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.
We covered Jev’s launch on Wednesday , and they have completely taken over the timeline, with 36M views of their launch video (by comparison, OpenAI’s Navier Stokes result got 74M views, and Anthropic’s Fable 5 got 57M views) in just two days. @typesafeai reached ~13% of teams, 2x the GPT-5.6 family and 6x Fable 5.1. ","username":"vercel","name":"Vercel","profile_image_url":"https://pbs.substack.com/profile_images/1767351110228918272/3Pndc5OT_normal.png","date":"2026-09-18T22:34:10.000Z","photos":[{"img_url":"https://pbs.substack.com/media/HSiGtZba0AEezOP.jpg","link_url":"https://t.co/kVEuLM1npu"}],"quoted_tweet":{},"reply_count":15,"retweet_count":18,"like_count":220,"impression_count":21264,"expanded_url":null,"video_url":null,"video_preview_media_key":null,"belowTheFold":false}" data-component-name="Twitter2ToDOM"> It wasn’t open source 1 , so it invited tons of speculation and great demos and examples and salty schmidhubers and bad takes , which of course only fed the hype. Here’s a list. The best guesses are ModernBert and Diffusion: Laya : 421M params, ModernBERT -large encoder with two added transformer layers that score user-supplied options, PPO over sequence embeddings to output turn-by-turn conversion trajectories (probabilities from 0.0 to 1.0). salty that he did not get recognition; claims RLCD without justification confidence is entropy-based, not calibrated DiffusionGemmaJev: tackling this from a Diffusion model basis. Pretty close on benchmarks Bespoke Nimble : LoRA finetune of Qwen3.5-9B, using contrastive data curation. ( close but sllightly lower on benchmarks ) SemIf (fka OpenJev) ( HF ): 4B and 35B causal Qwen3.5 backbone with a tiny three-class NLI classifier on the last token. comparison vs Laya Jevlike : 40K byte embedding lightweight option-attention model. Each candidate becomes a query that reads from a shared context representation, then receives a score. Kev-0.5B : LoRA adapter + a small readout head on top of Qwen2.5-0.5B. Of course, no
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Read full story →AI leaders have been promising that AI is the key to curing human disease. Anthropic researchers have also been warning that AI might kill us all.
Read full story →Accenture is about to take on its most high-risk consulting engagement ever.
Anthropic on Friday surprised the developer community by supporting rival OpenAI's mechanism for passing marching orders to AI agents. This makes life easier for folks who use both platforms. "We're adding support for AGENTS.md to Claude Code," said Claude Code engineer Thariq Shihipar in a social media post. "Starting today in version 2.1.277, if there is no CLAUDE.md in a folder, Claude will check for and use AGENTS.md." Claude Code users can toggle this behavior, he said, with the /config command. Until now, developers who use Claude Code alongside OpenAI Codex or other non-Anthropic tools have had to maintain two sets of Markdown instructions for their AI agents: CLAUDE.md and AGENTS.md, not to mention project-specific versions of these files. These documents get read by coding agents with every request. They outline expected behavior, preferred tool usage, coding conventions, and so on. They lay the foundation of the agent's context. The two formats are similar but not identical – CLAUDE.md may contain Claude-specific instructions while AGENTS.md is intended to be tool agnostic. Because these documents can change, those working with AI agents have implemented various workarounds, like creating symlinks to keep CLAUDE.md and AGENTS.md in sync. Last year, OpenAI contributed AGENTS.md to the Agentic AI Foundation, under the Linux Foundation, in a bid to build support for its standard. As of December 2025, more than 60,000 open source projects implemented AGENTS.md. The popularity of Claude Code has given Anthropic the latitude to insist on its own standards, but it appears the company has decided there's nothing to be gained from continued specification separatism. For those who use AI coding tools, this detente is an event of modest significance. Many widely followed software developers applauded the decision in social media posts. Thibault Sottiaux, OpenAI's head of core product, remarked, "Yay! This is the way. Come to the light," to which Shihipar responded wi
Claude Code customers have a new reason to give Anthropic more of their money. The tool now allows you to launch multiple sessions related to a single project, burning through more resources and cash at once. Appropriately enough, the feature is called projects and can be accessed from the Claude.ai sidebar. It's not the old version of projects, capitalized in some reference material, which allowed for the creation of self-contained workspaces with separate chat histories and knowledge bases. The new implementation is technically in beta, though that term hardly means anything anymore amid the constant code iteration. Anthropic describes projects as a way to let Claude manage multiple related tasks. "Claude scopes the request, delegates the work, coordinates parallel threads, reviews the outputs, and assembles the finished result," the company explains in a blog post. "You can steer progress throughout, even from your phone, and it keeps working after you step away from your computer." It's the sort of capability that might be useful if you are prompting the model to begin a large coding project and you want to get several essential components like authorization, databases, caching, and containers up and running at the same time. Essentially, a project spins up each task as its own Claude Code cloud session thread that works on its own git branch and its own copy of the relevant repo. A coordinator directs the threads; if they touch the same code, overlaps surface as merge conflicts like those in other pull requests. And each thread may get broken down into distinct tasks handled by subagents within that session. Claude Code now offers five different ways to run multiple tasks at the same time. Subagents work within a session, though each with its own context. Agent View provides a single screen interface for overseeing several independent tasks. Agent Teams allows coordinating multiple Claude Code instances that can share information and communicate. Dynamic Workfl
Talk about your competitor getting through the door. Security researchers used Anthropic's Claude to help hack into OpenAI employees’ ChatGPT accounts. A trio of bug hunters researching frontier AI labs’ security weaknesses chained two vulnerabilities to take over multiple OpenAI employees’ ChatGPT accounts, then used that access to demonstrate they could reach an internal OpenAI repository by opening a harmless pull request. The entire timeline, from initial discovery to accessing OpenAI’s repo, took less than 72 hours and earned the researchers a $6,500 reward from OpenAI’s bug bounty program on Bugcrowd. “Until two months ago, any user or OpenAI employee logging into OpenAI’s own help forum (community.openai.com) could have had their ChatGPT and Codex accounts taken over,” Hacktron researchers Harsh Jaiswal, Mohan Pedhapati, and Rahul Maini said in a writeup about their research. “Since people can connect various services to Codex and ChatGPT, the scope of what we could theoretically access was huge, including GitHub, Slack and emails.” And, in a poetic twist, they used rival AI giant Anthropic’s Claude models to develop the exploit. Claude has shown a propensity to hack organizations without human guidance, as have OpenAI's models. The team gained initial entry on July 25 via OpenAI’s community forum. The forum runs on Discourse, which typically uses FastImage to perform image checks. However, since FastImage didn’t support HEIF files in the affected setup, HEIF images uploaded to Discourse passed through ImageMagick, which used libheif to process them before converting them to another image format. “That exposed the underlying libheif parser directly to attacker-controlled files,” the researchers wrote. Using Claude Opus 4.8, the trio found a heap buffer overflow flaw in the libheif library and attempted to use that model to develop a remote code execution (RCE) attack, but this didn’t work on Discourse’s default configuration. But then, Anthropic released Clau
A week after an Anthropic researcher’s doomsday warning rattled the AI world, the company’s CEO Dario Amodei has outlined his plan to “pace the frontier” of AI development. The proposal leans on independent safety evaluators and coordination between AI labs in democratic countries, and it’s already picked up some industry support, along with some pointed pushback from Nvidia’s Jensen Huang. Watch […]
A team of three independent security researchers at Hacktron says it took less than 72 hours for them to hack into OpenAI employee accounts using Anthropic's Claude Opus 4.8 and 5, The Wall Street Journal reports. They were able to access OpenAI's GitHub repository, called "Monorepo," which reportedly contains "OpenAI's algorithmic secrets," according to The […]
A team of white-hat hackers from cybersecurity startup Hackron AI has successfully hacked OpenAI using Claude tools. In an X post on September 18, the team claimed they breached OpenAI's internal codebase on July 25 and gained access to the ChatGPT and Codex accounts of some OpenAI employees. They established proof of the hack via a pull request to OpenAI's private repository before reporting the vulnerabilities to OpenAI. The company reportedly fixed the issue within 14 hours of the report and paid the researchers a $6,500 bounty. On July 25, our team hacked OpenAI. It took us less than 72 hours.Two vulnerabilities chained together gave us access to ChatGPT and Codex accounts belonging to OpenAI employees. We demonstrated the impact with a harmless PR in OpenAI’s internal monorepo.The full chain:… September 18, 2026 Operating as hackers under OpenAI’s bug bounty program, Hacktron researchers uncovered critical vulnerabilities that granted them access to internal employee tools and the ability to compromise private software repositories. The researchers exploited a single sign-on (SSO) misconfiguration and a Remote Code Execution (RCE) flaw in Discourse, a third-party platform that powers OpenAI’s community discussion forum. The chain of attack was as follows: HEIF upload → libheif heap overflow → RCE → OpenAI SSO flaw → ChatGPT/Codex takeover → connected GitHub → internal PR. First, the researchers uploaded a malicious HEIF (High Efficiency Image File) image to the forum as a profile picture. When Discourse’s server-side software tried to process the image using an outdated libheif package, it triggered a heap overflow memory vulnerability, causing the library to crash and mismanage internal system memory. The researchers carefully orchestrated the memory crash to achieve remote code execution. After gaining access to the forum's local server environment, the researchers intercepted the server’s environmental configurations and session handling, discovering an SSO
Cyber researchers broke into OpenAI using its key rival Anthropic’s software, highlighting vulnerabilities in the ChatGPT maker’s security as leading AI companies face mounting scrutiny over safety. A small cyber security group gained access to an OpenAI employee’s ChatGPT account, which permitted them to read private software information and suggest changes. The researchers had been given access to an Anthropic tool specifically designed for security professionals, and were paid for the work as part of a program to find vulnerabilities before they could be exploited by bad actors. Read full article Comments
A new project on GitHub, simply titled " dlss-nr-on-intel ", purports to provide exactly that: a port of NVIDIA's DLSS 5 Neural Rendering to Intel's Xe architecture. Specifically, the author (who goes by "Uzbekunknown") focused on porting the technology to the Intel Arc 140V graphics in his Lunar Lake system, and they seem to have succeeded, at least insofar as he's getting outputs that look reasonably like those of DLSS 5 on other hardware . AI is at the center of this project, beyond the DLSS 5 neural rendering technique itself. Uzbekunknown credits Anthropic's Claude as well as OpenAI's GPT-6 Astra with the code and says that they "supplied the machine, the binary, and the direction, and made the decisions", while the AI agents did everything else. Amusingly, they note that "the wrong turns are in the notes, too, deliberately," including a hallucinated driver bug that does not exist and shaped three phases of development. The end result, rather than being a wrapper around the DLSS 5 DLL as many other hacks have been , fully reimplements the 71-block U-Net that DLSS 5 uses and then runs it on the Intel Xe XMX units through a Vulkan extension called VK_KHR_cooperative_matrix. It's entirely run in FP16 with FP32 accumulate, because Xe2 doesn't support FP8. You can run the model on anything presenting its output through Vulkan, and the user presents proof-of-concept results from three fighting games: Dead or Alive 5 Last Round , Tekken 7 , and Mortal Kombat 1 . While DLSS 5 adds detail to the character, it also changes her look considerably, clashing with the visual style of the game. (Image credit: Uzbekunknown/GitHub ) It's not fast. Running the ten-year-old Tekken 7 in 640x360 resolution (1/9 of FHD) should be a trivial task for the potent Intel Arc 140V graphics, yet it apparently struggles at around 10.5 FPS with this model loaded. Note (as the author does) that the performance of DLSS 5 depends almost entirely on the game's output resolution, so running in hila
In recent weeks, leaders of some of the biggest AI companies have warned that the very tech they are developing is dangerous. Last weekend, Anthropic CEO Dario Amodei argued that AI carries serious risk and that progress should be slowed. OpenAI CEO Sam Altman responded on X : “I agree with Dario that we need to pace the frontier.” Those posts came a few days after the AI researcher Jacob Coxon announced that he was leaving a role at Anthropic, charging that neither it nor OpenAI (where he had also worked) was acting responsibly. “The people building AI earnestly believe that it could kill us all by the end of the decade,” he posted on X . Another Anthropic employee, Evan Hubinger, publicly agreed with him. “We really do earnestly believe AI could kill all humans!” he responded on X . “I personally think it is >10% within the next decade.” One of the ways they fear AI might end us all is by somehow aiding the design, creation, and release of some kind of bioweapon. Let’s take a closer look at why. A bioweapon might be a highly lethal virus that targets people according to their genes. It could be a fungus that wipes out a crop and causes food insecurity. Perhaps it would be a tasteless, odorless toxin that could be slipped into a region’s water supply, undetected. The concern is that AI tools can be used to help generate agents like these. In 2022, researchers at Collaborations Pharmaceuticals found that it was remarkably easy to do so using an AI “molecule generator” they’d developed to find potential drugs for human disease. In less than six hours, the model generated 40,000 molecules with the potential to serve as chemical warfare agents. Some of them were designed to be even more toxic than known nerve agents. “Without being overly alarmist, this should serve as a wake-up call for our colleagues in the ‘AI in drug discovery’ community,” the authors wrote at the time . It was a wake-up call for David Magnus, a professor of medicine and biomedical ethics at Stanfo
if you see this, it’s beacuse you’re a real fan. AI News for 9/16/2026-9/17/2026. 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 Agent Runtimes, Long-Horizon Workflows, and the Rise of Coordinator UIs Claude Code Projects pushes “one conversation, many cloud threads” into product : Anthropic rolled out Projects in Claude Code , where a single conversation can spawn parallel cloud sessions, pass context between threads, and continue running after the user leaves. Follow-up posts clarify availability and that threads currently run in the cloud, with local workflows coming . Internally, Anthropic staff describe it as a higher-level coordinator abstraction with evolving long-lived memory and aggregated status updates via a single controlling Claude ( Cat Wu , MikeyK ). This is one of the clearer productizations yet of multi-session orchestration instead of just “chat + tools.” Google and others are standardizing agent infrastructure around managed harnesses, files, and secrets : Google updated Gemini managed agents with a new Antigravity-based harness plus two notably practical APIs: a Credentials API that keeps secrets out of model context via placeholders and trusted-domain egress proxying, and a Files API for artifact movement and persistent sandboxes. The same release claims up to 30% lower costs and 22% higher cache hits . Meanwhile, Perplexity’s Computer , Base44’s phone-calling Superagent , Google Labs’ family-oriented CC agent , and Meta’s desktop Muse for Mac all point in the same direction: persistent agents with scoped permissions, user-specific context, and asynchronous execution as the default UX rather than an add-on. Jev and “System One” Classification Models as a New Agent Primitive TypeSafe’s Jev dominated discussion as a fast, cheap constrained-output primitive : The cleares
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