The Wire · Anthropic
Claude, the research, and Anthropic's product moves — the lab whose models power much of what builders ship, this site included.
OpenAI cut GPT-5.6 Luna API prices by 80% to $0.20 per million input tokens and $1.20 per million output, while Terra fell 20% to $2 and $12. Fast mode gives Sol up to 2.5 times Standard speed at twice the price. OpenAI says Sol-assisted kernel work lowered serving cost by 20% and improved token-generation efficiency by more than 15%, while Luna delivers year-old frontier performance at roughly six cents per task-dollar and nearly nine times the speed. The edition connects cheaper models to Amazon's reported $1.8m, 860%-over-budget coding task, Gemini Robotics 2 whole-body control, Nscale's Anyscale acquisition and Okta's roughly $200m Permiso deal.
815 points, 399 comments on HN
Read full story →Claude Code, Codex, Pi, OpenCode, Gemini, and Prime Agent versions of Poteto's pstack. Rigorous agent workflows with Cursor primitives translated for other harnesses.
Read full story →For a while, those hoping to avoid the difficulty and expense of dealing with Adobe's Creative Suite have been able to use several free and/or open-source alternatives that replicate some or all of those capabilities. Now, one developer is using AI-powered reverse engineering to mimic the look, feel, and functionality of Adobe's well-known software with what he hopes will soon be perfect fidelity. Artcraft got its start last year as a "controllable AI for artists" that allows for precise post-generation editing of AI-crafted images and videos. Over the weekend, though, the brand debuted its pivot to a suite of seven open source apps recreating the user interface and tools found in Adobe's Photoshop, Illustrator, Premiere, Lightroom, After Effects, InDesign, and Acrobat Pro. In announcing those new apps on Reddit earlier this week, software developer Brandon Thomas said he used Anthropic's Claude Opus 5.5 to create "clean-room replacements" for Adobe's closed source software in Rust (with WebAssembly versions available for use in a browser). And while commenters on Hacker News and elsewhere have taken pains to point out the new software's many current shortcomings, Thomas make the grandiose promise that the current "super early alpha" state will soon lead to something just as functional as anything Adobe has ever put out. Read full article Comments
Read full story →If you thought Windows Recall was creepy, buckle up. Microsoft is about to shoehorn even more AI into its desktop operating system, giving Copilot access to every file on your PC. But don't worry, Redmond insists it's totally secure and safe since the agents powering the functionality will run entirely on your PC — some of the time. Microsoft describes the new functionality, announced alongside a new Nvidia N1X powered Surface Laptop, as "hybrid intelligence." Built atop Microsoft's HydraFusion model router, Windows apps and Copilot features will soon be able to use local models when it makes sense and cloud-based models when it doesn't. For example, Windows might use a local model for simple or privacy-sensitive tasks, or decide complex or performance-sensitive requests need OpenAI's GPT-6 or Anthropic's Claude. Local agents will run in Microsoft's Execution Containers (MXCs), agent sandboxes with fine-grained permissions applied at runtime. According to Microsoft EVP of Windows and Devices Pavan Davuluri, MXCs mean Windows is now "the most secure platform for agents." (That claim will surely age well because models never escape their sandboxes and routers never misbehave.) To support this hybrid intelligence functionality, Microsoft is extending support for Windows ML, its inference framework built on the ONNX runtime, to Llama.cpp – one of the highest-performing inference engines for local AI inference. By adding support for Windows ML, Llama.cpp should be able to take advantage of a broader range of accelerators. Microsoft is rolling out its hybrid intelligence functionality to GitHub Copilot — its AI code assistant — next week and its Copilot app over the next few months. The new Copilot features look and sound suspiciously like OpenClaw in a business suit. Copilot's agentic functionality will not have access to your entire file system, opening the door to advanced search functionality and productivity tasks through integrations with Microsoft Office. In a demo
<p>As previously <a href="https://simonwillison.net/2026/Sep/28/claude-sonnet-5-5/">promised</a>, here's Anthropic's new fast, low cost model: <a href="https://www.anthropic.com/claude-haiku-5-5">Introducing Claude Haiku 5.5</a>.</p> <p>The previous Haiku, 4.5, was very much showing its age. It came out <a href="https://simonwillison.net/2025/Oct/15/claude-haiku-45/">almost a year ago</a>, and was priced at $1/million input and $5/million output - relatively expensive even back then, and a full 10x the price of OpenAI's <a href="https://simonwillison.net/2026/Sep/22/opus-and-sol-and-luna/#gpt-6-sol-and-luna-are-half-the-price-of-their-gpt-5-6-equivalents">GPT-6 Luna</a>, released last month.</p> <p>The new Haiku exactly matches the price of GPT-6 Luna - $0.10/$0.50 - up to 100,000 tokens. Beyond 100,000 tokens the price increases 5x to $0.50/$2.50. Luna itself has a price increase at 272,000 tokens but only to $0.20/$0.75.</p> <p>Haiku 5.5 also uses a new, less generous tokenizer. My <a href="https://tools.simonwillison.net/claude-token-counter">Claude Token Counter</a> tool shows that the same long prompt uses around 1.25x as many tokens with Haiku 5.5 compared to Haiku 4.5, so there's a hidden price increase there.</p> <p>If your workloads fit in 100,000 tokens, Haiku is the same price as Luna and reports higher benchmark scores. Above 100,000 tokens, Luna looks like a much better deal.</p> <p>The <a href="https://github.com/simonw/llm-anthropic/releases/tag/0.30">most recent release of llm-anthropic</a> finally fixed it so I don't need to ship a new version of that plugin for every new model. I tested the new model like this:</p> <pre><code>llm install -U llm-anthropic llm anthropic refresh llm -m claude-haiku-5.5 "Generate an SVG of a pelican riding a bicycle" -o thinking_effort low </code></pre> <h4 id="pelicans">Pelicans</h4> <p>Here are <a href="https://tools.simonwillison.net/markdown-svg-renderer?url=https%3A%2F%2Fgist.github.com%2Fsimonw%2F485dfa24b4efa126
A phishing campaign targeting advertising managers by impersonating Gemini, Claude, ChatGPT, Perplexity, and Manus to steal credentials and multi-factor authentication (MFA) codes has added a fake Muse Ads product to its lure lineup – just eight days after Meta launched its personal AI agent. Meta announced Muse on September 8, and by September 17, a very convincing website – museads.ai – for a product called Muse Ads that promised to help advertisers reach buyers and run sponsored placements popped up online. “The operators already had the platform, so adapting it to a new brand can take minutes,” Oleg Zaytsev, lead security researcher at Island, told The Register. “The striking part is how quickly they turned a timely announcement into a credible reason for someone to act. The same platform could then be repackaged around other familiar tasks, from connecting a business tool to claiming a refund or applying for a job.” The security startup spotted the Muse Ads webpage, and upon digging into the scam uncovered that just the page was new. “Its code, from the sign-in forms to the fake browser window, came from a wider operation that had already run fake ad products for Gemini, Claude, ChatGPT, Perplexity, and Manus,” Zaytsev and fellow Island researcher Ofek Ronen wrote in a blog post published Tuesday. All of these products served as lures for browser-in-the-browner (BitB) attacks designed to trick agency staff, media buyers, and manager-account administrators into handing over their advertising account credentials – along with stored payment methods and client accounts – to digital thieves. “For victims, the potential cost is loss of access to an advertising account, unauthorized ad spend, and exposure of linked client accounts,” Zaytsev told The Register. How the scam works BitB is a clever phishing technique originally detailed by a researcher called mr.d0x in 2022. It involves building a fake login window directly inside a legitimate one. The fake window looks l
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At a high level, coding agents have a basic architecture: an LLM calls tools in a loop, while carrying forward the required context. Since that’s easiest to implement locally , many coding agents started out as terminal tools and then desktop apps (Claude Code famously began as a CLI tool). But as the open laptop meme illustrates, modern agent harnesses have frustrating limitations if they’re not able to be managed in the cloud . Since 2025, both OpenAI and Anthropic have been trying to shift their harnesses more into the cloud — but with varying levels of success. The challenges include keeping sessions reliable, isolating tool execution and preserving context. This sounds a lot like container orchestration in the early 2010s , from which emerged the open source Kubernetes in 2014. Kubernetes, developed by Google, became the dominant open source system for deploying and managing cloud applications at scale — it kickstarted the “cloud native” era of computing . Kubernetes uses control loops to keep applications running, recover from failures, and scale when needed. What if coding agents could be managed in the same way? That’s the bet that two Kubernetes creators, Craig McLuckie and Joe Beda , are making with Stacklok . Stacklok architecture; diagram via Stacklok Stacklok raised a $17.5 million Series A back in 2023 from Accel, Madrona and Bain Capital. Initially focused on software supply chain security , the company recently pivoted to Kubernetes-based agentic solutions . Mecatl: a harness built for the cloud The most interesting product of Stacklok is Mecatl, a “cloud-native harness” begun in June as an open source project on GitHub . At first, I thought the name was pronounced “me-cattle” — a play-on-words referencing the ‘cattle vs. pets’ concept of Kubernetes, where you treat your servers, pods and clusters like cattle rather than pets. But no, the name is actually pronounced “MEH-kah-tl” and is an Aztec word meaning a cord or rope ! In any case, what does clo
Tickets for AIE NYC are selling out soon! See you next week! see past AINews issues for subscriber discounts. Pour one out for Mistral, who shipped a decent Large 4 “Le Chonk” model on the new 3800 GB300 cluster funded by their recent Series D . But they were overshadowed by more mathematics results from OpenAI’s internal Navier-Stokes math model - published as a blogpost , repo , and tweet . The best compliment comes from their Navier-Stokes competitor from Anthropic, who despite his personal issues with OpenAI, does not mince words: “It’s obviously the most significant moment in mathematical history.” This bears some qualification , but most experts seem to agree that it solves many of the top 500 open problems in math . In particular, Result 003, the Quasi-Riemann Hypothesis , is somewhere between a Fields Medal result and “the biggest result in number theory in 200 years ”. The most astonishing is the how - while Navier-Stokes was done in 88 hours and 10,000 agents , these solutions were 3 hours of ChatGPT Pro on average . AI News for 10/5/2026-10/6/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space . You can opt in/out of email frequencies! AI Twitter Recap OpenAI Releases 722 Math Manuscripts From an Unreleased Internal Model The release : OpenAI published a broad set of mathematical results from an internal frontier model in a public GitHub repo . It says it consulted the Institute for Advanced Study’s independent Advisory Group on Mathematics and AI on how to release them. Scale and compute : The collection reportedly holds 722 manuscripts grouped into 372 families of related results. They came from an evaluation of about 4,000 research problems and used an average of roughly three hours of ChatGPT Pro thinking compute per result ( summary , Rundown ). Artifacts : The release includes papers, proof artifacts and selected reasoning summar
Only a week after warning about the perils of competitor Z.ai's GLM-5.3 model and its advanced cybersecurity capabilities, Anthropic has expanded its Cyber Verification Program (CVP) – or rather, reconfigured it. "For the past six months, we’ve enabled trusted access through two programs: Project Glasswing and the CVP," the AI biz said. "Now, we’re integrating these programs into one expanded offering, designed to give more security organizations access to the capabilities they need to protect their systems." Project Glasswing and CVP launched in April 2026 alongside the debut of Mythos, the company's highly capable and equally hyped frontier model. Project Glasswing gave partners early access to Mythos so they could scour their systems for vulnerabilities before attackers beat them to it. VulnCheck researcher Patrick Garrity was not particularly impressed with CVEs identified by Project Glasswing, noting that fewer than 0.5 percent of the 225 Anthropic-linked vulnerabilities he tracked were being exploited in the wild. And Anthropic's own warning last month about the risks posed by GLM-5.3 somewhat undermines the idea that there's anything special about its own Mythos model. Even so, Anthropic says that its security program has allowed its partners to spot at least 129,000 verified software vulnerabilities between April and July 2026. And the biz claims that its own open source scanning efforts revealed an additional 5,500 verified vulnerabilities between April and October. "Of these verified vulnerabilities, more than 33,000 have so far been rated as critical- or high-severity," Anthropic said. "This is likely an undercount, as it is based on survey data from only a subset of Glasswing partners. As such, we expect the true impact to be at least five times higher." When these might get patched is unclear. The company's own figures indicate that of 5,674 true positive vulnerabilities, 3,014 are high severity, and 1,522 are critical severity, yet only 516 have been p
"We created this program because we believe the benefits of AI will reach most people through the companies that build on top of models, rather than through the models alone."
<p><strong>Tool:</strong> <a href="https://tools.simonwillison.net/scrimshaw-jukebox">Scrimshaw Jukebox</a></p> <p>I wanted to see if Claude Opus 5.5 could compose music, so <a href="https://claude.ai/share/1f721c20-2499-4d23-b368-3ab57146d956">I tried this</a>:</p> <blockquote> <p><code>I want you to write some computer game music for me. First design simple text based format for the music and build an artifact that can play it out loud - include some example tracks in that artifact</code></p> <p><code>I am looking for music of the quality of the original secret of Monkey Island</code></p> </blockquote> <p>It leaned a lot harder into the Monkey Island theme than I had intended, but the results are surprisingly good.</p> <p><a href="https://tools.simonwillison.net/scrimshaw-jukebox"><img alt="Screenshot of a retro pixel-art web music player. Header in blackletter type reads "Scrimshaw Jukebox", with the text "Six original adventure-game tracks, written as plain text and played by a synthesizer running in your browser. Pick a tune, press Play, then open the score and change it." A large pixel-art scene shows a harbor at night under a purple starry sky: a full moon at top right reflecting on the water, an island silhouette on the left with palm trees and a hut with two lit windows, and a sailing ship moored at a wooden pier. Overlaid on the scene: "Moonlit Harbor" and "Press Play". Below it a bar reads "Play Moonlit Harbor". A control panel has buttons "Play", "Stop", "Restart", "Loop: on", "Edit score", "Read guide" and a "Volume" slider set to about three quarters. A track list of six cards, the first highlighted: "Moonlit Harbor 100 bpm · 4/4 · 16 voices · 1:26", "The Rusty Anchor 112 bpm · 6/8 · 8 voices · 0:56", "The Ghost Galleon 66 bpm · 4/4 · 9 voices · 2:11", "The Jungle Path 92 bpm · 4/4 · 12 voices · 1:29
I’ve had one too many discussions on the cyber risks of open models — where someone assumes that either banning open models happens in a silo (and bad actors will somehow actually be stopped) or that China is a threat and doesn’t care about AI safety — that I feel we’re going to end up making policy decisions that both limit American AI competitiveness and increases long-term cyber risk. I feel like we’re locked in on a path of lose-lose situation, unless people embrace nuance, trade-offs, and scary realities. Much has been written about the views of various players in this debate, which I summarize as follows: Open weight models pose an untenable risk to society’s functionality , occupied by frontier lab leadership and the national security community in the U.S. Western voices saying open weight models are necessary for defense and banning them will make the world less safe , occupied by AI risk moderates to different extremes. I include myself in this bucket, along with Hugging Face’s perspective after the OpenAI incident, and others like Joshua Saxe . Chinese companies continuing to release open-weight models with strong cyber capabilities , who are making decisions based on the risk assessments of their society and government. This list is also organized by volume or clarity of message in the Western AI ecosystem. The risk-focused discussions have been disproportionately visible, including pieces of work which I view as very detrimental to potential of engaging across assumed party lines on this issue. There’s also very little substantive engagement trying to understand how Chinese companies assess risk — rather, mostly arguments that border on ad hominem attacks like “Chinese labs don’t care about safety.” Interconnects AI is a reader-supported publication. Consider becoming a subscriber. The delusions of the anti open-weight alliance The most recent publication from the “open-weights are dangerous” crowd was the report by Anthropic on the risks of GLM-5.3 as a
This past year, OpenAI, Anthropic, and other labs have announced breakthroughs on numerous long-standing mathematical problems, in some cases pushing well beyond what researchers expected current systems to be capable of — including resolving one of the famous Millennium Prize problems. But in classic Silicon Valley style, AI labs are moving fast and breaking things, […]
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