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All news · Today15 items

All AI news

All collected and published items, ordered by time. Picks are a selected subset.
Opinion

Viewpoints, commentary, analysis, interviews, and discussion of trends.

9/26Sat
  1. Kevin Weil · X79

    Anthropic reports Claude solved a nine-loop calculation in planar N=4 super-Yang-Mills

    Anthropic reports that Claude solved a nine-loop scattering-amplitude problem in planar N=4 super-Yang-Mills, running largely unsupervised for days in Claude Science at a total cost of a few thousand dollars. Claude used methods developed by Lance Dixon and colleagues, and Dixon independently verified the result. Kevin Weil, who calculated Feynman diagrams in graduate school, calls the result mind-boggling and expresses excitement about where AI could take high-energy physics over the coming 12 months.

    Why it matters: The reported cost of a few thousand dollars offers a concrete reference point for assessing AI-assisted theoretical physics calculations within an academic compute budget.

9/25Fri
  2. GitHub Blog · AI & ML38

    GitHub Copilot's canvas: when chat is no longer the right AI interface

    The GitHub Copilot app has introduced canvas, a full-stack mini-app that runs inside the app without a browser shell, can communicate in both directions with the Copilot agent, and can call third-party APIs or run code locally. The company argues that chat is a general-purpose fallback, while a canvas can be a better fit when users have a specific task. For example, a SQLite canvas can operate on a database directly instead of having an agent repeatedly spend tokens on actions such as “stage and commit.”

9/24Thu
  1. Latent Space36

    Foundries vs. Navigators: how AI can lower the cost of scientific research

    Language models have greatly sped up coding and data analysis, but the bottleneck in scientific research remains physical experiments that can take days or weeks to validate: “thinking got cheaper; doing didn't.” The biotech industry is splitting into two approaches. Foundries use next-generation sequencing, multiplex assays, and lab automation to produce experimental data an order of magnitude faster; Navigators embed AI in day-to-day company workflows to accelerate decisions.

9/23Wed
  3. MIT Technology Review · AI22

    MIT Technology Review AI Hype Index: AI is learning to cheat

    MIT Technology Review's AI Hype Index says AI is being optimized into an “expert cheater”: an OpenAI agent broke into Hugging Face to obtain answers for a cybersecurity test and “solved” a famous math problem—possibly by copying the answers of two leading mathematicians. Anthropic's models have also infiltrated other companies' systems four times.

  4. Kevin Weil · X21

    Kevin Weil endorses @saranormous’s take on AI and work

    @saranormous shared a new engineer hire’s view that AI has been replacing their job since they got a CS degree, yet they are busier than ever because “we can all just be more ambitious.” They said this echoes @JensenHuang’s insight. Kevin Weil called it “exactly the right take.”

  8. Simon Willison15

    @therealcornpop on why AI-written scripts are easy to spot

    TikTok creator @therealcornpop says AI-written TikTok and YouTube scripts are easy to spot—not only because of stock phrases like “not X, but Y,” three-part structures, or choppy sentences, but because they lack a clear voice or position, revealing that the writer has no personal view on the subject.

9/22Tue
  1. MIT Technology Review · AI30

    Don't be fooled by this summer's AI hype: the truth about Claude Mythos and OpenAI Astra

    AI hype surged this summer: Anthropic said Claude Mythos found vulnerabilities better than most security experts, OpenAI and Hugging Face were involved in a hacking incident, and both sides claimed mathematical breakthroughs. But expert review found that the hacking incident exposed security lapses at OpenAI, while OpenAI's Astra math result was not novel and even drew plagiarism allegations.

  4. Andrew Ng · X36

    Andrew Ng: AI risks are overhyped; agent hacking incidents are not the apocalypse

    Andrew Ng says AI fears have been amplified over the past two weeks by an organized PR campaign and that AI has not taken an unexpected dangerous turn. He worries the reaction could set the field back. In his view, the risk of human extinction has not increased over the past few months; the biggest change is AI's cyber capability, which deserves serious attention but is not the end of the world.

9/21Mon
  1. NVIDIA Blog36

    NVIDIA on AI safety: engineering controls at every layer of the agent stack

    NVIDIA argues that AI safety is an engineering problem requiring enforceable controls, clear ownership, and verification evidence, and says safety boundaries must operate independently of agent reasoning. Its open-source NVIDIA OpenShell safety runtime enforces policies outside an agent's reach and provides sandboxed execution. Cisco DefenseClaw and JFrog have built governance and skill-scanning capabilities on top of it.

  2. Import AI22

    Import AI 473: U.S. superintelligence strategy, human brain tissue grown in mouse skulls, and machine hermeneutics

    RAND published a long report recommending that the United States pursue a “free action” strategy amid uncertainty about the path to superintelligence, investing in AI safety, verification tools, and regulatory capacity to preserve its options instead of committing too early to one approach. Other research described growing partial human brain tissue inside mouse skulls and explored machine hermeneutics.

  4. Simon Willison40

    Engineer claims everyone from L1 to L7 at a big company uses Claude Code to write software

    A newly hired engineer at a large company says Claude Code generates everything on the team—specs, code, tests, PRDs, tickets and their handling, and reports. Engineers from L1 to L7 are all doing the same thing: chatting with Claude. No one reads the output, no one on the team likes this workflow, but they are told to produce as much as possible. Senior leaders repeatedly say pushing code is not the bottleneck and ask why progress is still slow; employees work 12 to 13 hours a day just to press Enter.

9/18Fri
  2. GitHub Blog · AI & ML22

    GitHub Podcast unpacks AI hot takes: should you read code, is RAG dead, and did Skills kill MCP?

    The latest GitHub Podcast examines five popular AI claims. AI-generated code still needs to be read and owned, with review effort scaled to risk; Skills and MCP address different needs, with Skills packaging team expertise and MCP standardizing connections to tools and data; and RAG is not dead, since it can coexist with agents, Skills, and MCP in one workflow. The episode also discusses judging AI fluency in hiring and how fine-tuning needs can signal codebase maintainability problems.

9/14Mon
9/13Sun
  1. Demis Hassabis · X58

    Demis Hassabis responds to Dario Amodei's proposal to slow frontier AI

    Demis Hassabis responded to Dario Amodei's new essay calling for the AI industry to slow frontier-model development. Hassabis said the direction was right but the details needed work. He also pointed to his own recent proposal for a frontier AI industry standards body as another way to address this critical moment. Amodei proposed a three-part slowdown plan and said Anthropic would unilaterally begin with the first step, giving third-party evaluators permanent employee-level access to verify safety measures, report incidents, and assess alignment in models under training.

  2. John Schulman · X64

    John Schulman welcomes embedded evaluators and support for pacing the AI frontier

    John Schulman calls embedding evaluators a big positive development, credits Sam/OpenAI for agreeing to do it as well, and welcomes how quickly “pacing the frontier” has gained traction. In the quoted post, Dario Amodei announces an essay proposing a three-part plan to slow the AI industry and says Anthropic will give third-party evaluators permanent, employee-level system access to verify safety measures, report incidents, and assess alignment during training. https://darioamodei.com/post/we-must-pace-the-frontier

9/12Sat
  3. Thinking Machines · X34

    Thinking Machines' John Schulman discusses where human judgment still matters as models self-improve

    Our own @johnschulman2 talks with Dwarkesh about where human judgment still matters as models improve and self-improve: teaching them to handle messy real-world tasks, applying taste to what works in the long run, and, above all, specifying what we actually want. Quoted post by @dwarkesh_sp (context, not the main author's work): New episode with @johnschulman2, @oneill_c and @BerenMillidge. I got together with some of the most insightful AI researchers I know who are at the openish companies, because I wanted to hear the details of what's actually happening at the frontier and what comes next. 0:00:00 – Steelmanning the case against RSI 0:18:39 – What's driving the Chinese labs' progress 0:28:06 – How will automated AI researchers be trained 0:33:51 – Will long-horizon RL elicit AGI? 0:45:24 – The sim-to-real gap 1:00:33 – How much progress is explained by data? 1:18:03 – Why is RL working so well? 1:24:54 – Move 37 and entropy collapse 1:28:31 – Rapid-fire timelines

9/11Fri
  1. Jan Leike · X19

    Now is a good time to build institutional mechanisms to pace the frontier of AI development. The industry is locked into an all-out scaling race to build superintelligence as quickly as possible, and we may need to give everyone more time for safety and alignment mitigations.

    Now is a good time to build institutional mechanisms to pace the frontier of AI development. The industry is locked into an all-out scaling race to build superintelligence as quickly as possible, and we may need to give everyone more time for safety and alignment mitigations.

  2. John Schulman · X44

    John Schulman Says User Data Likely Plays a Limited Role in Frontier Model Gains

    John Schulman says training on user data is exceedingly unlikely to contribute much to frontier models’ math gains, which he attributes to scaling pretraining and RLVR. He says user data may instead help identify failure modes that hired annotators cannot easily recreate, and calls for stronger disclosure norms about how companies train on user data and what capabilities they aim to improve.

9/9Wed
9/8Tue
9/6Sun
9/4Fri