The Beta channel is being updated to OS version 16765.38.0 (Browser version 152.0.7977.74 ) for most ChromeOS devices. If you find new issues, please let us know one of the following ways: File a bug Visit our ChromeOS communities General: Chromebook Help Community Beta Specific: ChromeOS Beta Help Community Report an issue or send feedback on Chrome Interested in switching channels? Find out how. Luis Menezes Google ChromeOS
AI model-training startup AfterQuery has reportedly raised a round that valued it at $3.2 billion, just five months after announcing its $30 million Series A at a $300 million valuation in April.
Anthropic says its newest AI models, Fable 5.1 and Mythos 5.1, address criticisms from customers about price, data retention, and overzealous safeguards. The company claims Claude Fable 5.1 offers stronger performance than Fable 5, but costs around 25 percent less typically and up to 45 percent less for complex agentic tasks, thanks to reduced pricing […]
Palo Alto Networks' stock has nearly doubled this year as AI boosts demand for security detection and response tools
Hugging Face Blog published: BenchMIRT: What are LLM benchmarks actually measuring?
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OpenAI previewed the precautions it is taking as it prepares to release Astra, its newest, cyber-critical LLM.
Anthropic launched Claude Fable 5.1 on Tuesday with a statistical signature embedded in its generated text, but developers shouldn’t expect The post Claude Fable 5.1 watermark: It has a blind spot developers can’t ignore appeared first on The New Stack .
Here are Google’s latest AI updates from August 2026
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Anthropic is launching an API that lets regulators, media outlets, and researchers check whether text carries Claude's digital watermark. The EU AI Act now requires invisible watermarks in AI-generated text. Critics warn the technology could hurt text quality and create transparency problems where contracts ban AI use. The article Anthropic opens Claude AI text detection to regulators, media, fact-checkers, and others appeared first on The Decoder .
Anthropic launches Claude Fable 5.1 and Mythos 5.1, its most capable AI models yet. Fable 5.1 doubles its predecessor's score on Terminal-Bench-Science and improves agentic coding by over 30 percent. Costs drop by up to 45 percent for long, autonomous runs with many tool calls. The article Anthropic's Claude Fable 5.1 promises better coding and research at up to 45 percent less appeared first on The Decoder .
Article URL: https://www.newsonaut.com/articles/hang-on-to-your-firefox Comments URL: https://news.ycombinator.com/item?id=49527748 Points: 829 # Comments: 422
The cybersecurity giant announced the acquisition alongside quarterly results showing a 34% increase in revenue and strong growth in next-generation security ARR. The post Palo Alto Networks Acquires AI Agent Platform Console appeared first on SecurityWeek .
You can now set an optional expiration date on an individual user budget, and GitHub removes the budget on that date. This capability is generally available. The user then falls… The post Set an expiration date for individual user budgets appeared first on The GitHub Blog .
Article URL: https://9to5mac.com/2026/08/31/apple-openai-forensic-macbook-evidence/ Comments URL: https://news.ycombinator.com/item?id=49527573 Points: 205 # Comments: 147
Article URL: https://simonwillison.net/2026/Sep/1/codex-libreoffice/ Comments URL: https://news.ycombinator.com/item?id=49527396 Points: 333 # Comments: 152
Like many readers, I’m wary of AI generated prose. Simon Wilison has written an LLM cliché highlighter - paste in some text, or a URL, and it will flag various patterns common to LLMs. It references a wikipedia page of signs of AI writing . That page points out that: Humans are notoriously bad at distinguishing human and LLM-generated text. While research on humans’ abilities to detect AI-generated text is still limited, a 2025 study has shown that human ability to distinguish LLM text from human is no better than random chance. Another 2025 study on German theses has shown that humans managed a “recognition rate of 57% for AI texts and 64% for human-generated texts”.[ Not just do I find myself repelled by prose with an LLM-voice, I also wonder how accurate my reaction is. I’m old enough to see all sorts of new tic-phrases appear, and in the past would just chalk it up to youngsters or airport business books. (Not to mention Americanisms, which I’ll get used to momentarily.) ❄ ❄ ❄ ❄ ❄ NVIDIA’s technical blog reports on an Architecture for Long-Horizon Autonomous Agents . Their research group used a combination of Claude Opus 5 and a harness called AVO, and used it first to do GPU kernel optimization and then a broader reasoning benchmark (ARC-AGI-3). Both of these were long-term tasks, for the kernel optimization the agent ran for seven days. AVO is designed to preserve progress beyond a single model context. Two mechanisms are particularly important: persistent memory and supervision. Persistent memory carries forward prior implementations, evaluation results, compiler and profiler outputs, and accumulated reasoning, allowing the agent to resume from the current state rather than repeatedly reconstructing the search. The supervisor monitors the broader trajectory for stagnation or repeated unproductive cycles and can redirect the main agent toward alternative strategies when needed. During the seven-day attention-kernel run, the main agent remained responsible for deciding what to inspect, change, test, and evaluate, while the supervisor helped maintain forward progress when the search plateaued. The team was encouraged that AVO did well at two different kinds of long-horizon tasks, indicating that it’s a general-purpose tool. ❄ ❄ ❄ ❄ ❄ Mickey Petersen : MCP is SOAP for Zoomers. ❄ ❄ ❄ ❄ ❄ Paul Stack writes that AI Broke the Assumptions Behind CI . Here’s his description of CI with agents. An agent writes a change, opens a PR, and CI picks it up instantly. The compile fails, the agent pushes a fix, CI picks it up instantly again. A test fails, another fix, another instant run. Each iteration is fast, but the agent is still discovering that its change doesn’t work only after it crosses the PR boundary. The feedback loop is in the wrong place regardless of how fast CI runs. He points out that all of this breaks the pipeline, because “CI” keeps failing, and advocates doing verification before the agent pushes. This is where I get to be the grumpy old guy, and point out that was always how Continuous Integration works . When I’m done with a change, first I pull (to get everyone else’s change since I started), I build and test locally, and if all is well I push and let the CI server do its thing. The only reason the CI server should fail is if there’s some funky mismatch between my machine and the CI server. Tests that take a while to run aren’t part of this loop, instead they are run further down the deployment pipeline , downstream of CI. Any failures there imply missing tests in CI. (I’m being a bit unfair dumping on this article here. After all I could have filled a full working day correcting misleading descriptions of Continuous Integration for most of the last twenty years. Maybe I’m just after an excuse to point readers to the extensive range of articles hosted here about what’s needed to get code from laptop to production.) Stack is right that we should question how the deployment pipelines should work with agents in play. He’s also right that CI with humans relies on them being disciplined to run commit tests locally before pushing to the CI server - and that we can (and should) automate that when using agents. I also don’t know more about his setup than what he’s written in his post, so there’s likely complications he faces that I don’t understand. But when thinking about designing pipelines it’s important to understand the principles that underlie Continuous Delivery , understand how the practices really work, and understand why they are in place. Above all, Continuous Integration is a practice, not just the CI server. Yes, CI does conflate two jobs: executing verification and coordinating merges. But that’s the point: verification is a necessary part of merging if we want to retain a healthy mainline . ❄ ❄ ❄ ❄ ❄ Recently Noah Smith posted an article about how he was worried about an AI-generated super-virus savaging humanity . It’s a worry I’ve heard a few times, seen as a greater concern than AI turning us into labradors or paper-clips. Claus Wilke, who works in the field, isn’t so concerned . Computational design of biological systems is unfathomably difficult. Experts who have dedicated their life to this topic routinely hit their head against the wall when nothing they try seems to work. PhD students in 2026 using state-of-the-art AI software are spending months or years trying to design simple peptide binders that inhibit some enzyme or pull down some protein, and the majority of their designs fail, or don’t express, or are toxic. But in Smith’s fictitious world a disgruntled teenager with no special training in biology can just solve a problem thousands of times more complicated than designing a peptide binder. The distance between where we are today and where we would have to be for Smith’s story to have any realism is enormous. ❄ ❄ ❄ ❄ ❄ It seems that a couple of remarkably talented academics are experts in a staggeringly wide range of fields. Or maybe they are just ghosts. These names do not exist. Elena Vasquez and Marcus Chen have appeared as volcano experts, astronauts, thriller protagonists, podcast hosts, and academic co-authors across hundreds of independently produced AI-generated documents, never having lived. We show that large language models do not merely default to high-probability individual names when generating fictional experts: they produce correlated character ensembles: pairs and trios whose co-occurrence rates far exceed chance and are consistent across independent generations.
Runway introduced Solaris on Monday, the first model of a new class of AI systems it calls Interface World Models, The post Runway wants to generate software as you use it. Solaris is its first step. appeared first on The New Stack .
"Black hole stars," making cookies from plastic, tiny sound-powered drones, and more.
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Sevii has expanded its ADR platform with AI agents designed to investigate, contain, and remediate AI-driven attacks within minutes. The post Sevii Targets AI-Speed Attacks With Preemptive Autonomous Defense appeared first on SecurityWeek .
I am excited to announce that etcd RangeStream is graduating to beta in Kubernetes v1.37. Paired with etcd v3.7, it reduces the memory the API server and etcd need to read a large collection, and makes peak usage more predictable. The cost of large reads The API server serves most list and watch requests from its in-memory watch cache. Populating that cache requires reading a resource's full state from etcd, at startup and on every re-initialization. For a resource with many objects, or large ones, such as Pods, that read is expensive. The API server already paginated these reads, asking etcd for a fixed number of keys at a time rather than the whole collection at once. But a page bounded by key count has no awareness of object size, so a page of large objects can still be very large. That makes memory usage hard to predict, and a bad combination of object size and concurrent reads can be enough to trigger an OOM. etcd's unary Range assembles each page in full before sending it, and the API server holds it while decoding, so the same payload sits in memory on both sides at once. Most of that cost lands on etcd, which is also where streaming helps most. Streaming reads with RangeStream etcd v3.7 adds a streaming version of that read, the RangeStream RPC. It takes the same RangeRequest as Range and returns the same result set, but instead of building the whole response up front, etcd splits it into chunks and streams them. Chunk size is tuned adaptively to the values being returned, so a collection of large objects is bounded by bytes rather than by a key count, and memory is freed as the stream progresses instead of being held until a whole page is assembled. When the feature is enabled, the API server uses RangeStream wherever it reads a whole collection out of etcd. This includes watch cache initialization, and the fallback paths where a list request cannot be served from the cache and reads etcd directly. In either case the API server decodes each chunk as it arrives and releases it before pulling the next one, so neither side ever holds the whole collection. Requirements Kubernetes v1.37 or later etcd v3.7 or later RangeStream is used when the EtcdRangeStream feature gate is enabled on the kube-apiserver, which is beta and on by default in v1.37, and etcd is v3.7 or later. The API server resolves etcd's support at startup and also falls back at runtime if a call returns Unimplemented , so an API server paired with an older etcd keeps using the paginated Range path on its own. To turn it off, disable the gate: --feature-gates=EtcdRangeStream=false Confirming RangeStream is in use The API server records streamed reads under their own operation label on its etcd metrics. A non-zero count here means RangeStream is in use: etcd_request_duration_seconds_count{operation="listStream"} If it stays at zero, the API server is still using the paginated Range path, most likely because etcd is older than v3.7. Learn more KEP-5966: etcd RangeStream RangeStream in the etcd API guide Announcing etcd v3.7 SIG etcd If you have questions or feedback, join the #sig-etcd channel on Kubernetes Slack .