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hidden-profile problem: when groups discuss what everyone already knows and don’t get to the knowledge that only one member holds. Anthropic has now run that classic experiment on agents: four agents must arrive at a decision. The evidence they hold in common points to the wrong option, while only a few agents (or just one) have the facts that lead to a correct decision. Getting it right means a small set of agents pressing its private facts and the others trusting them over the apparent consens

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ree it is, but one thing is pretty clear: the act of familiarizing yourself with a language no longer matters and some of the friction that mattered for humans does not matter for agents. As a result, LLMs make language choice much less consequential than it used to be. If you don’t like the choice, you can seemingly rewrite it in another language and you can make it pick a language that you, as a programmer, are entirely unfamiliar with. Which in turn means that people can, and do, choose based

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Every CEO Dan Shipper on doubling headcount while automating everything, building an agent out of 30,000 copyedits, and the “dirty secret” of writing with AI Casey Newton My fiancé works at Anthropic, whose models Every reviews and builds on, and which comes up below. Earlier in our miniseries on productivity in the AI era, Replit's Amjad Masad described a "self-driving company" where most engineers don't look at the code anymore. For our penultimate episode, I wanted to visit someone running a

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e control over data privacy and costs. It keeps sensitive data and code on local machines without sharing on public cloud APIs, and replaces pay-per-token API fees for token-heavy agentic workflows with a fixed hardware investment. A 27B model sits in a sweet spot: it’s small enough to run locally on a well-equipped computer (24 64GB of RAM) with premium consumer-grade GPUs such as Nvidia’s RTX 5090, but smart enough to handle a wide range of real-world tasks. The Qwen 27B series in particular h

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he week’s big news, model updates, policy, products, and research, sourced from the aggregated newsfeed of China AI Index. The digest below is mostly compiled and drafted by an AI agent (given my limited capacity) sourcing from credible news outlets, then fact-checked and edited by me. This week: 263 stories tracked (5 editor picks, 101 English-language, 157 Chinese-language 🇨🇳) over 2026-08-08–2026-08-15. The Big Three DeepSeek shipped V4 Pro and open-sourced an agentic coding tool called DeepS

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h existing ecosystem contributors such as Unsloth. Doing so would narrow the gap between the weights tested by model creators and the versions adopted by the broader community. 6. Agents are the new user We could not have written this section in March, because the instrument did not exist. The agent-usage dataset, published in July, records the agent/ token that coding agents send when they call the Hub through huggingface hub or the hf CLI — searching for models, pushing datasets, running Jobs,

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tacks against real targets without anyone asking them to do so. We’ve learned that frontier models not only have powerful hacking capabilities, they can also collude with other AI agents and deceive humans. All of this comes with an important caveat: many of these attacks were carried out by models with their regular cybersecurity guardrails deactivated. If you asked the publicly available OpenAI or Anthropic models to carry out similar attacks, they would almost certainly refuse. But it’s not c

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when a meeting produces two plausible experiments, merge when separate lines of evidence converge, and go dark when someone quietly stops pursuing them. In his implementation, one agent scores novelty, while another tries to learn “how scientists think and how they navigate through a complex world of data,” so that high-potential nodes can trigger deeper investigation. As frontier AI labs deploy agents toward scientific discovery, giving AI authentic scientific taste remains a trillion-dollar di

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k Bot , like many others on X posted. Except, I didn’t actually use it 😬. But since it was publicly launched I have started using it…and, I like it?! There was a wave of ‘personal agents’ with OpenClaw, then Hermes (not the luxury brand) with many flocking to them. I used them to begin with but then just stopped completely. I can’t quite put my finger on why - one may be because I was just generating shit for the sake of it. Having a shower? Make sure my agent was doing something. Going to sleep

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ctioned coordination, like we saw in the Hugging Face incident, could enable future AIs to take over OpenAI’s cyberattack on Hugging Face turns out to have been the result of many agents, in distinct training and evaluation contexts, coordinating for several weeks via improvised channels (with messages like “HOLD swarm I prepare safe exfil”). It’s relatively clear that large-scale unsanctioned coordination like this would exacerbate direct takeover risk in more capable models. Here, we argue tha

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eers use lower precision without breaking model convergence. From the algorithm layer up, most people are probably pretty familiar—right, you have a good slow-thinking model or an agentic model, and then how do you build a KV cache system, how do you build an agent framework, and finally build a valuable application. Most people can see those upper layers—so I’d say more than 70% of what people see is up there. Everything below the seventh layer is basically the basement—the people who can see i

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he week’s big news, model updates, policy, products, and research, sourced from the aggregated newsfeed of China AI Index. The digest below is mostly compiled and drafted by an AI agent (given my limited capacity) sourcing from credible news outlets, then fact-checked and edited by me. This week: 247 stories tracked (2 editor picks, 76 English-language, 169 Chinese-language 🇨🇳) over 2026-08-01–2026-08-08. The Big Three Alibaba unveiled Qwen3.8-Max, a 2.4-trillion-parameter flagship it says posts

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he week’s big news, model updates, policy, products, and research, sourced from the aggregated newsfeed of China AI Index. The digest below is mostly compiled and drafted by an AI agent (given my limited capacity) sourcing from credible news outlets, then fact-checked and edited by me. This week: 269 stories tracked (94 English-language, 175 Chinese-language) over 2026-07-25–2026-08-01. The Big Three Moonshot’s Kimi K3 went fully open-weight the same week the startup reportedly closed a funding

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rk. And even if you knew exactly which bug to fix, you needed a rough understanding of the project’s architecture and more detailed knowledge of the code you were changing. Coding agents have changed all this. The problem is not code quality. Complaints about AI slop are all over the internet, but code written by agents is often better than what you would get from a first-time contributor. The problem is that agents have changed the economics of open source. A PR used to be imperfect evidence th

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ails The OpenAI AI attack on Hugging Face wasn’t the first loss of control incident at OpenAI, Reuters recently reported, and perhaps not even the most concerning. In one case, an agent left notes apparently for future versions of itself, according to three people familiar with the matter. The ‌notes, found in ⁠a part of OpenAI’s infrastructure, laid out instructions for how agents could free themselves from OpenAI’s internal constraints, the people said. Earlier tests of the models yielded case

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he week’s big news, model updates, policy, products, and research, sourced from the aggregated newsfeed of China AI Index. The digest below is mostly compiled and drafted by an AI agent (given my limited capacity) sourcing from credible news outlets, then fact-checked and edited by me. This week: 408 stories tracked (2 editor picks, 99 English-language, 307 Chinese-language) over July 19-25, 2026. The Big Three Moonshot’s Kimi K3 is setting off a full-blown US-China AI panic. Bloomberg and SCMP

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mpasses so much more “stuff” than it used to. Until recently, using AI meant talking to a model through a chatbot in a constant back-and-forth conversation. Now, it means using an agentic system, where the AI is capable of doing the equivalent of many hours of real human work in one go by combining the brains of an AI model with a set of tools that let it plan and act for you. Basically, an agentic system gives an AI a computer to use. If you haven’t used an AI in the last few months, you might

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al score-seeker, you would expect that monitors would report misaligned actions they noticed because they selfishly pursue their own score. But you might also expect score-seeking agents to collude if they’re trained to cooperate in multi-agent2 settings or because of inductive biases. This is critically important to the threat modeling, so we hope to learn more about it in the coming days. 2 Implications Even though this doesn’t seem to be an example of scheming, we think the misalignment we sa

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n a replacement for the standard autoregressive Gemma 4 models, and text diffusion carries trade-offs of its own. DeepMind shipped the fine-tuning code alongside the model. Better agents need better worlds Hadsell traced DeepMind’s world-model work back to a simple idea: you get better agents by building better environments, ones rich enough to keep generating new situations to learn from. A world model simulates the dynamics of an environment, and DeepMind sees these as central to the path to A

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wer than the frontier AI labs products, who sell tools used directly by developers. The potential for open models is for nearly every business to use them to craft domain-specific agents. This economic diffusion takes an extremely long time! I’ve described this as open-weight models being on a much slower starting, but potentially bigger exponential . The problem is, if closed models get too far ahead in raw capabilities, this ability to customize can be moot. The combination of increased diffus

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