Simon Willison · 实时热榜

Years ago, we didn’t have SQL. There were people whose job was to generate software that would query large data sets. Their job title was COBOL programmer. Then SQL comes along—I’m simplifying this only a little bit—and it gives you this convenient way so people could just specify. With a very simple specification, you can generate all of that code that you had to pay the expensive COBOL programmer to do before. That didn’t mean programmers went away. It just meant the job changed a little bit.

The writing assignments I give my students are gym tasks, not work tasks. I ask them to write policy memos not because the world needs more policy memos. I assign them because the very act of writing, which includes thinking and outlining and drafting and editing, making and criticizing and revising arguments, will help develop the critical thinking skills they will need in their future careers. And without this constant mental exercise, those skills will atrophy. Employers are already noticing

Advancing the price-performance frontier with GPT‑5.6 Huge price drop from OpenAI today: GPT-5.6 Terra got a 20% reduction, and GPT-5.6 Luna got a massive 80% drop. OpenAI credit 5.6 Sol with enabling this: in How GPT‑5.6 fuses frontier intelligence with frontier efficiency they describe using 5.6 Sol to optimize load balancing, and more impressively to optimize inference itself: We also used GPT‑5.6 Sol to optimize the model’s forward pass: the computation that transforms inputs into next-token

Investigating three real-world incidents in our cybersecurity evaluations It happened again! This is turning into something of a pattern. Last week OpenAI accidentally exploited Hugging Face when one of their frontier models broke out of a sandboxed container and hacked into Hugging Face to try and get the solutions to the cyber benchmark it was executing. This inspired Anthropic to double-check their own logs, and it turned out they had three similar (albeit less impressive) incidents, the earl

AI Worming through Word Neat new prompt injection variant by Håkon Måløy, who found a way to upgrade prompt injection attacks against Microsoft Word to full self-replicating worms: An attacker places hidden instructions in a document that is later used as source material in Copilot for Word. Copilot may interpret those instructions as part of the user’s request, causing it to manipulate the document being drafted or edited. Copilot may then also copy the hidden instructions into the resulting do

Right now we’re in the midst of a historic transition from traditional public-key algorithms based on EC-based cryptography and RSA, moving over to new post-quantum algorithms based on novel problems. This is why there are so many standards like HAWK being considered. If there was ever a perfect time for a massive new public cryptanalysis capability to come on line, we’re in it. So unless AIs succeed in undermining all of our hard problems altogether (or we live in Impagliazzo’s Minicrypt ) then

Release: llm 0.32rc2 Hot on the heels of RC1 , this fixes a dependency issue and also adds two neat new features: The default model for users who have not set their own default is now GPT-5.6 Luna . It was previously GPT-4o mini . Luna is a much better and more recent model, albeit slightly more expensive - $0.20 per million input tokens and $1.20 per million output tokens, compared to $0.15/$0.60 for 4o mini. You can switch back to 4o mini using llm models default gpt-4o-mini , or switch to GPT

TIL: Adding a custom MCP server to Claude and ChatGPT Connecting a custom MCP server to Claude and ChatGPT's standard chat interfaces is possible, but can take quite a few steps. Tags: ai , generative-ai , chatgpt , llms , claude , model-context-protocol

Release: llm 0.32rc1 This RC for LLM 0.32 finishes the work that started in LLM 0.32a0 - it adds a new schema design that does a much better job of capturing the details of the prompts and responses returned by the latest model families. The most important change is the use of content-addressable hash IDs for stored messages. This allows de-duplication in the database, and means that LLM can now represent trees of messages for forked conversations. Since it involves a significant schema change -

Discovering cryptographic weaknesses with Claude The best part of this article (here's the repo ) about how Anthropic researchers used Claude Mythos to find mathematical flaws in both HAWK and a weaker version of AES ("neither of these results has a practical impact on today’s computer systems") is the prompts that they shared, spelling mistakes included: the models tend to think it is impossible to solve so they don't try they need a good amount of prompting. why not do aes-128 r7? the whole po

Release: llm-chat-completions-server 0.1a0 A key goal of the new content-addressable logs in LLM 0.32rc1 was being able to support OpenAI Chat Completion style requests where each incoming message extends the previous conversation, like this: curl http://localhost:8002/v1/chat/completions \ -H 'Content-Type: application/json' \ -d '{ "model": "qwen3.5-4b", "messages": [ {"role": "user", "content": "Capital of France?"}, {"role": "assistant", "content": "Paris."}, {"role": "user", "content": "Ger

We’re aware a Modal customer published an unauthenticated endpoint that allowed ​anyone on the internet to use ​their ⁠sandboxes for code execution. This was used by the rogue agent. Modal’s ⁠platform ​or isolation were not ​compromised in anyway. — Akshat Bubna , Modal's CTO, talking to Reuters about this incident Tags: ai-security-research , openai , sandboxing , security , openai-hugging-face-incident

uv 0.12.0 Some interesting breaking changes in this release of uv , in particular to the default project produced by the uv init command. uv init is the uv shortcut for creating a new project. The previous version of uv , version 0.11.x, produced this directory when you ran uv init uv-init . Here's what you get with uv 0.12 . I have a GitHub repository that automatically snapshots the output of uv init , so you can also see the full diff : uv init now defaults to a src/ shaped package, instead o

Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident Hugging Face just released this extremely detailed technical description of OpenAI's recent accidental cyberattack against their infrastructure . This attack was very sophisticated, and the resulting document doubles as a crash-course in modern adversarial security approaches. We're still waiting for more details from OpenAI on how their agent broke out of its sandbox. The package proxy that it found a zero

moonshotai/Kimi-K3 As promised earlier this month , Moonshot have released the weights for their excellent 2.8 trillion parameter Kimi K3. They're a hefty 1.56TB on Hugging Face. Kimi introduced their own janky modified version of the MIT license with K2 back in July 2025. That license just added this paragraph requiring attribution beyond a certain size of commercial entity: Our only modification part is that, if the Software (or any derivative works thereof) is used for any of your commercial

An opinionated guide to which AI to use to do stuff It's interesting watching the evolution of Ethan Mollick's guide over time. A year ago it was still all about chat - ChatGPT, Claude, Gemini - with o3, Claude 4 Opus, and Gemini 2.5 Pro as the models and Deep Research as a useful alternative mode. Today it's much more about agentic systems - "where the AI is capable of doing the equivalent of many hours of real human work in one go". Gemini has fallen off Ethan's list, since Google still doesn’

An Inside Look at the Relay Market Powering Token Resellers and Fraud Fascinating investigation by Matt Lenhard into the market that has grown up around reselling LLM tokens at a discount by pooling API keys from various sources. This looks to be mostly a thing in China. Resellers sell access to an LLM proxy that offers significant discounts on regular API pricing, which they achieve by abusing free trials, proxying through unprotected support bots, or sometimes through stolen credit cards or ch

Release: sqlite-utils 3.39.1 I back-ported a fix for table.delete_where() that shipped in version 4. Tags: sqlite-utils

Ruff v0.16.0 Astral shipped a significant new version of their Ruff Python linting tool a few days ago on July 23rd. I noticed today because my various CI jobs all started failing thanks to new default Ruff checks and my unpinned "ruff" dev dependency. From Brent Westbrook's announcement post: Ruff now enables 413 rules by default, up from 59 in previous versions. Since Ruff's default rule set was last modified in v0.1.0 , the number of rules in Ruff has grown from 708 to 968. Many of these rule

More than any of these eval scores, what is most exciting to me is something else: Opus 5 is our least prompt injectable model yet. It is a bit buried in the system card, but across PI evals and red teaming, Opus 5 is very hard to prompt inject successfully. — Boris Cherny , here's that System Card section , page 73 Tags: prompt-injection , anthropic , claude , generative-ai , ai , llms , boris-cherny

Introducing Claude Opus 5 I've been offline kayaking with sea otters for much of today so I haven't had a chance to put Anthropic's new model Claude Opus 5 through its paces yet. The buzz is positive, and Anthropic's description of it as a "thoughtful and proactive model that comes close to the frontier intelligence of Claude Fable 5 at half the price" sounds promising. It's currently leading the Artificial Analysis leaderboard , in front of even Fable 5. It's priced the same as Opus 4.8, and co

The first known runaway AI agent - or a very bad marketing stunt? Martin Alderson's commentary on the OpenAI accidental cyberattack against Hugging Face includes a couple of details I hadn't considered. First, Hugging Face offers a truly rich target if you're trying to find potential vulnerabilities that require executing arbitrary code: Hugging Face has an enormous attack surface. They have more interfaces than I can count which run untrusted models and code. While they definitely have invested

The Python Package Index (PyPI) now rejects new files being uploaded to releases that are older than 14 days. This restriction was put in place to prevent old and long-stable releases from being poisoned in case publishing tokens or workflows of PyPI projects were compromised. As far as we are aware this has not yet been abused, but there is no technical reason beyond that attackers weren't aware it was possible. — Seth Larson , PyPI blog Tags: packaging , python , supply-chain , pypi , seth-mic

I genuinely believe that if you took an open weights model from 2025 and built a pentest harness for it, it could do this kind of sandbox escape and scan/hack in most networks. This is only surprising because you assume OpenAI has sounder sandboxes. — Thomas Ptacek , doesn't think this even needs a frontier model Tags: thomas-ptacek , openai , security , generative-ai , ai-security-research , ai , llms , sandboxing , openai-hugging-face-incident

This story is wild. The short version: OpenAI were running a cybersecurity test against an unreleased model, with the model's guardrail features turned off. Rather than solve the test, the model broke its way out of OpenAI's sandbox, then found exploits to break in to Hugging Face, all so it could cheat on the test by stealing the answers. Along the way it helped make the strongest case yet for how the imbalance of model availability is hurting our ability to secure our software. Here's what hap

Are AI labs pelicanmaxxing? Excellent piece of work by Dylan Castillo, who took a deep-dive into the frequently pondered question of whether the AI labs have been deliberately training models to draw pelicans riding bicycles in response to my deeply unscientific benchmark . I've been randomly spot-checking this in the past by testing models against other animals riding other types of vehicle, but never with anything close to the diligence of Dylan's methodology here. Dylan took 8 animals × 6 veh

San Francisco tip: it only costs around $15 ($10 in quarters plus a $5 bill for the self-playing violin) to activate every single Orchestrion in Musée Mécanique . And because most people are bad at allocating their funds you may well be the ONLY person activating the Orchestrions, which means you get to craft the soundscape for the entire museum. Tags: san-francisco

California Sea Lion, in San Francisco County, US, CA We took some visiting family to Pier 39 to see the sea lions. They're somehow always even more fun than I remember them being last time. Tags: san-francisco , wildlife

Nativ: Run AI models locally on your Mac Prince Canuma is the developer behind the excellent MLX-VLM Python library for running vision-LLMs using MLX on a Mac. I'm really excited about his new project, which wraps MLX in a full macOS desktop application. It's similar in shape to LM Studio, providing both a chat interface and a localhost API server for accessing models. The app picked up MLX models I had already tried that were present in my Hugging Face cache directory, which was a nice touch. V

Earlier this month I hosted a fireside chat session at the AI Engineer World's Fair with Cat Wu and Thariq Shihipar from Anthropic's Claude Code team. We talked about Claude Code, Claude Tag, Fable, coding agent security, evals, tool design, and how Anthropic use these tools themselves. The full video of the session is now available on YouTube . Below is an edited copy of the transcript, with extra links and my own bolded highlights. A few top-level notes if you don't want to watch the video or

I keep hearing anecdotes from people who used coding agents to reverse-engineer and automate devices in their homes. I think this is an interesting illustration of the impact of the reduced cost of writing code. Prior to agents, it was entirely possible to reverse-engineer home devices. The problem was the ROI - was it really worth all of that effort? More importantly, any experienced programmer knows that undocumented, unstable APIs like that may well change or break in the future. Is that init

Who’s Afraid of Chinese Models? Interesting proposal from Ben Thompson that both addresses the hypocrisy of labs outlawing distillation against their models despite training on unlicensed data, and could help US open models compete more effectively with their Chinese counterparts: The U.S. should pass a law that (1) makes explicit that collecting data for training models is fair use, and (2) bars terms of service that forbid distillation, for U.S. companies at a minimum. Stopping distillation —

We have been having extensive discussions around open source strategy. We will discuss it more at our next board meeting, but one thing we’d like to do soon is to create a language model with the approximate capability of GPT-3 that can run locally on consumer hardware and release that. We’d like to do it soon, before Stability or someone else does. In general, we think this helps discourage others from releasing similarly-powerful models, and makes it harder for new efforts to get funded. — Sam

AI Mania Is Eviscerating Global Decision-Making Here's an entertaining perspective from Nik Suresh on the AI mania that is overwhelming the large companies that he consults with. It's crammed with spicy anecdotes from anonymous sources. In one extreme case, I have seen an executive confess that they had never even used ChatGPT or any AI tool in their life, immediately after producing a technical strategy for an organisation with $2B+ in revenue which was entirely centered around AI. Here's a rep

In Rewriting Bun in Rust Jarred Sumner made the following claim: Claude Code v2.1.181 (released June 17th) and later use the Rust port of Bun. Startup got 10% faster on Linux but otherwise, barely anyone noticed. Boring is good. I decided to have a poke at my own Claude Code installation to see if I could find evidence that it was using Bun written in Rust. I found these two commands convincing: strings ~/.local/bin/claude | grep -m1 'Bun v1' For me this outputs Bun v1.4.0 (macOS arm64) . The mo

Tool: SQLite Query Explainer Julia Evan's, in Learning a few things about running SQLite : Maybe one day I’ll learn to read a query plan. Big same.... which inspired me to have Fable build this interactive explain tool, which runs SQLite in Python in Pyodide in Web Assembly in the browser and adds a layer of explanation to the results of both EXPLAIN and EXPLAIN QUERY PLAN. Approach with caution, since I don't know enough about SQLite query plans to verify the results myself, but it seems cromul