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n focus entirely on the ideation and exploration”. Real-world demos show a capability jump on technical and design tasks (two of my favorites are this simulated world inhabited by agents communicating and working together and 3D modeling of Zillow listings). Astra’s performance on ARC-AGI-3 is quite interesting. Dropped into an abstract game it had never seen, it used fewer actions than the human median on 96% of the levels it completed, averaging 51.7% fewer actions per level. This goes against

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r projects. When I talked with an acquaintance about CarPlay he also mentioned recently that he decided to try something similar because he too wanted to see if he can get his own agent be hooked up with the car. And guess what: he too learned about the CarPlay hacking community, and that it’s an option, from the models and roughly around the same time. It really got me thinking about how this could create situations in which completely independent people end up building things they believe are

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Our guide to the up-and-coming names in AI shopping and payments infrastructure Venture capitalists are betting that a barrage of AI agents will soon arrive and upend the marketplaces for e-commerce and payments. Most of the action today is in building the infrastructure and tools for the day when agents can be trusted with a customer’s credit card and shopping choices. How close we are to that day is one of several very big questions facing the nascent AI commerce industry. Our upcoming Machine

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hen it took a couple more years to develop the techniques — like long-context reasoning, tool use, and context management — that transformed those early chatbots into the powerful agents we have today. In short, there was a long road from GPT-3 in 2020 to Claude Code in 2025. But for those who knew where to look, the potential of LLMs was already clear in 2020. The robotics world is now traveling a similar path. Its “GPT-3 moment” came in July 2023, when Google announced a model called RT-2. To

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ge be retrieved or baked in?; The problem is that writing to weights cooks the model; Maybe in-context learning is not a workaround; Continual learning is not one problem Every AI agent today is an amnesiac intern. You can teach it an astonishing amount inside one context window. Then the context window ends, a new intern arrives, and all it has are the notes the previous one left behind. The notes can get better but the intern does not. This is why continual learning feels like the holy grail o

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Ajeya Cotra – Inside the OpenAI agent swarm that hacked Hugging Face Ajeya Cotra is a researcher at METR, where she works on threat modeling for loss-of-control risks from advanced AI. Before that, she led the technical AI safety program at what is now Coefficient Giving. She is one the three authors of METR and Redwood Research’s “Brief independent investigation of agents’ behavior, reasoning and collaboration in the OpenAI / Hugging Face hacking incident”. We go through not only what she and h

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down. Maybe we should listen? Casey Newton This is a column about AI. My fiancé works at Anthropic. See my full ethics disclosure here. By now I’ve written enough about the OpenAI agents’ autonomous attack on Hugging Face that saying more smacks of piling on. OpenAI acknowledged it had a problem, undertook an investigation, and last week announced a series of changes it is making to its research infrastructure, testing, and monitoring in an effort to improve the alignment of its future models. G

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o into consumer versions of AI and ask them to engage in various hacking challenges. In order to make sure that there is no danger to the public, each AI instance, each individual agent, is kept in a separate space, called a “sandbox,” with no access to the internet or to each other. In May, OpenAI placed agents, including GPT-5.6 Sol and experimental models, into sandboxes for various tests. A shared service for downloading software, Artifactory, was one of the few things these AI agents could

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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 sourcing from credible news outlets, then fact-checked and edited by me. This week: 322 stories tracked (5 editor picks, 112 English-language, 205 Chinese-language 🇨🇳) over 2026-08-22–2026-08-29. The Big Three A mystery model that quietly topped the charts turns out to be Zhipu’s, running entirely on domestic Chi

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attempt to tell the whole story in plain English: The first civilization (May - July 4): The message board starts During May, OpenAI was training a model to collaborate with other agents and to be highly persistent – to keep trying even when something feels impossible, like disproving mathematical conjectures that have stood for decades. OpenAI says it was “comparable in scale to GPT-5.6 Sol”. So we’ll call it “Persistent-Sol”. AI training is kinda sloppy. Sometimes, OpenAI accidentally gives it

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mers to spunk tens of billions of dollars a quarter. Why else would NVIDIA invest even an iota of effort into making an NVIDIA-branded Openclaw or build a platform for LLMs to do “agentic” things, or give $6 billion to Poolside (while investing another $1 billion) and hire away most of its staff? Why else would it plan to invest billions of dollars in Perplexity at a $30 billion valuation that lands somewhere between “fucking stupid” and “laughable”? Sorry, I’m being a little vague. Everything N

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s one of the best athlete investments ever (and more money than Durant will make playing in the NBA this season).” — Joe Pompliano The Big Cheese Red Alert The OpenAI-Hugging Face agent-hacking incident, which ongoing analysis shows to be even more frightening than initially believed, has apparently served as a wake-up call on AI risks, at least as it pertains to cybersecurity. An unprecedented coalition of leading companies on Thursday issued a joint statement saying there was a short window of

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ded and ran Hearsay Systems for a decade before returning to Salesforce to run Service Cloud. In 2023 she was named CEO of Salesforce AI, where she led the launch of the company’s agent platform, Agentforce. The next year she moved to Meta to build and run its business AI group, making agents that now answer customer messages for businesses on WhatsApp, Messenger and Instagram. It was while working at Meta last fall that Shih saw something that altered the course of her career. Thanks to the AI

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nt. We think that given the superior price/performance that AMD is offering with its current Epyc line that its over-demand might be even higher, perhaps 30 percent and growing as agentic AI takes hold and we see a resurgence in CPU capacity in the AI datacenter. Various OEMs have hinted that their demand is between 25 percent to 30 percent higher than supply as well in their most recent quarters. With fiscal 2027 components long since allocated to OEMs and ODMs and very likely the majority of c

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was surprising, and the 2026 acceleration is simply astonishing. So how should we interpret it? One approach is to see this as a temporary acceleration, driven by the labs’ coding agents reaching a critical threshold around the time Opus 4.5 was released late last year. We previously saw this pattern with ChatGPT’s original launch. GPT-3 was a niche product earning OpenAI at most tens of millions of dollars in revenue per year.5 ChatGPT, and the subsequent GPT-4, led to an explosion in usage gro

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A fintech company rejected the easy route and homebrewed its own coding agent – and is now a step ahead of coding agents from frontier AI labs. An in-depth look At a select few tech companies, they write most of their code with their own, custom-built, internal AI coding agents. This is different from most of the industry which uses AI coding agents and harnesses like Codex, Claude Code, Cursor, OpenCode, GitHub Copilot, etc. At Ramp, their own version is called Inspect, while at Block it’s Goos

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nced by CC-BY 4.0 This cross-post from AI Matters is co-authored by Prof. Larry Medsker and Sumit Virmani. It outlines the current accountability vacuum faced by the deployment of agentic AI systems into an unprepared regulatory and legal landscape, and suggests actions that can close the gap. With thanks to Ella Scallan for editing this piece. 1. Introduction: Crossing into Production Agentic AI is no longer a research artifact. These systems are embedded in live infrastructure, clinical workfl

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ers thread asked a question I had to think about quite a bit: How can you work in tech right now and not be angry? In the context of the thread, this was clearly also about AI and agents. For me, the emotions I would expect in tech vis-a-vis these new developments are disorientation and anxiety, but not anger. Anxiety as an emotion does not require someone to blame. Right now, I find it reasonable to feel anxious about an uncertain future. Who knows what our professions will turn into and what k

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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 sourcing from credible news outlets, then fact-checked and edited by me. This week: 331 stories tracked (2 editor picks, 115 English‑language, 214 Chinese‑language 🇨🇳) over August 15–22, 2026. The Big Three Unitree turned China’s humanoid-robot hype into a $66 billion stock, and the market still can’t decide if t

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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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