomplexität kontrollieren und managen.

OrbusInfinity

Orbus Software hat Anfang 2025 die Akquisition seines Konkurrenten Capsifi abgeschlossen. Der Anbieter stellt mit OrbusInfinity eine Enterprise-Transformation-Plattform auf KI-Basis zur Verfügung, die schnellere, bessere Entscheidungen, Kosteinesparungen und Risikominimierung verspricht. Architecture-Teams sollen mit Hifle von OrbusInfinity mit einer Vielzahl von Stakeholdern interagieren können, um eine “digitale Blaupause” ihres Unternehmens zu generieren, die eine einheitliche Sicht auf das aktuelle und künftige Geschäft realisieren soll. Diverse Drittanbieter-Tools lassen sich außerdem mit der Plattform integrieren, darunter etwa von Microsoft, Flexera, ManageEngine oder ServiceNow.

Wichtigste Use Cases:

Stakeholder-Management;
Enterprise-Landschaften visualisieren;
Entscheidungsfindung und Datenanalyse automatisieren.

Planview Enterprise One

Planview bietet eine ganze Reihe von Produkten, mit denen Unternehmen Teamwork, Prozesse und die Enterprise Architecture nachvollziehen können. Die Enterprise Tools sind in drei Kategorien unterteilt: strategisches Portfolio-Management, Produktportfolio-Management und Projektportfolio-Management. Im Zusammenspiel entstehen hardware- und Software-übergreifende Layer, die rollenbasierte Perspektiven für Führungskräfte und Teammitglieder eröffnen. Das Toolset integriert mit gängigen Ticket-Tracking-Systemen wie Jira, um Workflow-Analysen und Reports zu erstellen. Inzwischen hat Planview nach einer Übernahme neue Tools in sein Portfolio integriert, die früher unter den Namen Daptiv, Barometer und Projectplace bekannt waren.

Wichtigste Use Cases:

eine langfristige, strategische Vision für die Architekturentwicklung aufbauen;
Entwicklungsarbeit auf Projektebene tracken und in eine beliebige Strategie integrieren;
mit Fokus auf die Customer Experience und die Produktstruktur den Change vorantreiben.

QualiWare Enterprise Architecture

Das Enterprise Architecture Tool von QualiWare ist Teil einer größeren Sammlung von Modellierungswerkzeugen, die darauf abzielt, sämtliche Geschäftsprozesse zu erfassen. Beispielsweise ist es möglich, einen digitalen Zwillinge zu bauen, mit dem sich Customer Journeys nachvollziehen lassen. Qualiware hat diverse KI-Algorithmen integriert, um Dokumentation und Process Discovery zu optimieren.

Wichtigste Use Cases:

ein kollaboratives Ökosystem für Business Manager aufbauen, das ein Verständnis von der Enterprise Architecture vermittelt;
architektonische Designelemente erfassen, um ein Wissens-Ökosystem rund um den Stack aufzubauen;
eine breite Beteiligung in Sachen Dokumentationserstellung und -überprüfung fördern.

Quest Erwin Evolve

Das Erwin Evolve Tool von Quest hat sich von einem Datenmodellierungs-Tool zu einem System für Enterprise-Architecture- und Geschäftsprozess-Modellierung weiterentwickelt. Um die Komplexität moderner, ineinandergreifender Softwaresysteme und der von ihnen gemanagten Geschäftsprozesse zu durchdringen, können Anwender auf benutzerdefinierte Datenstrukturen zurückgreifen. Das Web-Tool erstellt Modelle, rollenbasierte Diagramme und andere Visualisierungen, die in allgemein zugängliche Dashboards einfließen. Zum Paket gehört ein KI-basiertes Modellierungs-Tool, das Whiteboard-Skizzen integrieren kann.

Wichtigste Use Cases:

einen digitalen Zwilling für die strategische Modellierung der Enterprise Data Architecture erstellen;
Customer Journeys verstehen;
Services und Systeme mit Application Portfolio Management tracken.

SAP LeanIX Enterprise Architecture Suite

Die Tool-Sammlung von LeanIX umfasst unter anderem Enterprise Architecture Management und andere Bereiche, die für Aufgaben wie SaaS– und Value-Stream-Management wichtig sind – etwa um Cloud-Deployments und darauf laufende Services zu tracken. Die Daten die dabei über die IT-Infrastruktur gesammelt werden, fließen in ein grafisches Dashboard ein. Das Tool ist eng mit wichtigen Cloud-Workflow-Tools wie Confluence, Jira, Signavio und Lucidchart integriert. Das ist für Teams von Vorteil, die diese Tools bereits nutzen, um ihre Entwicklungsstrategien zu planen und umzusetzen. Seit November 2023 ist LeanIX Teil von SAP.

Wichtigste Use Cases:

Anwendungsmodernisierung und Cloud-Migration managen;
Obsoleszenz von Software-Services evaluieren;
Kosten kontrollieren und managen.

ServiceNow

Die Tool-Sammlung von ServiceNow lässt sich auf verschiedene Architekturtypen herunterbrechen, darunter Assets, DevOps, Security und Service. Die Tools katalogisieren die unterschiedlichen Hardware- und Softwareplattformen, um Workflows und Datenflüsse im Unternehmen abzubilden und zu verstehen. Ausführliche Reportings und detaillierte Dashboards ermöglichen Analysen, auf deren Grundlage Risiken minimiert und die Ausfallsicherheit der Systeme erhöht werden können.

Wichtigste Use Cases:

Tracken von Assets, Services und Systemen, die das Unternehmen ausmachen;
Governance-Themen, Risikobegrenzung, IT-Management und Security Operations werden in einer Plattform zusammengeführt;
durch die Integration von CRM-Tools lassen sich auch kundenorientierte Services managen.

Sparx Systems

Um Teams und Projekte verschiedener Größe und Komplexität zu unterstützen, hat Sparx vier Versionen seines EA-Tools entwickelt. Allen gemeinsam ist eine UML-basierte Modellierung, mit der sich die Komponenten komplexer Systeme tracken lassen. Eine Simulations-Engine ermöglicht “War Gaming” und vermittelt ein Verständnis darüber, wie sich Fehler ausbreiten und kaskadieren können. Sparx stellt zudem eine Vielzahl von vorgefertigten Design Patterns bereit, um Teams bei der Modellierung zu unterstützen.

Wichtigste Use Cases:

Nachfrage- und Lastveränderungen zur Prognose künftiger Anforderungen simulieren;
(potenzielle) Probleme durch eine Verbindungs-Matrix im Auge behalten;
Dokumentation erstellen.

Unicom System Architect

System Architect ist eines der Angebote aus Unicoms Team Blue. Es handelt sich um ein Tool, das ein Metamodell verwendet, um automatisiert so viele Daten wie möglich über die laufenden Systeme zu sammeln – manchmal auch durch ein Reverse Engineering von Datenflüssen. Dieses systemweite Datenmodell kann über benutzerdefinierte Dashboards Teammitgliedern aller Rollen zugänglich gemacht werden. Ein weiteres erwähnenswertes Feature: Die Ressourcenzuweisung lässt sich mit Hilfe von Simulationen optimieren.

Wichtigste Use Cases:

Was-wäre-wenn-Fragen zum Architekturmodell stellen;
ein Metamodell von Daten und Systemen aufbauen;
Migrations- und Transformationspläne erstellen.

ValueBlue BlueDolphin

Dieses EA-Tool sammelt Daten auf dreierlei Art:

Es importiert Basisdaten auf der Grundlage standardgesteuerter Automatisierung (ITSM, SAM).
Es arbeitet mit den Dateiformaten von Architekten und Systemdesignern – etwa ArchiMate oder BPMN.
Es gibt Fragebögen an andere Stakeholder heraus, die auf anpassbaren Vorlagen basieren.

Die aufbereiteten Informationen werden in einer visuellen Umgebung bereitgestellt, die Auskunft über die historische Entwicklung von Systemen gibt.

Wichtigste Use Cases:

systemweite Daten von internen und externen Stakeholdern automatisiert und formularbasiert erfassen;
zukunftsorientierte Reportings erzeugen, um den Change zu überwachen und voranzutreiben;
Kooperation und Zusammenarbeit durch offenes Data Reporting fördern.

(fm)

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

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.

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.
Want to join?

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

Get our breaking news email, free app or daily news podcast

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.

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!

Decades-Old Bash Tricks Expose AI Coding Agents To Supply Chain Attacks

Sat, 25 Jul 2026 10:12:50 +0200

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.

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.

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.

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

submitted by /u/oleg_antonyan
[link] [comments]

Accelerating AWS Network Firewall troubleshooting with AWS DevOps Agent

Fri, 24 Jul 2026 22:11:14 +0200

When an administrator introduces a rule change in AWS Network Firewall and network connectivity is disrupted, pinpointing the cause requires inspecting multiple points in the traffic path. The firewall gives you stateless and stateful rule engines, domain rules, and routing…

The post Accelerating AWS Network Firewall troubleshooting with AWS DevOps Agent appeared first on IT Security News.

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.

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.

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?

LHB Linux Digest #26.09: Docker Compose Override, Claude Skills for DevOps, Solidtime and More

Fri, 24 Jul 2026 13:44:12 +0200

Override the compose

8 Claude Skills I Use Everyday as a DevOps Engineer

Fri, 24 Jul 2026 13:30:07 +0200

Sharing the skills I use for troubleshooting and other purposes as a devops engineer. They are primarily for Claude but should work with other AI tools, too.

GitOps vs Traditional DevOps

Fri, 24 Jul 2026 13:05:05 +0200

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.

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.
Want to join?

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.

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.

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.

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.

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.

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.

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.
Want to join?

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

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.

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.

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 […]

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.

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,

Hackers Turn GitHub Actions Into a Global Botnet for Attacking Web Hosting Servers

Thu, 23 Jul 2026 13:27:00 +0200

Hackers are abusing compromised GitHub repositories and GitHub Actions workflows to build a de facto global botnet that scans and exploits web hosting servers, with a primary focus on cPanel and WHM deployments. The campaign first surfaced when malicious development versions were discovered across ten Packagist PHP packages tied to a legitimate PHP and DevOps […]

The post Hackers Turn GitHub Actions Into a Global Botnet for Attacking Web Hosting Servers appeared first on GBHackers Security | #1 Globally Trusted Cyber Security News Platform.

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.

[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
Protect your personal data, keep yourself safe with Surfshark VPN: See current promotion

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.

Click to view the poll.

Check if your mirror has already synced:

Mirror-Check Service

3 posts - 2 participants

Read full topic

Azure DevOps MCP: Unsichtbare PR-Kommentare kapern KI-Review-Agenten und leaken Projektinfos

Thu, 23 Jul 2026 09:24:46 +0200

LONDON (IT BOLTWISE) – Eine einzelne HTML-Notiz in der Beschreibung eines Pull Requests kann eine KI-gestützte Review-Automation missbrauchen und damit Zugriff über Projektgrenzen hinweg verschieben. Der Angriff läuft nicht als „klassisches“ Prompt-Troubleshooting, sondern nutzt eine Lücke in der Werkzeugverkettung des offiziellen MCP-Servers. Besonders kritisch wird es, wenn ein Reviewer die KI mit höherer Berechtigung ausführt […]

... den vollständigen Artikel »Azure DevOps MCP: Unsichtbare PR-Kommentare kapern KI-Review-Agenten und leaken Projektinfos« lesen

Dieser Beitrag Azure DevOps MCP: Unsichtbare PR-Kommentare kapern KI-Review-Agenten und leaken Projektinfos erschien als erstes auf IT BOLTWISE x Artificial Intelligence.

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.

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.

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

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.

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.

Legal obligations vs social contracts | Perspective from a KDE Developer

Wed, 22 Jul 2026 22:15:26 +0200

submitted by /u/novafunc
[link] [comments]

Game developer Harebrained Schemes kicks off its new indie era with survival-horror RPG ‘GRAFT’

Wed, 22 Jul 2026 21:21:21 +0200

The latest game from Seattle-based developer Harebrained Schemes, and its first since its return to independence in 2023, is a dark sci-fi/horror adventure where the player must constantly change and enhance their body in order to survive. Read More

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

Team IT Security - 📰 Alle Kategorien

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

Transparency requirements

A consistent code of practice

Criteria for compliance

Creating a transparency pipeline

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

A ‘significant opportunity’ in technology

Monday.com’s key market advantages

Actions for the near-term

AMD raises the AI stakes with Helios, Venice and robotics

AMD’s MI455X targets the AI memory wall

AMD’s Helios rack takes aim at Vera Rubin

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

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

The May 2026 Security Update Review

Announcing winapp, the Windows App Development CLI

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

Enhanced developer tools on the Microsoft Store

Accelerating Arm momentum in Japan with App Assure

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

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

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

OpenPBR Support - Alpha

The February 2026 Security Update Review

18 Enterprise-Architecture-Tools

Empfehlenswerte Enterprise-Architecture-Tools

9 Kommandozeilen-Tools, die jeder Dev braucht

tldr

ngrok

screen

sdkman & nvm

fzf

exa

bat

nethack

Model Context Protocol is going stateless to make scaling simpler

MCP’s new features

Rebuilding the trust boundary

Updated MCP SDKs

Getting a grip on shadow tokens and AI blowouts

When shadow tokens result in real costs

Shifting the culture from usage to yield

Stopping token waste before an AI blowout

5 endpoint blind spots your EDR/XDR was never built to see

Developer withdraws plans for Perth datacentre after fierce community opposition

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

Build 2026: Furthering Windows as the trusted platform for development

Windows platform security for AI agents

Making Windows the trustworthy OS for agents

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

Why join the catalyst program?

Ready to start building?

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

Android development unlocked for Antigravity

Unlocking Android Studio capabilities for any agent

More ways to get started

Support for Journeys

Expanding Android skills

Get started today

Increasing app discovery and engagement on Google TV

Drive discovery and engagement with Gemini

Declare support for pointing modality

1. Adapt your TV app UI Library

2. Test pointing interactions with a mouse today

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

Onboard the Engage SDK

Build native Android apps in Google AI Studio

The power of native Android

A seamless, end-to-end workflow

Start building today

What’s Next?

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

Build High Quality Android Apps Using Agents

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

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

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