The Wire · Agents
What's shipping in agentic AI — frameworks, the Model Context Protocol, tool use, and autonomous systems — read continuously and scored for builders.
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.
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Read full story →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
Read full story →The software engine underpinning GitHub Copilot and a growing number of Microsoft products is now written entirely in Rust, with AI agents doing most of the porting work. The migration cost about $120,000 in AI token usage plus about three weeks of a developer's time. However, managers also had to grapple with a few dozen regressions in the resulting code, pointing to AI’s ongoing challenges in understanding Rust. The effort updated the runtime module-by-module until the job was completed, spanning over 135 releases across a 14.5-week time period. Roughly 1.3 port pull requests were opened per day. Overall, agents converted 430,000 lines of TypeScript into 800,000 lines of production Rust. To keep the port as simple as possible, the port only replaced TypeScript modules on a case-by-case basis. It didn’t look to optimize the structure of the runtime itself. That work is next. And Rust, known for its lean performance, did not disappoint. One benchmark measured how quickly the runtime could complete 1,000 one-turn session lifecycles, using a shared client and 100 concurrent pipelines. The original TypeScript implementation completed 7.55 of those lifecycles per second, while Rust running in-process managed 120 per second - representing a 15.9x speedup on that particular workload. In terms of memory, a 10-client batch of agents consumed 1,383 MB with TypeScript while the Rust rewrite consumed only 126 MB serving the same swarm. Within Rust, the work remained in-process instead of spawning external background processes for completion, a requirement for TypeScript. Copilot from VS Code to Microsoft Office At first glance, most users may not know how pervasive the Copilot runtime is. It backs the GitHub Copilot command-line interface (CLI), the Copilot app, the SDK and the GitHub Copilot cloud agent. It shows up in VS Code, Visual Studio, Excel, Outlook, PowerPoint, and innumerable other Microsoft cloud services. Originally, the runtime was written in TypeScript and used
363 points, 420 comments on HN
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
Google is refocusing its CC AI agent on household coordination, letting families share emails, schedules, and tasks so the AI can manage calendars, fill out forms, make shopping lists, plan meals, and more.
The U.S. House of Representatives just passed a bill that creates a federal standard requiring data centers to pay for grid upgrades made in their favor. H.R. 9340, also known as the Ratepayer Protection Act , amends the Public Utility Regulatory Policies Act of 1978, which would require each State regulatory authority and each non-regulated electric utility to consider the adoption of the bill within two years of its passing, if it is signed into law. Go deeper with TH Premium: AI and data centers (Image credit: Microsoft) The data center cooling state of play The custom AI ASIC state of play America’s AI chip rules keep changing — and the rest of the world is paying the price GTC 2026: Ian Buck press Q&A transcript — VP of Hyperscale and HPC speaks out on shelving CPX and shipping LPU decode this year Demand for data center CPUs has surged, and AI agents are responsible This bill would ensure that data centers with a capacity of 100 megawatts or more would have to pay “the full, incremental cost of any generation, transmission, or distribution upgrade necessary to serve the load of such large-load customer, including in the event of such large-load customer terminating a contract or other agreement with the electric utility pertaining to the sale of electric energy, or otherwise ceasing the purchase of electric energy from the electric utility.” This bill closely follows President Donald Trump’s “Ratepayer Protection Pledge,” where he made AI hyperscalers, utility providers, and state governors promise that they will pay their own way when it comes to their electricity demands. All this stemmed from the surprise price hikes that many residential users and small businesses suffered from because of the massive demand by AI data centers and has become one of the primary reasons why the majority of Americans now oppose data center developments in their communities. Oregon is actually one of the first states to have taken concrete steps in controlling the utility price
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
This is today’s edition of The Download , our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Could AI really kill us all? Your questions, answered On Wednesday, MIT Technology Review hosted a live Roundtables event that asked the question many seem to be asking right now: could AI really kill us all? But attendees had more questions than we had time to answer, so we asked senior AI editor Will Douglas Heaven and AI reporter Grace Huckins to tackle some of the best ones. The questions they tried to answer include: am I going to die? Why should AI kill us, if at all? Is AI really dangerous, or is it just tech companies drumming up PR? And what steps can be taken to make sure AI is controlled, monitored and regulated effectively? Here are their responses . —Will Douglas Heaven and Grace Huckins The specter of AI-enabled bioweapons is a wake-up call for biotech One of the ways AI could potentially cause catastrophic harm is by aiding the design and creation of bioweapons. In 2022, researchers found that it was remarkably easy to do this with an AI “molecule generator” built to develop drugs. In less than six hours, the model generated 40,000 molecules that could serve as chemical warfare agents. Today, AI tools can answer questions on almost every area of science, while advances in gene editing and synthetic biology have made biotech tools more accessible. There are safeguards, but none are ironclad. However, scientists disagree about how serious the risk is anyway. Find out why it’s easier than ever to design killer pathogens . —Jessica Hamzelou This story is from The Checkup, our weekly biotech newsletter. Sign up to receive it in your inbox every Thursday. The role of the astronaut is in flux We go to space for geopolitical prestige, manifest destiny, spiritual fulfillment, scientific curiosity, and, increasingly, business opportunities. In the wake of Artemis II, a slew of new books suggest that these justifi
On Wednesday, MIT Technology Review hosted a live Roundtables event for subscribers that asked the question everyone’s asking right now: Could AI really kill us all? But attendees had so many more questions than we had time to answer in the 30 minute session. So we asked our senior AI editor Will Douglas Heaven and AI reporter Grace Huckins to round up some of the best questions attendees submitted and try their best to answer them. Thanks to all who submitted questions! Am I gonna die? Yes, eventually. Unfortunately, my journalistic powers of prognostication aren’t powerful enough for me to tell you how. But it certainly could be because of AI. AI-powered drones have already killed people in Ukraine, and AI-driven cyberattacks on hospitals will surely claim victims before long. Could AI go even further, and kill all of us? Less likely. But some people—quirky people, but undeniably knowledgeable about AI—have been warning for years that this could happen. And while I’m not yet stockpiling canned food or trying to get in good with a bunker-owning megabillionaire, I have noticed that the doomers’ predictions about AI capabilities and alignment have, over the past couple of years, proved disconcertingly accurate. That certainly doesn’t mean that their more dire forecasts will come true, but it’s enough for me to sit up and take notice. — Grace Huckins Are you going to die because of AI? I’d say there’s a non-zero chance. Let’s say you’re unlucky enough to be the victim of a freakish near-future event or accident. Maybe it’s a cyberattack carried out by a swarm of AI agents on critical infrastructure. Sadly, a scenario like that now no longer feels as far-fetched as it once did. Or maybe a novel AI-designed pathogen cuts through the population. Or the world economy crashes, causing conflicts and famine. Both plausible, but I think less likely. Are we all going to die because of AI? Nope. There are no circumstances outside of apocalyptic science fiction in wh
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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
597 points, 340 comments on HN
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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