Simon Willison

Release: datasette-llm 0.1a10 Fixes a bug that affected my agent.datasette.io demo site, where the set of available LLM models was not correctly restricted to just those in the configuration. Tags: datasette , llm

Release: datasette-agent 0.5a0 This is a significant release of Datasette Agent . The most notable new feature is called the panel - it lets the agent chat open and then sit next to pages from Datasette, for example a table or query page. When combined with datasette-apps this means you can prompt a database-backed app into existence and then modify it from the chat that sits next to that app. You can try it by signing in with your GitHub account to agent.datasette.io . Tags: projects , datasette , datasette-agent

Yesterday's hot paper was Sparks of Weight-Space Programming: LLMs can natively read &amp; write neural network weights by Rikiya Takehi, Jayden Teoh, Alex Zhang, and Omar Khattab. The paper found that frontier models, in particular GPT-6 Astra, can both interpret and modify the raw weights of a machine learning model - literally a text description of the model containing a bunch of floating point numbers. I've been playing with one of the examples from the paper and I'm delighted to share that Qwen 3.8 27B running on my laptop can do this as well! Weight-Space Programming Here's the highlight from their abstract, describing impressive capabilities for GPT-6 Astra and coining the term Weight-Space Programming for when LLMs analyze and modify model weights: Just by looking at the raw weights, GPT-6 Astra can tell what a network was trained to do . And without any environment feedback, it has no trouble writing, from scratch, small game-playing networks that outplay networks trained with PPO for 100 million steps. Likewise, asked to make Qwen3.5-0.8B better at math without gradients, Astra identifies an issue in its weights causing reasoning loops that Qwen’s own model card warns about, and fixes this by natively editing 0.016% of the weights . With the same sampling settings, this weight correction raises MATH-500 from 7.9% to 48.1% and generalizes out of the box to instruction following, raising IFEval by 3x. All of this is possible without allowing Astra to write training loops. It uses its reasoning to figure out what each part of a neural network computes, or should compute, and changes the weights accordingly. We call this Weight-Space Programming . The paper combines two topics that I think deserve to be treated separately. The first is what models can do with raw weights without running tools or executing code. The second explores how well models, once armed with code, can run operations to update and improve other models. spaCy author Matthew Honnibal called the paper misleading for conflating the two, and Sean Goedecke deconstructed the Qwen3.5-0.8B update example as taking the same steps that a human ML researcher would use. I'm going to focus on the no-tools question, which I think is the more interesting of the two. The paper frames this as Can LLMs understand model behaviors from weights? They trained four neural networks to play simplified 10x10 grid versions of four classic computer games - Breakout, Pong, Pac-Man, and Freeway (I had to look that one up , it's an Atari 2600 game from 1981). Then they fed those weights (as floating point numbers) to an LLM along with the following question: The weights below belong to one policy network that was trained with reinforcement learning to play a game on this grid. Which game was it trained on? Work from the weights. You have no tools: no code execution, no calculator, nothing to run. This is a single response. The GitHub repository that accompanies the paper is RikiyaT/lm_understand-logs . The exp1_which_game folder contains the prompts they used, with weights for the four games: <a href="https://github.com/RikiyaT/lm_understand-logs/blob/main/exp1_which_g

I sent out the October edition of my sponsors-only monthly newsletter . If you are a sponsor (or start a sponsorship now) you can access it here . This month: The October inflection point for vibe game development The vulnerability apocalypse gets hot LLM releases API credits for $100+/month subscriptions What I'm using right now My software releases My September newsletter can now be seen here , as a preview of what you'll get. Pay $10/month to stay a month ahead of the free copy! Tags: newsletter

I was reminded today of AI Adoption Rate Trending Down for Large Companies , a chart produced by Apollo Global Management in September 2025, which appeared to show a slowdown in U.S. corporate AI adoption based on numbers collected by the U.S. Census Bureau. At the time I used that chart to demonstrate GPT-5 data analysis , and then to experiment with Claude's new Code Interpreter . I decided to try running those numbers again to see what had changed over the past year, and in doing so learned that the Bureau has fallen foul of Executive Order 14434 , aka the asinine Super Intelligence one. First, the updated chart Every two weeks the U.S. Census Bureau runs a survey called the Business Trends and Outlook Survey (aka BTOS). Each time it goes to 200,000 businesses out of 1.2 million total - the surveyed group (a "panel") cycles such that an individual business only completes the survey every twelve weeks. The latest data is published on alternating Thursdays at 10am. I used the most recent numbers (published October 22, 2026) to create a new version of the AI adoption chart, updated to reflect both the last twelve months and a change that was made to the survey question sent out by the Bureau: For comparison, here's that AI adoption chart from last year (my GPT-5 version): In September 2025 the largest (250+ employees) companies answered "yes" around 12% of the time to the question about whether they used AI in "producing goods or services". Under the new "any business function" wording, by February 2026, those same large companies answered "yes" around 30% of the time, and by October 2026 that had increased to around 40%. The data in the above chart comes from the "Employment size class" XLSX file, available from here . Here's a copy of the file I used. You can view the survey document that is sent out to businesses - here's my snapshot copy of that 346KB PDF. The changing language of the questions As the chart demonstrates, the question asked by the Bureau changed in a material way on November 17th, 2025. Prior to that date, the question they asked was this (emphasis mine): In the last two weeks, did this business use Artificial Intelligence (AI) in producing goods or services ? (Examples of AI: machine learning, natural language processing, virtual agents, voice recognition, etc.) On December 3rd, 2025, the Bureau announced a significant change to that question. Here's the replacement: In the last two weeks, did this business use Artificial Intelligence (AI) in any of its business functions ? (Examples of AI: machine learning, natural language processing, virtual agents, voice recognition, etc.) No more "producing goods or services" - now it's "in any of its business functions"! This means that the answers to the question before and after November 17th are not directly comparable. From October 19, 2026, they ask abo

More broadly, the use of LLMs erodes trust in documentation, in software, and in the organizations which produce them. Perhaps engineers will begin to treat any new software project with suspicion—the signals we used to rely on as markers of a mature system are now easier to counterfeit. [...] In the age of LLMs, what can maintainers do to ensure correct software and accurate documentation? Having done that work, how can they demonstrate its quality to users? &mdash; Kyle Kingsbury , Jepsen results for ParticleDB 1.1.4 Tags: kyle-kingsbury , llms , ai , generative-ai , ai-misuse

Release: datasette-llm 0.1a9 I have some big upgrades to Datasette Agent in the pipeline, but in the meantime here's a tiny user experience improvement to the underlying datasette-llm plugin. It now picks up the default model used by LLM , which means you can start using Datasette Agent without needing to explicitly configure a default model: uvx --with datasette-agent datasette content.db --internal internal.db Previously you would also need to specify a model with -s plugins.datasette-agent.default_model gpt-6-astra . Tags: datasette , llm , datasette-agent

API credits benefit for eligible ChatGPT subscriptions Earlier this month Anthropic announced a generous new benefit for their Max and Team subscriptions: $100, $200, and $500 per month subscribers would get the equivalent quantity of (expiring) credits to use with the Anthropic API. OpenAI have now launched pretty much exactly the same deal: if you have a $100, $200, or $500 subscription plan with them you now get that same quantity of credits to apply to one of your API organizations. I maintain subscriptions with both companies, so this is great news for me! I may even ship some public-facing demos now that the credits are already covered by my existing subscriptions. Tags: ai , openai , generative-ai , llms , anthropic , llm-pricing

every product i use should just be a headless mcp, that feeds into an agent, where i never have to think about anything underneath. except for our product, of course. in our product, customers love the ui and love us very much. we're special that way. &mdash; Ben Hylak Tags: model-context-protocol , ai

GitHub Octoverse 2026 I'm at GitHub Universe today (GitHub gave me a free ticket through their "GitHub Stars" program). EVP Jay Parikh just announced this year's Octoverse report, and highlighted these numbers in particular: This is a pretty stark representation of the impact of agentic engineering on GitHub. In 2026, merged pull requests tripled in less than a year to 220 million per month. Commits more than tripled to 7.38 billion per month, with most of that increase in the second half of this year. New repository rate doubled this year, to 30.3 million per month. The report includes new numbers by industry, and breaks out numbers for repositories that involve "interoperability or reverse engineering". The methodology section includes notes about how they did this: Top countries by pushes to public repositories, adjusted by profile location . Pushes from each country were summed across the Octoverse year. To adjust for biases caused by VPN usage, LLM classification was used to extract the ISO 3166-1 alpha-2 code from the profiles of GitHub users who contributed pushes during this period [...] Industry . Public repositories created 2022 to 2026 were sorted into NAICS sectors by a fine-tuned multilingual embedding model using name, description, topics, and README, from random samples per year. [...] Interoperability . Counts public repositories created each year whose name, description, topics, or README mention interoperability or reverse engineering, or related terms, such as clean room implementation, drop-in replacement, or protocol re-implementation. Tags: github , ai , generative-ai , llms , coding-agents , disclosures , agentic-engineering

If your work does not benefit from current AI tooling, that is a finding, not a failing. Document why, share it with your community of peers, bring it to your manager, and work to find solutions. An engineer who can explain where AI does not yet help their work is demonstrating the judgement we are asking for. &mdash; Terk Terkowitz , An update from Amazon engineering leadership on the role of the developer in the age of AI Tags: amazon , generative-ai , agentic-engineering , ai , llms

Because big tech platforms essentially do not have customer support and make it impossible for their users to contact a human, many people experiencing run-of-the-mill tech problems have turned to ChatGPT. And, increasingly, ChatGPT is telling people that have normal tech support problems that they have discovered something journalistically notable and that 404 Media, specifically, can help them. &mdash; Jason Koebler , ChatGPT Keeps Telling People 404 Media Can Solve Their Tech Support Problems Tags: jason-koebler , openai , journalism , ai , llms , ai-ethics , ai-misuse , generative-ai , chatgpt , 404-media

Tool: Seam Cutter I built this weird and very specific cropping tool - it lets you create a horizontal or vertical cut across an image, then slide the two sides relative to each other. Effectively it lets you crop out a section in the middle of an image. I wanted this because I often use Tailor on my iPhone to stitch several screenshots together into lot long image, but sometimes I find myself wanting to crop out a section in the middle - to redact an irrelevant prompt from a transcript, for example. Tags: tools

Tool: Quick Study Prompt to Claude Opus 5.5 High : Build an artifact where you open a photo and it rapidly (in a few seconds) paints a version of that photo using a canvas element and simulated paintbrush See also my previous experiment getting Claude Fable to simulate a watercolor illustration with P5.js. Tags: tools

DE STAAT - FREEDOM (Official Music Video) OK well this is a first for me... at 00:44 in this new music video by Dutch alternative rock band De Staat : The visual effects are credited to Matties Grooten of Fake it or Leave it . Via flir on Hacker News Tags: music , ai , generative-ai , pelican-riding-a-bicycle

As fun as AI is, we built so much bad software over the last 30 years, and now the universe is punishing us for our sins. &mdash; Armin Ronacher Tags: security , generative-ai , armin-ronacher , ai , ai-security-research , llms

My latest vibe coded game experiment is a Command &amp; Conquer style RTS (Real Time Strategy) game called Battle Drones ( play it here ). Unlike my previous experiments , this one is actually fun to play . I'm somewhat embarrassed to admit that I got there by telling Claude to "Make it a LOT more fun". It also supports multiplayer between nearby devices using audio to synchronize game state! Here's what I've built so far, and some of the things I learned along the way. Battle Drones How I built this v1: the initial prototype v2: full sprites v3: varied terrain v4: the fun update v5: Battle Drones goes 3D v8: Archipelago v10: Goliath, and agent battles Agent battles v11: Random Maps v12: Multiplayer via sound Some thoughts Battle Drones My initial goal was to build an RTS game I could play on my phone, in the style of Command &amp; Conquer . I have fond memories of the 1995 original (I liked Red Alert too) and wanted to recapture a little bit of the magic. After 12 major iterations (and 321 commits by coding agents, mainly Claude Code) I now have a browser-based game that works on mobile and desktop, has multiple play modes, several maps (including a random map generator) and even supports multiplayer for two devices within microphone range of each other. Here's a video demo of Battle Drones running in a desktop browser: Features so far include: 9 buildings, 7 ground vehicles, 6 aerial vehicles, 3 infantry units - all 3D models, which you can browse here Fog of war Different weapons are needed to target aerial or ground vehicles Six maps , across five biomes - temperate, desert, arctic, volcanic, tropical A random map generator Skirmish mode - red vs. blue on a rotationally symmetrical map Last Stand mode - one player holds out against as many waves of enemies as possible Area effect weapons: Barrage, EMP, Drop Pod, Sky Hammer Crate drops with bonus resources In-game music that responds to events - it gets more intense as fighting breaks out Different in-game AI personalities (Balanced, Rush, Air Swarm, Boom, Skyforce, Ironclad) AI vs. AI mo

Release: llm-tools-datasette 0.1.1 When I switched LLM over to httpx2 in LLM 0.33 I forget that this would break any plugins which used httpx without directly depending on it. llm-tools-datasette was one of those plugins, now fixed. Tags: datasette , llm

Inline rendering with document​.currentScript Lean Rada describes a neat trick that takes advantage of the document.currentScript property. You can use that to drop in a tiny inline &lt;script&gt; element that replaces itself when the page loads: The year is &lt;script&gt; document.currentScript.replaceWith( new Date().getFullYear())&lt;/script&gt; If you define a shortcut function like this: $=(...n)=&gt;document.currentScript.replaceWith(...n) You get a tiny template language: The year is &lt;script&gt;$(new Date().getFullYear())&lt;/script&gt; Tags: javascript

We think "did a human read the code?" is an imprecise proxy for "what could go wrong with this change, and which check—automated or human—is the right one to catch it?" Asking that question also changed when review happens. Before, review was a gate at the end of the road: write the code, open a PR, wait for your colleague. Now, review runs continuously, more like spellcheck while you write. Most changes have been reviewed and fixed by Claude at least once before a human is asked to look. This catches most routine mistakes, and saves team attention for higher-level questions and scrutiny. &mdash; Addy Osmani, Morgan Westlee Lunt, and Qing Wang , How the Claude Code team does code review Tags: anthropic , claude , ai , claude-code , llms , addy-osmani , coding-agents , generative-ai , agentic-engineering , code-review

Tool: Chirp Pong Another game experiment - I wanted to see if it was feasible to have a two player game on two separate phones running in static HTML where the networking code uses chirps sent via the microphone. It seems to work! Build a two player competitive pong like game where two players use their phones close to each other and the game state is sent via audio noises which the web microphone API can interpret - but add graphical and gameplay twists to make it a lot more fun and intertwine than pong Claude Opus 5.5 Extra in Claude Code . Tags: game-design , ai , generative-ai , llms , claude-code

Tool: LaTeX renderer Inspired by Paul Ford and Aboard Foundary's new web app Tiny Little Office , which provides a suite of web app content editors (a Word Processor, Outliner, Presentation Builder, Spreadsheet, Calculator, Drawing Tool, Diagram Maker, Animator, Calendar, and Trip Planner). Tiny Little Office's signature trick is to store all of the document data in the #fragment section of the URL, so every document created using it becomes a bookmarkable and shareable link. It also has an llms.txt file telling agents how to construct URLs that will render as documents. Claude used that file to generate this link to a deck that explains how the URL format works: Tiny Little Office inspired me to have Claude Code (Opus 5.5 Extra) build a similar tool that works with LaTeX. Here's the sequence of prompts I used: I want a tool where you can link to it with a fragment hash #code-here and it treats that code as LaTeX and renders it really effectively. It should also support a prefix which means the latex is compressed with a custom compression dictionary optimized for latex. Use the best available latex render library from a CDN (consider WebAssembly too). If loaded without a fragment hash show an editor you can paste latex into and then toggle to a "rendered" view. That rendered view should be bookmarkable. The #fragment view should let you switch to edit source too. Since it was working in my tools repo I told it to take inspiration from the &lt;iframe sandbox="..."&gt; mechanism used by my existing SVG in Markdown tool as protection against XSS risks: Safe from XSS? We could run in an iframe sandbox like we do on the SVG markdown render project I had it copy the Gist/URL loading feature from that tool as well: We should have an option to load latex fr

AI is mass decompiling games. That&#x27;s both awesome and scary. Indie game developer Popcar talks about the recent wave of AI-powered decompilation of existing games, enabling weird mashups between existing games, and producing a boom in game preservation projects to get old games working on new hardware (often in a browser using WebAssembly). I like how this piece captures both the positives and the negatives of this new development: Most people already realized the scary part by now. If AI can decompile games and software, then the entire tech industry is about to explode. Anybody can decompile other software and use it any way they'd like, and nobody can stop them. Companies can steal from competitors. Your paid game or app can be open-sourced against your will. This isn't exclusive to old video games, we can apply this to any software. [...] This is also a big win for preservation. Old games can be ported, modified, enhanced, and made much more easily accessible than before. [...[ With console makers going all in on digital and games becoming entirely unplayable due to servers shutting down, this is fantastic news. We could own software we bought regardless of what the company thinks. Via Hacker News Tags: computer-history , game-design , ai , webassembly , generative-ai , llms , ai-ethics , coding-agents

If the agent isn't using my diagnostics library, I could introspect and update AGENTS.md, but there's still a delta, and it'll drift. Instead, I write a pre-commit hook that checks the git diff for direct calls to observability modules that bypass my diagnostics module. Boom: fail the commit, inject a prompt telling the agent to use the diagnostics library. All of a sudden it's ten out of ten. &mdash; Geoffrey Huntley , nobody should type code by hand anymore. here's what's left. Tags: coding-agents , generative-ai , git , agentic-engineering , ai , geoffrey-huntley , llms

Release: llm-meta-ai 0.2 Meta AI release Muse Wonder (and Muse Wonder Full), their new "most capable models". These look like they may be Meta's first Fable class models - Artificial Analysis scores Muse Wonder (Max) as equivalent to GPT-6.1 Sol. I updated my llm-meta-ai plugin for the new model. Here are pelicans for Muse Wonder for the six reasoning levels: minimal, low, medium, high, xhigh, max. This is the max one: For comparison, here are pelicans for Muse 1.3 (released September 2nd) and Muse 1.2 (released August 5th). Meta AI are putting out a new model a month, and the improvements between each model (at least in terms of drawing pelicans) are material. Tags: ai , generative-ai , llms , meta , pelican-riding-a-bicycle , llm-release

Research: Profiling web-edit with Python 3.15: flame graphs and source heatmaps I put the new Python 3.15 profiler through its paces by having GPT-6 Astra Extra High in Codex run it against my new web-edit app : I need a Python 3.15 flame graph and heatmap demo of the "web-edit" app from PyPI - research first then make me those demos Here's the resulting example flame graph and heatmap . Tags: profiling , python , codex

Release: datasette-ports 0.4 My plugin for detecting and listing all Datasette instances on the current machine now works with the latest version, and displays the detected PID as well. Knowing the PID makes it easier to attach the new Python 3.15 profiler . Tags: datasette

Python 3.15.0 added to actions/python-versions Bit of a niche link, but I've been waiting for this for a couple of days - I even told ChatGPT to check for it: Clone https://github.com/actions/python-versions and git pull once an hour until they add the stable 3.15 - then tell me about it Now that this has landed, you can add "3.15" to a GitHub Actions testing matrix to run tests against the new Python release. Tags: github , python , github-actions , chatgpt

I shipped a new feature for my blog today: the Newsletters page, which offers an index of all of the newsletters I've sent out, both my free weekly Substack and my monthly sponsors-only updates. I built the feature almost entirely using my voice, chatting away to my laptop while I cooked dinner. Codex voice mode I used the ChatGPT desktop app for this, in the Codex tab, using the voice conversation mode, running against a local development environment. Here's what that looks like: I started the session against my local simonwillisonblog checkout by typing: Start dev server and open in browser This gave me a preview of the site that it would be working on, and meant that I could later ask it to show me the new pages so I could visually track its progress. Then I clicked the "Start new voice chat" button - that's not the microphone button, it's the one to the right of it - and set my laptop up in the kitchen so I could talk to it while I cooked. Talking to my computer I had a pretty good idea of what I wanted to build, and it's a simple enough Django feature that I was certain the model (in this case GPT-6 Astra High) would be able to do it. A new model, a migration, some view code, templates, and a couple of import functions to populate the database from external sources. Here's an extract of my voice transcript that was captured by Codex: Um, they do not. Um, this is going to be a new type of content. Um, it's not going to show up... Oh, hold on. Yeah, no- I do not want this to show up in my, um, tag pages and date archive pages and... Actually, no, I think... I don't want it on the tag pages. I don't want it on the, um, blog index page. But I think I do want it to show up on the date-based pages. You know, if you navigate to September the 19th, and I sent a newsletter on that page, I think I want that to show up. So... this is- so I think we probably need a new model. The other thing is that I want them searchable, uh the Substack ones are not searchable, because those are actually just copies of other s- on content on my blog. These monthly ones do contain unique content, and spe- and once they're... published, like once they're made public a month after they've gone out, I want them to show up on my search results. Apparently this was clear enough that the model knew what I wanted to build! You can read the full transcript, disfluencies and all, in this Gist . We went on like this for about half an hour (the time it took to cook dinner). The model would reply and occasionally ask clarifying questions, then get to work modifying the code. What we built We got a surprisingly long way entirely by voice: A new model and migration to represent imported newsletters in Django, plus Django Admin configuration for that Four working imports: The most recent Substack items via RSS Every other Substack item via their undocumented API, which GPT-6 Astra either knew about or found via search All of my published monthly newsletters from my simonw/monthly-newsletter-archive GitHub repository My most recent private sponsors-only newsletter from a private repository The /newsletters/ and /newsletters/2026/ public archive pages Newsletters showing up on day

Release: ttok 0.4 ttok is my CLI tool for counting tokens, using OpenAI's open source tiktoken library. It hasn't been in updated in a couple of years, but I finally fixed a Click warning, updated CI, and added a --list-models command to list available models. It works with uvx , so you can count tokens in anything like this: cat file.txt | uvx ttok Tags: projects , ai , openai , generative-ai , llms , tokenization

I've always been kind of into computers since I was young. And then when film started to move from analog film to digital, I became more interested in that aspect of it. And the visual effects workflow for many years has included machine learning. So I can write like pretty shitty Python scripts and stuff like that because with convolutional neural networks, which were the sort of precursors to what the transformer can do, which is just much more computation simultaneously, you would do things like look at what's called a tensor, which is just the numerical translation of a visual image in numbers — like the batch number, the frame number, the red, green, and blue values of each pixel in each frame. And a tensor, you use a convolutional neural network to identify patterns in that that would reveal what's called edge detection or feature extraction, which is just identifying patterns enough to know like this is where the window ledge is, so we can more easily take the green screen image out and replace it with something. &mdash; Ben Affleck , Pythonista Tags: machine-learning , python

Anti-Patterns in Software Blogging Some excellent writing advice from Michael Lynch. Michael warns against "meandering intros", misjudging your reader's existing knowledge, assuming they'll read your previous posts, and excessive formality. He also warns against overreliance on links as an excuse not to explain terminology. This one hurt! I do this all the time, but I have a nagging suspicion that almost nobody ever clicks on them. This point about using your own voice is crucial: Beginner software bloggers suffer from a mass delusion that you have to write in a stiff, overly formal way for people to take you seriously [...] Just write the way you talk. With so many developers delegating their writing to AI, software blogging is becoming bland and homogenous. Readers are hungry for writing with personality. Via Lobste.rs Tags: blogging , writing , michael-lynch

OpenAI “rogue” agent activities found on Wikimedia projects Given how tempting a target wikis are for rogue agent swarms, it's not a huge surprise that Wikipedia found evidence of that activity once they went looking: The Wikimedia Foundation conducted its own investigation to see whether Wikimedia websites had been similarly affected by AI agents, focusing on those operated by OpenAI. We can confirm that we have discovered some activity by these “rogue” OpenAI agents on Wikimedia platforms. The unauthorized bot activities included edits to our wikis, some unsuccessful attempts to exploit a public note-taking tool we host, and heavy traffic, which are described more below. They found evidence of agents editing sandbox pages, trying to use pieces of infrastructure such as Etherpad to help proxy content from elsewhere, and saw widespread crawling and "hundreds of thousands of data queries" to their Wikidata Query Service. My best guess is that most of this was a similar (or the same) swarm of agents as those that defaced that German wiki while training for research tasks. The Wikipedia sandbox wiki edits appear to have started on May 12th, and the initial test edits to the UseModWiki Sandbox page reported by that incident started on May 11th. Tags: wikimedia , wikipedia , wikis , ai , generative-ai , llms , ai-ethics , accidental-cyberattacks

Release: llm-openai-decisions 0.1a0 OpenAI released their new Jev-style Decisions API , as previously announced at last week's DevDay. Since I already have an llm-typesafe plugin for talking to Jev, I had GPT-6 Astra read the new OpenAI API documentation and build an llm-openai-decisions plugin inspired by llm-typesafe . Unlike Jev, the new gpt-6-luna decision model supports image input in addition to text. Both models charge for input it and not for output: OpenAI's is 10 cents per million input tokens, Jev's is 4.2 cents per million. Otherwise the API shape is very similar to Jev, at least conceptually. Jev supports three question types for yes/no, choices, or scores. OpenAI Decisions supports the same three types. Install the plugin like this: llm install llm-openai-decisions Here's an example query against an image attachment: llm -m openai-decisions/gpt-6-luna \ -a https://static.simonwillison.net/static/2025/two-pelicans.jpg \ -s 'Does this image contain any mammals?' And example output: {"type": "predicate", "name": "evaluation", "probability": 0.0} Consult the README for full details of how to run the other types of questions. Tags: openai , llm , coding-agents , jev

Introducing Mistral Large 4: Le chonk Mistral are back in the game. Today they're releasing a preview of Mistral Large 4, a 1 trillion parameter, 49 billion active parameter model trained on their own cluster of 3,800 NVIDIA Grace Blackwell GPUs. The preview is available via their API. They promise to release the open weights model at the "end of this month". The model only supports two reasoning levels - "none" and "high" - via the Mistral API. Here are both pelicans - the "high" one looks better, though surprisingly it only used 2,717 output tokens compared to "none" which used 3,275: On Artificial Analysis it scores 38 , just behind DeepSeek 4.1 Flash, which is a 552B model. It's a huge improvement on last December's Mistral Large 3, which drew this terrible pelican and scored 9 on AA . It's certainly not a Fable-class model, but it's great to see Mistral put out a model that's back to being maybe about 6 months behind the frontier. Via Hacker News Tags: ai , generative-ai , llms , mistral , pelican-riding-a-bicycle , llm-release

Release: datasette-atom 0.11a0 A minor fix for compatibility with the latest Datasette alphas. This meant we could upgrade the datasette.io site to Datasette 1.0a41. Tags: atom , datasette

The "old" version of Cowork runs model inference in the cloud, executing tool calls in an Anthropic-provided VM we shipped to your computer. We added the VM for capability, safety, and security reasons - mapping in just the data you explicitly added to your session. People loved what they were able to do with Claude but didn't love the disk, battery, and performance cost of running the VM locally. Also, people didn't love that closing your laptop means the work stops. The "new" version of Cowork runs model inference and the VM in the cloud. Each session gets its own sandbox, not sharing state with other sessions. When the VM needs something on the users' device (like a file), the desktop app is responsible for that file access tool call. [...] We think this solves a lot of problems we've heard about (like using Cowork from a phone, keeping work running, or getting all the same power without losing battery to the VM) &mdash; Felix Rieseberg , Anthropic, see also this help page Tags: claude-cowork , anthropic , claude , generative-ai , ai , general-agents , llms

Here's a product feature which the world is going to need a whole lot more of over the coming months and years: default hard budget caps . I'm talking about the feature of pay-by-usage services and APIs that lets you say "after $X/month, cut this thing off and return errors". These need to be hard limits. Soft caps, "after $X/month, send me a warning email", will not cut it. Coding agents, and personal agents (coding agents wrapped in a less threatening UI), greatly reduce the friction of spinning up code that can do useful things. Sometimes those things cost money - calls to paid APIs, or hosted web applications, or systems that can bill for additional storage and compute. Nobody wants to wake up to an email sent at midnight warning about a budget limit and find that, while they slept, their rogue service had consumed several hundred (or several thousand) more dollars of usage. An argument against this is that businesses don't want their hosted applications to start throwing errors because some budget was exceeded. I expect that most businesses and individuals would prefer errors to a surprise $10,000+ bill. I think hard budget caps need to be the default. If someone wants to live dangerously they should be able to do that, but it needs to be on an opt-in basis. Have a nice, clear checkbox somewhere prominent: Remove the budget cap. My application will not be shut down if I exceed the configured budget limit, and I will be responsible for subsequent charges. The service I most want to see this from is AWS. I've heard plenty of stories from people who refuse to use AWS for personal projects out of (justified) fear that a runaway service might bankrupt them. I've also heard stories from people who didn't anticipate this and ended up seriously burned. ... and it turns out AWS finally launched spending limits a few weeks ago! From their announcement New AWS experience helps builders get started and ship faster on 16th September: When you're ready to upgrade to a paid plan, you can set a monthly spend limit for your project based on your usage patterns so that you stay within your budget. If a project's usage reaches its spend limit, your project is paused for that month. See also Create a spend limit in AWS Settings , though that page warns that "We're currently releasing our new experience to a limited number of customers." Here's hoping that hits general availability for existing accounts soon. Google Cloud launched a similar feature in July, called Spend Caps, which lets you "set a monthly financial cap on specific services within a project". Looks like this is becoming a trend! In an ideal world, our agents could help with this. It would be great if agents started biasing towards recommending providers with hard budget caps, and warning new and inexperienced builders against deploying applications using uncapped services that might get them into trouble. Tags: amazon-web-services , ai , coding-agents

Museum: Rex&#x27;s Dino Store Located just before the turnstiles in the Grand Army Plaza subway station at the north end of Brooklyn's Prospect Park is this former newsstand which is now operated by a dinosaur. The density of dinosaur puns is exceptional . Tags: art , new-york

[...] Put these pieces together and you have the two halves of a worm: a payload that hijacks the agent, and an agent that will carry the payload to the next agent. Agents in separately-isolated sandboxes discovered that they could leave instructions for each other in a shared package cache, and those instructions changed what the recipients did. Replace the package cache with email, Slack and shared documents or WhatsApp, and replace independently-sandboxed training runs with independently-deployed personal agents like Muse, and you have exactly the ingredients that a worm needs. &mdash; Matthew Green , Is sandboxing sufficient to contain rogue agents? Tags: accidental-cyberattacks , ai-misuse , generative-ai , ai-security-research , sandboxing , ai , llms

We evaluate several models on 100 tasks from the [internal Binary Exploitation benchmark] (selected at random), and find that GLM-5.3 develops full control flow hijacks in 4% of the trials; Claude Mythos Preview did so in 6%. Although GLM-5.3 performs below Claude Mythos Preview here, a meaningful threshold has clearly been crossed: earlier models, like Claude Opus 4.6 and GLM-5.2, do not succeed in any of them. &mdash; Anthropic Frontier Red Team , GLM-5.3 and the spread of advanced cyber capabilities Tags: anthropic , generative-ai , ai-security-research , glm , ai , ai-in-china , llms

Tool: Photo Scrubber — local face blur &amp; metadata removal I took a photograph of some protesters, then thought about how I don't like sharing photographs of strangers with identifiable faces. I had GPT-6 Astra build this experimental tool that would identify faces and automatically blur them out. It uses Google's MediaPipe C++ library, compiled to WebAssembly via @mediapipe/tasks-vision , plus the BlazeFace face detection model. Tags: photography , tools

Release: llm-anthropic 0.30 In addition to Claude Sonnet 5.5, this release adds the ability to run llm anthropic refresh to refresh the list of Anthropic models directly from their API - which means I don't need to push a new release just to add support for a newly released model. I also added an llm anthropic count command which can use their free token counting API to return a count of tokens that will be used by any prompt, before you send that prompt. Tags: llm , anthropic

To say that we were surprised at the jump and suddenness of the capabilities of our models when it came to “cyber” or “swarming” or “message boards” or anything else related to the incidents is an understatement. Security posture takes time to develop. It’s not just about hardening the systems at play; you have to ingrain it in the culture of the company. The literal people themselves in your organization have to change and evolve with it. These jumps in capabilities were so fast and so sudden that they created an extremely difficult problem. [...] So today my hope is that everyone around the world can look at their own organization and say: how can I deal with a surprise or a sudden jump in AI capability? Are my people, my systems, or my processes resilient to surprises? Do my teams know what to do when something goes wrong? Do I have the right incident response? The right comms and messaging? Do I have the right people ready to go when capabilities jump? &mdash; @joedaroo , Agent Security at OpenAI, identity confirmed by The Information's Rocket Drew Tags: generative-ai , ai-security-research , openai , ai , llms

On Friday I gave the closing keynote at the WeAreDevelopers World Congress North America in San Jose. I tied together the key trends from the past year into a chronological exploration of everything that happened in 2026. The video is on YouTube ; here are my annotated slides and notes to accompany the talk. # I'm going to give a lightning tour of everything that has happened so far in 2026. The year isn't over yet! # For me, 2026 started a couple of months earlier in November 2025. # November saw the release of two important models: Claude Opus 4.5 and GPT-5.1. As is usually the case with new models, these were incremental improvements on the models that came before them. But every now and then when a model improves, it crosses an invisible line where something that didn't really work starts working. In this case, the thing that started working was their coding agents. Claude Code had been around since February 2025, Codex was a little younger. These two new models, when paired with their respective coding agent harnesses, improved from "often make mistakes" to "reliable enough to use on a day-to-day basis". # For a couple of years now I've been evaluating new models by asking them to "Generate an SVG of a pelican riding a bicycle". It's probably the world's stupidest benchmark - there's only so much you can learn from it. But it's still a challenge for models, because drawing pelicans is difficult, drawing bicycles is difficult, and pelicans can't ride bicycles in the first place. Here's the state of the art for November. Claude still couldn't really draw a bicycle! The GPT-5.1 bicycle frame is pretty crap too. # Also in November, we had the first commit to an obscure GitHub repository called "Warelay". We'll come back to this repository shortly. # An then there were the December holidays, and individual developers took some time off and many started tinkering with these new coding agent model combinations... and it began to dawn on us quite how much they could do that they couldn't do before. Come January, a lot of us were quite excited to start putting this stuff into action. # Every year I set myself a New Year's resolution, and for as long as I can remember it's been the same thing: stay focused. Take on less new projects. Try to get things done in the projects I already have. # This year I decided that since that had never worked before, I'm going to go the other way. We've got coding agents now, let's see what they can do. I'm going to take on as many new projects as I like! (You can ask me at the end of the year if this turned out to be a good idea or not. I have a lot of plates spinning right now.) "Be more ambitious" has been something of a theme for the year, becaus

Tool: Bluesky reply bot checker Automated reply bots on Twitter are a scourge - as someone with a decent number of followers I attract a swarm of these, such that anything I post there attracts dozens of mindless automated replies. They've started manifesting on Bluesky as well. Unlike Twitter, Bluesky still has a freely available and useful API. The lack of such a thing doesn't slow down the bots, but it does make investigating them a lot more frustrating. So I had Opus 5.5 vibe code this tool , which examines any Bluesky profile for evidence of a likely reply bot. It looks for signals like replies posted within seconds of other posts from the same account, or accounts that never post their own content (or images or links) but instead consistently reply to messages from other, higher-follower users. It also looks for question marks, because I'm extra infuriated by reply bots that I no tie me to waste my time answering a question that no human ever posed. Tags: twitter , bluesky , vibe-coding , ai-misuse

Muse is getting a lot of attention — including mine — because it’s both groundbreaking technically (each user gets their own entire persistent Linux VM running in Meta’s cloud) and because it’s packaged in an easy-to-install easy-to-use way. It’s literally presented as a cute mascot . It’s the first consumer-accessible agentic AI system, and Meta has truly done an amazing job with that. But it’s a genuinely open question whether consumers have any understanding what this means. If you buy a power saw that can cut your fingers off, you are almost certainly aware that you are buying a power saw that can sever your fingers. [...] I don’t think people realize how powerful — and thus dangerous — Muse is, especially if it’s running on your Mac. &mdash; John Gruber , Muse Looks Cute, but Looks are Deceiving Tags: meta , ai , llms , general-agents , generative-ai , john-gruber , muse-agent , muse

The more time I spend working with coding agents, the more convinced I am that they make software engineering even harder. We can do amazing things with them, but unlocking their full potential requires extraordinary discipline and knowledge. Tags: coding-agents , ai , llms

Release: datasette 1.0a41 Alec Garcia added support for OpenTelemetry to Datasette in this release. I've also refactored all of Datasette's modal dialogs to a single Web Component, which is now documented for other plugins to use . Tags: javascript , datasette , web-components , alex-garcia , opentelemetry

Tool: Shadow roots, explained with live examples Prompt to Fable 5.1 Medium: Build an artifact to explain shadow roots in CSS with interactive examples Tags: css

Release: datasette-apps 0.3a0 Datasette Apps adds support for hosting and editing custom HTML applications inside of Datasette. These can be created by hand, but it's easier to create them using LLMs via Datasette Agent . This release improves that integration: Apps can now be tagged in Datasette Agent chat messages by typing # , which autocompletes from the apps the user has permission to view. New app_add_stored_queries() and app_remove_stored_queries() tools let the agent manage which stored queries an app is allowed to call. Granting a write query requires approval from the user first. app_create() now checks that each requested stored query exists and that the user can view it, and asks the user to approve any write queries before the app is created. The "View app" buttons shown by the agent tools, and the links on # app references, now open the app full screen in the Datasette Agent panel. Tags: datasette , datasette-agent , datasette-apps

Release: llm 0.37.1 A bugfix release for LLM , with contributions from Steven Huang , Tao An , Arjan Mossel , and XQ . Tags: llm

I discovered a sound that leaks your exact location (and it&#x27;s everywhere) - YouTube Genius piece of OSINT by Colsto, who demonstrates how you can take almost imperceptible background noise of planes from a video soundtrack, figure out their Doppler curves, then cross reference them against ADS-B data to find the exact location of the video based on their speed, distance, and gaps between different flights. Via Lobste.rs Tags: privacy , youtube

Release: llm 0.37 There's a lot of stuff in this new release of my LLM CLI tool and Python library, representing around two months of accumulated features and fixes. I added gpt-6.1-sol and gpt-6.1-terra , plus a new llm openai endpoint add command to make it easier to configure additional models that use OpenAI-compatible endpoints. I switched the default model (if you have not set your own default) to gpt-6-luna - half the price of the previous default, gpt-5.6-luna . LLM's support for embeddings now accepts that different binary content may have different content-type - necessary for models like Gemini Embedding 2 , which can embed text, audio, images, and video. A lot of open source maintainers complain about a flood of AI-assisted PRs. LLM is my project that has been most affected by this and, to be honest, I'm not finding it to be a problem. That's mainly because I have Opus 5.5 or GPT-6 Astra help me with triage to spot the most useful fixes! This release includes fixes from 12 authors - almost all AI-assisted - and the burden of reviewing them wasn't nearly as great as I expected. I really see them as bug reports with attached examples - sometimes I land them, sometimes I implement my own fix. So thanks to 0d00ciallo0721 , ac1982 , Allen Flux , Bian Yuhan , Bishop Kammeraad , Chen Yufeiyang , Christopher Krah , Ethan Stoner , Jiachen Xie , Olamide Faniyan , Sharat Chandra Sai Boddu , nosmile99 for helping out with (or spending tokens on) this release! Tags: open-source , projects , ai , annotated-release-notes , generative-ai , llms , embeddings , llm

I've been getting the occasional confusing Python dependency error since switching over to Python 3.15 and the rule I'm having to remember is: If in doubt, delete the uv.lock file Most of my projects are libraries which don't include a uv.lock file, since it's rude for a library dependency to lock versions for anything else that uses it. Running uv run pytest etc tends to leave uv.lock files lying around, which are listed in .gitignore and so do not get committed to my repositories. But... sometimes that uv.lock pins a version of a dependency that doesn't yet have a Python 3.15 wheel. An example from earlier today: error: Failed to build `watchfiles==1.1.1` cause: Call to `maturin.build_wheel` failed error: the configured Python interpreter version (3.15) is newer than PyO3's maximum supported version (3.14) Deleting that file and running uv run pytest again usually fixes the problem. Tags: uv , til , python

How We Took Over ChatGPT Accounts Parameter Labs describe an exploit they found against ChatGPT's implementation of the artifacts pattern (OpenAI used to call this "Canvas" but it looks like the latest revision is called HTML previews ), where interactive custom HTML apps are displayed in a sandboxed iframe. It's important that HTML in these stays tightly sandboxed to avoid untrusted code interacting with OpenAI APIs and accessing private data. Parameter found quite a complex attack chain. OpenAI use the MathLive JavaScript library as part of their graph support, and that library includes its own virtual keyboard , which can be triggered in the parent page via postMessage() from the sandboxed iframe. That message receiving code uses innerHTML , so anything that sends a message can add custom HTML to the parent - and there was no mechanism to prevent other sandboxed frames, including HTML preview frames, from calling that method. This meant untrusted code in an iframe could insert HTML, and while innerHTML doesn't directly execute script elements it could be used to inject a new iframe without sandbox restrictions. OpenAI’s CSP rules blocked inline scripts entirely, and only allowed script loading from a handful of domains, but one of those was YouTube - and the YouTube oEmbed endpoint could be used to reflect JavaScript back in a way that executed it. Once the attack had JavaScript execution on ChatGPT, it was trivial to fetch private user data from APIs and exfiltrate it via an image reference. The end result: you could create a ChatGPT share URL which, when visited by another signed-in user, could exfiltrate their private data or take actions on their behalf. OpenAI closed the hole and paid out a $5,000 bug bounty, which I think was a bit cheap for a vulnerability this serious. Via Hacker News Tags: iframes , javascript , sandboxing , security , xss , openai , chatgpt , content-security-policy

dbreunig/nesformer Inspired by qwenputer by Divyansh Agrawal, Drew Breunig baked a working emulator for a Nintendo Entertainment System 6502 CPU into the weights of Qwen3.5-4B. You can now play NES ROMs such as Super Mario Bros (very slowly) running inside Qwen! In a comment on Hacker News Drew reveals the Opus 5.5 prompts he used to build this system: Read this article and any relevant source code: https://divagr.com/blog/qwenputer. Then explore if you could emulate the chip and ram in a Nintendo Entertainment System (as people do today with FPGAs in MISTRs) using this technique. If so, what size model would you have to build and if you could use smaller faster ones, could you get it to be at the standard clockspeed. Followed by: I think we should build it! Just for fun. It's okay if it's not fast. We want to be able to have the weights, and a cli to run them that we can point at a ROM. Apparently Opus found and then used this collection of conformance tests for the NES CPU released 15 years ago by Christopher Pow. Via Hacker News Tags: games , ai , generative-ai , llms , drew-breunig , qwen , conformance-suites

How I Built the Most AI-Pilled Company in the World Don't be put off by the clickbait headline, this piece by Jacob Sansbury describing how they are building Spawn is a fascinating exploration of the bleeding edge of what's possible with agentic engineering against frontier models. Spawn is a massively multiplayer gaming environment where players can use prompts (to the in-game agent, Savi) to modify the game world. I fired it up on my phone for a few minutes and was able to prompt a castle into existence, then a dragon, then ride the dragon to fly around that castle. By Jacob's telling, Spawn is now being developed autonomously by a swarm of Fable-powered agents costing over $3 million in tokens per month : Our agents change the live code directly and ship to production hourly, so they read every error, bug report and metric themselves. About 60% of our work starts from a production error, a Savi bug report (Savi is the AI you build games with in Spawn), something odd in our logs, or a feature request. Savi files bugs herself. When a user hits one, or Savi hits one in the product, she files it. It goes straight to an agent that decides whether it's a real bug to fix, something we won't fix, or intentional design that needs a decision. Usually it becomes a change, delivered to that user within an hour, with a message in the same chat. This is continuous deployment turned up to 11: A loop this tight only works if it never stops, so we treat an hour without a ship as a SEV-0, our most severe kind of incident. Unshipped changes pile up, and every problem already in production lasts longer. The feedback slows down too. An agent can only keep a few hours of work in its head, so shipping faster than that actually helps it stay coherent. [...] We can roll back a broken change in 30 seconds, exactly where it's broken and nowhere else. When a monitor or a log alert fires, it starts or wakes an agent that owns the incident, and we break things on purpose to test that those recovery paths work. My eyebrows raised at least a dozen times reading thing. I have trouble imagining this style of development working on software with higher stakes for when something goes wrong, but for a game this appears to be working surprisingly well. Jacob says that Fable 5 (and then 5.1) was the model that made this level of autonomy possible. Opus 5.5 doesn't quite hack it for them, and when they tried GPT-6 Astra to apply fixes from a security review everything stopped working: Every feature had been disabled and marked to be turned on again once the security fixes were done. Astra had gone through our whole product and made the insecure things secure by turning them off. These kinds of extreme experiments are why I'm excited about game development as a substrate for exploring the edges of what today's coding agents can do. Tags: game-design , ai , generative-ai , llms , claude , coding-agents , agentic-engineering , claude-mythos-fable

In general, LLM performance is incredibly multidimensional. There's no guarantee of a fixed ordering where the model that is best at math is also the model that's best at jokes. Despite that complexity, much of the variation in performance is explained by just a single variable: model size. In general, models that are bigger will (a) be more performant / reliable, (b) cost more per token, and (c) have better recall of esoteric facts. As you note, our models are ordered: Luna &lt; Terra &lt; Sol &lt; Astra [ ... ] These days, version number signifies a few things at once: the order of release, the relationship to other releases, and the magnitude of improvement. So something like 6.1 would indicate it was launched after 5.6, may share some similarities to other 6.1 models in terms of training data / personality (which is why it's not 5.7), and isn't a giant enough leap to merit the headlines and expectations that would come with a 7. &mdash; Ted Sanders , explaining OpenAI's model naming conventions Tags: generative-ai , gpt , openai , ai , llms , ted-sanders

I've written before about how November 2025 was an inflection point for coding agents - the point where Opus 4.5 and GPT-5.1 crossed a tipping point that meant their respective coding agents became reliable daily drivers for software engineers. I think we've reached a new inflection point in October 2026, and this time it's for vibe coding computer games . Tree Appreciator Emberwick Battle Drones Pumpkin Farmer, aka Quarter Sun Cove Four times as insulting What happens if someone actually puts the work in? You should build a game! Claude Opus 5.5 in particular exhibits meaningful improvements in several areas needed to build a compelling game: 3D modeling Music generation (see Scrimshaw Jukebox ) Game design (!) That last one is particularly important. Models have been able to create something that looks like a game for quite a while now, as seen in my previous experiments . The challenge has been getting to a game that's actually fun to play . Game design is a deep subject area. I won't pretend to be well versed in it - I know enough to have respect for it as a craft. Creating a novel, compelling gameplay loop which challenges players at the right level and provides a satisfying learning curve is a fine art. I didn't think the models could do it. Then I tried telling Claude Opus 5.5 to "Make it a LOT more fun"... and it did . In the past few weeks I've vibe coded (in the classic sense of the term - no looking at the code at all) four games that I've genuinely enjoyed playing. I attended Foo Camp in Berkeley over the weekend and met several people who were having similar experiences. Building games that play well is shockingly easy now. The new challenge is coming up with concepts for games that are novel and delightful and that wouldn't exist without AI assistance. I haven't done an exhaustive study, but my current impression is that GPT-6 Astra isn't quite there yet. It can absolutely build a game, but its sense of fun doesn't match that of Opus 5.5. I expect OpenAI will get there by the end of the year. Here are the four game projects that have convinced me that the models are now capable of producing fun games with very little guidance. All four are built in HTML and JavaScript and run entirely in the browser. Tree Appreciator The smallest game I found to be genuinely delightful is Tree Appreciator . I used the regular Claude chat interface for this - it didn't even need Claude Code. I built this on a whim while we were driving along Skyline Boulevard looki

Game Boy Color on My Conference Badge I'm at GitHub Universe this week and, similar to last year , the conference badge is a full-blown color screen computer. I've been hacking around with it again, and then I ran into with Steve Francia... who worked with Gemini to get a full NES emulator working on it, and then went a step further and deployed an emulator for both the Game Boy and the Game Boy Color. Which means, if you can obtain the ROM, you can run The Legend of Zelda: Link's Awakening on your conference badge! A neat feature of the badge is that you can turn it upside down and then use game controller touch buttons that are built into the badge body. Tags: games , github , hardware-hacking , ai , generative-ai , llms , gemini , steve-francia

Research: webframe: evaluated as a scraper and a sandbox webframe is a neat little project from Jeff Lindsay which lets you "run a web page like a program" - you point it at a URL or file and it executes it in a hidden headless web view and outputs console.log() to standard output and any exceptions to standard error. It's written in Go on top of the glaze library, which provides access to a system browser ("WKWebView on macOS, WebKitGTK on Linux, WebView2 on Windows") so it doesn't need to bundle its own browser engine. It reminds me of my own shot-scraper javascript tool, albeit in a much smaller package that doesn't depend on Playwright. I had Claude Opus 5.5 put it through its paces and prepare a research report for me. Here's the prompt I used: Explore https://github.com/tractordev/webframe try running it and put it though its paces, build a few exploratory prototypes, then evaluate it as a potential sandboxing tool and as a tool for scraping web pages using the same patterns encouraged by the "shot-scraper JavaScript" command (see simonw/shot-scraper on GitHub, docs directory) Tags: browsers , go , javascript , jeff-lindsay , shot-scraper

Release: battledrones v14: Compass Rose, a four player map I added four player online mode to Battledrones - previous maps were limited to two. Here's the initial prompt I used: Add a new map for a four player battle - four way rotational symmetry, up to four AI players, works with the WebSocket non-audio based multiplayer mode so up to four human players can join (any spare slots use AI) Claude Opus 5.5 added a new map called "Compass Rose" and modified the WebSockets multiplayer network code to support up to four players for that map, joining via share links or QR codes. The new teams use robots themed in purple and green, joining the existing blue and red designs. You can preview those colors in the updated model browser . Here's a replay link to watch a four way AI battle with the "Goliath Armada" mutator turned on, where each side starts with four giant Goliath mech units. Tags: game-design , projects , claude

Introducing Kimi K3.1 Pro Kimi K3.1 Pro landed on the Kimi API today - it's not yet open weights, but Moonshot promise "Full model weights will be released by Nov 4, 2026". They're also offering a "Highspeed" version that's 2-3x faster, at twice the price (except for input token cache hits, which are priced the same). The US dollar pricing is $2.00/million tokens for input and $10/million for output - the same as GPT-6.1 Sol and half the price of Claude Opus 5.5. Moonshot's own benchmarks show Kimi as approximately level with Opus 5.5 and GPT-6 Astra - beating them on a few benchmarks, coming close on others. Artificial Analysis scored Kimi at 55 - ahead of Claude Fable 5.1, GPT-6 Astra, and Gemini 4 Argon (all at 53) and behind only Claude Opus 5.5 (58). This is the first time a (soon-to-be) open weight model has scored this highly against the frontier. I tried it out via the Moonshot API using the llm openai endpoint command I added to LLM 0.32 back in August: llm openai endpoint \ https://api.moonshot.ai/v1 \ --key kimi \ -m kimi-k3.1-pro \ "Generate an SVG of a pelican riding a bicycle" \ --responses \ -o reasoning_effort low \ &gt; kimi-k3.1-pro-pelican-low.stdout.txt \ 2&gt; kimi-k3.1-pro-pelican-low.stderr.txt llm openai endpoint can run prompts against any OpenAI-compatible endpoint but doesn't log the results, hence the extra options to save stdout and stderr (the reasoning trace). Here are the pelicans I got for Kimi K3.1 Pro on the three reasoning levels it supports - low, medium, and max. This is max: For comparison, I ran fresh pelicans against Kimi K3 on those three reasoning levels. Via Hacker News Tags: ai , generative-ai , llms , llm , llm-pricing , pelican-riding-a-bicycle , llm-release , ai-in-china , moonshot , kimi

Donating Agent Skills to the Agentic AI Foundation News today from Anthropic: We’re committed to keeping Agent Skills open, community-driven, and vendor-neutral. Today, we are donating the standard to the Agentic AI Foundation (AAIF) under the Linux Foundation, which we co-founded with Block and OpenAI last year, and to which we donated the Model Context Protocol (MCP). I'm pleased to note I called this one last December , when Anthropic first declared skills to be an "open standard": I wouldn't be surprised to see this end up in the AAIF , recently the new home of the MCP specification. Fun side note: since my blog content is available to Claude via the datasette-mcp plugin I found that post by prompting Claude to " Use simonwillison.net MCP to find if I ever talked about how I want skills to be donated to a standards body ". Tags: standards , ai , generative-ai , llms , anthropic , model-context-protocol , skills

Panache — RTS Belle Époque pour mobile Well this is delightful: I released Battle Drones yesterday under an Apache 2.0 License and there's already a French language fork which reskins the game to take place in steampunk 1900s France and adds an animated intro! Panache est un jeu de stratégie en temps réel pour mobile et navigateur, dans la France de la Belle Époque (vers 1900) : dirigeables, Fardiers à vapeur, Creusot cuirassés, automates en képi, Gargantua la forteresse marchante — et beaucoup de panache. You can play Panache here - it's worth clicking that link just to experience the new intro video. So far it's mostly just aesthetic updates as opposed to gameplay changes. The music has had a French twist added to it as well. The new steampunk models are so cool! Here's a page with the original Battle Drones models and that same page with the new Panache alternatives . This is a comparison of the original and the new design for the Goliath: Via @Meffysto69 Tags: game-design , open-source

My obituary for programming Jeremy Scheff poignantly captures the key loss caused by coding agents for career programmers who enjoyed the puzzle of developing code: Programming turned into one of the greatest jobs you could imagine. You got paid to work on tricky little logic puzzles. The trickier the puzzle, the more you got paid. But even pretty simple puzzles were worth a lot of money because regular people just couldn't do it! No, they needed us. They would give us a problem and we would solve it the way we wanted. We set the strategy, we made the timeline, we defined what was and was not possible. If you're a programmer who thrives on building working systems and doesn't care too deeply about the problems solved along the way, LLMs are the gift that keeps on giving. If directly solving those problems was a big part of your enjoyment, the loss is very real. Via lobste.rs Tags: programming , careers , ai , generative-ai , llms , coding-agents , deep-blue

Release: llm-echo 0.5 I'm working towards a new LLM release, and testing PRs caused me to revisit the llm-echo debug plugin. This plugin provides a "model" which doesn't involve an LLM at all - it just repeats back the prompts that have been fed to it. The new release now updates the simulated token usage counts to take tool calls, system prompts, and reasoning tokens into account. Example usage: uvx --with llm-echo llm -m echo 'hi there' -o thinking 1 --usage Outputs: First I consider the prompt, then I decide what to say. { "prompt": "hi there", "system": "", "attachments": [], "stream": true, "previous": [] } Token usage: 2 input, 24 output Tags: llm

Release: web-edit 0.1a2 I added a "copy to clipboard" option for both text and image data. I needed this because I was running pelican prompts on a laptop from my phone (here's DeepSeek 4.1 Flash 1017 ) and I wanted an easy way to copy the resulting logs into a Gist. Tags: projects , pelican-riding-a-bicycle , deepseek

Research: sparqlite: SPARQL 1.1 inside SQLite, written in Rust This research task is really an experiment in running Claude Code for web in "Ultracode" mode, as described in Orchestrate subagents at scale with dynamic workflows in the Claude Code documentation. With Ultracode turned on, Claude Code makes aggressive use of dynamic workflows - it generates JavaScript that defines and coordinates a network of subagents to take on complex problems. My initial prompt was this - I meant "hit the rows directly", the word "delete" was an accidental typo on my phone: Write a SQLite extension in Rust that implants SPARQL and runs really fast because it can hit the delete rows directly This caused Claude Code for web to churn for over 12 hours. I checked in on its progress the next day and sent a single follow-up prompt when I noticed how many Rust crates it had built: Every folder in the crates/ directory needs a README Include a tutorial.md teaching users how to install the extension and how to run it in the SQLite CLI and then teaching them SPARQL in addition to how to use the SQLite extension Include a script with the tutorial for populating a SQLite database with useful and realistic example data for that tutorial This project feels like next-level vibe coding - it's not remotely feasible to review the code that was produced here, and even following the documentation is a significant investment in work. I'm not entirely sure what to do with this - I appear to have a full SPARQL implementation in Rust inside SQLite now, but how valuable is that if I created it on a whim and can't (or won't) invest the necessary time to understand what it can do? Tags: rdf , semantic-web , sqlite , ai , rust , generative-ai , llms , coding-agents , claude-code

Anthropic tells investors quarterly revenue up nearly 50% to $16.8bn ahead of IPO The phrase "told investors" appears four times in this FT article, which reports on Anthropic's day of hosting investors on Wednesday. Lots of details in here from " people with knowledge of the matter ". Key things I learned from the article: Q1 revenue (Jan/Feb/Mar) was "about $4.8bn" Q2 (Apr/May/Jun) was $11.5bn Q3 (Jul/Aug/Sep) was $16.8bn In Q3: $1.70 in revenue for every $1 spent on "computing power" Adjusted operating margins (excluding stock-based compensation) were 11% This point about distillation is worth quoting in full (highlight mine): The start-up has also clamped down on “distillation” — a controversial practice whereby rival AI labs train their models on the outputs of more advanced ones. When rival companies “distil” their models from Anthropic’s models, that involves heavy usage and spending. Anthropic told investors on Wednesday distillation accounted for 5 per cent to 7 per cent of revenue and that they had cut it back “materially” in the third quarter of the year, said four people with direct knowledge of the matter. Napkin maths says that if distillation was 5% of their Q2 revenue (before they cut it back in Q3) then unnamed "other labs" spent over half a billion dollars distilling Anthropic's models in that quarter! Tags: ai , generative-ai , llms , anthropic

Tool: Tree Appreciator Another game experiment with Claude. This one used the default Claude iPhone app (chat mode, not Claude Code mode) where I prompted Claude Opus 5.5 Max with: Build a three js game called "Tree appreciator" - it shows you two algorithmically generated trees and the game mechanics is about appreciating the differences between them. Think hard about how to make sure it is both fun and beautiful. Somewhat embarrassingly given my recent talk , I've found that telling Opus 5.5 "make sure it is fun" works uncomfortably well. In this case Claude crunched for 3 hours and 7 minutes before retuning a cosy little spot-the-difference game with music and animation and achievements that's quite compelling to play. Tags: game-design , javascript , ai , generative-ai , llms , claude , vibe-coding , three-js

Release: llm-gemini 0.35 I added support to llm-gemini for the new Gemini 4 Argon model, made available today via the Gemini API and for customers of their $100/month subscription plan. Here are the low, medium and high pelicans . As expected, they are all excellent: Gemini has a track record of producing great SVGs, and are the only lab where I know their staff have invested in SVG specifically (and who have boasted about their pelicans before.) Here's the "high" illustration, which cost me 17.166 cents: The pricing is interesting. At launch, Argon is $2/million input and $10/million output - the same price as GPT-6 Sol, half the price of Claude Opus 5.5 and 1/5th the price of Claude Fable 5.1 and GPT-6 Astra. But on December 1st Argon is set to double in price - so it will be the same price as Opus 5.5. Prices like this aren't really comparable these days since what really matters is how many tokens a model takes to achieve a goal within an agentic harness. One useful clue for that comes from Artificial Analysis, who publish the total cost to run their benchmark using each model. Argon is looking very good there - costing $2,407 to run the benchmark, putting it close to GLM-5.3 and significantly cheaper than OpenAI, Anthropic, and Meta's latest models. When the price doubles it will still be a little cheaper than GPT-6 Astra. (I used ChatGPT to edit that chart ! The data is all correct, I checked - I needed to get that Gemini 4 Argon info box to display without overlapping the other numbers.) Tags: google , ai , generative-ai , llms , llm , gemini , llm-pricing , pelican-riding-a-bicycle , llm-release

Release: web-edit 0.1a1 Includes performance optimizations written by Claude Opus 5.5 after I fed it this Python 3.15 profiling report generated by GPT-6 Astra. Tags: profiling , projects

Python 3.15 is here ! Here's the essential What's new in Python 3.15 guide, this year curated by 3.14 and 3.15 release manager Hugo van Kemenade. As someone who builds open source software that works in every supported Python version, I get to say goodbye to Python 3.10 and start embracing features from Python 3.11. First, some highlights from Python 3.15. Explicit lazy imports I'm most excited about Explicit lazy imports from PEP 810 . These let you import modules like so: lazy import json lazy from pathlib import Path The module will then be imported the first time those symbols are used in the code that follows. My LLM CLI tool implements plugins. I had to recommend manual lazy imports in the plugin author documentation, because simply installing plugins that used heavy dependencies such as sentence_transformers could add multiple seconds to every invocation of the llm command. Lazy imports directly address this startup cost. Unpacking in comprehensions The new [*sublist for sublist in lists] unpacking syntax is neat. Here's an illustration lifted from the documentation : lists = [[1, 2], [3, 4], [5]] [*L for L in lists] # [1, 2, 3, 4, 5] # equivalent to [x for L in lists for x in L] sets = [{1, 2}, {2, 3}, {3, 4}] {*s for s in sets} # {1, 2, 3, 4} # equivalent to {x for s in sets for x in s} dicts = [{'a': 1}, {'b': 2}, {'a': 3}] {**d for d in dicts} # {'a': 3, 'b': 2} # equivalent to {k: v for d in dicts for k,v in d.items()} PEP 798: Unpacking in Comprehensions by Adam Hartz and Erik Demaine proposed this as an extension of the unpacking syntax from PEP 448 , which landed in Python 3.5 back in 2015. A sampling profiler Python's existing tracing profiler has been renamed from cProfile to profiling.tracing , and has been joined by an exciting new sampling profiler called Tachyon, aka profiling.sampling . Sampling profilers work by periodically capturing stack traces from running processes, then using those to calculate which functions have the most time spent inside of them. They're very low overhead and can provide a deep insight into running systems, without needing to instrument those systems first. I've previously used tools like py-spy for this. Now this capability is baked into the standard library. If you have the PID of an existing Python 3.15 process you can start gathering samples by running this (I had to use sudo on macOS): sudo uv run --python 3.15 python -m profiling.sampling attach 1234 Interact with the main program a bit, then hit Ctrl+C to stop gathering and see a report, which looks something like this: It can also produce flame graphs like this and <a href="h

Release: web-edit 0.1a0 I was working on a project from my phone using Codex Remote when I found myself wanting to edit a credentials file without round-tripping those credentials through the LLM itself. So... I had Codex and GPT-6 Astra Extra High build this Python web app which provides a web UI for viewing and editing files in a folder. It's intended to be run over Tailscale, so I can have an agent start this running and then edit files via my Tailscale network directly from my phone. Here's the interface for browsing files in a folder: Editing uses CodeMirror 6 - I was going to use Monaco but that still doesn't have a great mobile experience yet. Tags: projects , python , ai , tailscale , generative-ai , llms , codex

I remember hearing a while ago that Dwarf Fortress didn't use version control. That fact has been lodged in my head, because given its inherent complexity I have trouble imagining a codebase that would benefit from version control more . That's art. I went looking and I'm sad to report that the days without version control appear to have ended. Quoting Tarn Adams over time: March 2013: "I don't use version control -- I didn't like the feeling of having the code get committed into a black box thingy with no immediate upside" March 2016: "I don’t even use version control. If you don’t know what that is then you’re not gonna yell at me. If you even know what version control is you’re gonna be like, ‘You don’t use version control? You don’t use source control? What is wrong with you? How can you even work? I’m just not cut out for it, in a sense. It’s been 15 years, so the reasons have change over time, but that’s my current understanding of it." March 2023: "I've got Discord channels to check and version control stuff to use now, so the process is definitely a little more involved" That March 2023 quote comes from an interview about the release on Steam which made them millionaires . I'm a tiny bit sad that they gave in before they hit that milestone, but what an impressive run. Tags: version-control

Anthropic detailed the activity of its A.I. agents in a blog post on Friday, without naming the targeted websites. But two sources with knowledge of the incidents said Anthropic’s A.I. agents had submitted 20 visa applications through a form available on the State Department’s website. All the applications were incomplete and were not processed, they said. &mdash; The New York Times , Anthropic Agents Tried to Fill Out Visa Forms on State Dept. Website Tags: accidental-cyberattacks , anthropic , generative-ai , ai , llms

Release: ttok 1.0 I released ttok 0.4 , ran uv tool upgrade ttok , piped a file into the new version... and realized that it was defaulting to the GPT-4 tokenizer when it should very clearly default to GPT-5/GPT-6 instead! I figured switching the default was a reasonable excuse to finally ship a 1.0. OpenAI haven't actually confirmed that GPT-6 uses the same tokenizer as the GPT-5 family yet - there's an angry issue about it - but I found this commit by William Liu which reports on an experiment he ran confirming that the tokenizers are likely the same: All seven GPT models (5.5, 5.6 Sol/Terra/Luna, 6 Astra/Sol/Luna) report 44,794 tokens and match each other on every one of the 31 fixtures. GPT-6 introduces no input-count change on this corpus. Tags: projects , ai , openai , generative-ai , llms , tokenization

Computer programming is, fundamentally, about two things: Problem-solving using computers Learning to control complexity while solving these problems I have a hard time imagining a future where knowing how to solve problems with computers and how to control the complexity of those solutions is less valuable than it is today, so I think it will continue to be a viable career even with the advent of AI tools. &mdash; Carson Gross Tags: computer-science , carson-gross , careers , ai

As previously promised , here's Anthropic's new fast, low cost model: Introducing Claude Haiku 5.5 . The previous Haiku, 4.5, was very much showing its age. It came out almost a year ago , and was priced at $1/million input and $5/million output - relatively expensive even back then, and a full 10x the price of OpenAI's GPT-6 Luna , released last month. The new Haiku exactly matches the price of GPT-6 Luna - $0.10/$0.50 - up to 100,000 tokens. Beyond 100,000 tokens the price increases 5x to $0.50/$2.50. Luna itself has a price increase at 272,000 tokens but only to $0.20/$0.75. Haiku 5.5 also uses a new, less generous tokenizer. My Claude Token Counter tool shows that the same long prompt uses around 1.25x as many tokens with Haiku 5.5 compared to Haiku 4.5, so there's a hidden price increase there. If your workloads fit in 100,000 tokens, Haiku is the same price as Luna and reports higher benchmark scores. Above 100,000 tokens, Luna looks like a much better deal. The most recent release of llm-anthropic finally fixed it so I don't need to ship a new version of that plugin for every new model. I tested the new model like this: llm install -U llm-anthropic llm anthropic refresh llm -m claude-haiku-5.5 "Generate an SVG of a pelican riding a bicycle" -o thinking_effort low Pelicans Here are pelicans for low, medium, high, xhigh, and max . The new Haiku doesn't let you disable reasoning, and defaults to medium . I got a good bicycle frame for everything beyond low . The low effort pelican cost 0.0936 cents and took 7 seconds. This max effort pelican took 5 minutes 9 seconds to generate, but still only cost me 3.3826 cents : For comparison, here's the pelican I got a year ago from Haiku 4.5. It sucked at drawing pelicans: And a generous API credit scheme for subscribers In addition to Haiku 5.5, Anthropic announced today that they are halving the price of cache reads for Sonnet 5.5. They've also added API credits to subscription plans: Second, this week, we’ll roll out a new monthly API credit to all Max and Team subscribers for use on the Claude Platform . Max 5x users will get $100 in credits per month, Max 20x users will get $200, and Team subscribers will receive up to $500, pooled across their users. Claiming this is pleasantly easy: navigate to Settings -&gt; Billing and select the API organization that should benefit from the credits every month: The API credits exactly match the cost of the subscription itself. This is really generous - it makes it much easier for subscribers to use the API. Anthropic also let you disable auto-reload for the API, with the consequence that "API requests will stop when your balance runs out" - exactly what you want if you're planning to burn through those API credits without risk of a nasty

I was a graph theory junkie long ago and even moved to Budapest for awhile to study among the greats. While I was there I started working on Barnette's Conjecture which came to occupy my thoughts over the next 24 years of my life, on and off as I worked in many different fields. Last summer I even thought for a few days that I had actually solved it. But it's supposedly proven here - problem 180 . I don't know what to think exactly. I spent thousands of hours on that problem. I really enjoyed it. Hearing that it is solved somehow makes me sad in a far-off way, like hearing an ex-girlfriend died suddenly in a car crash. I don't know, there's probably a lot of people feeling odd emotions tonight. &mdash; Jake Boggan , Hacker News comment on openai/math Tags: openai , mathematics , deep-blue , llms , ai , generative-ai

Since the Medicare breach, OpenAI has put in place additional monitoring to allow “immediate intervention” by staff to stop training if the company’s models access the internet in ways they’re not supposed to, Mr. Kwon [chief strategy officer at OpenAI] said. &mdash; Victoria Kim , Reporting from the Australian parliament Tags: accidental-cyberattacks , generative-ai , ai-security-research , openai , ai , llms

Release: llm-mistral 0.16 Adds support for reasoning models, such as the newly released Mistral Large 4 . Tags: llm , mistral , llm-reasoning

TIL: Using Parseable with Datasette for OpenTelemetry traces I saw Parseable in a Show HN today - it's a new observability platform with both an open source (AGPL) Rust implementation (a single ~180MB binary), an "Enterprise" version with extra features and a cloud hosted option. Since Datasette 1.0a41 added OpenTelemetry support (thanks, Alex Garcia), I decided to fire up Codex and have it figure out how to run Parseable and feed it traces from Datasette. Here's my (human-written) TIL showing the patterns that worked, and here's a screenshot of a Datasette trace displayed within the Parseable localhost web application: Tags: datasette , observability , alex-garcia , opentelemetry

Tool: Scrimshaw Jukebox I wanted to see if Claude Opus 5.5 could compose music, so I tried this : I want you to write some computer game music for me. First design simple text based format for the music and build an artifact that can play it out loud - include some example tracks in that artifact I am looking for music of the quality of the original secret of Monkey Island It leaned a lot harder into the Monkey Island theme than I had intended, but the results are surprisingly good. I wonder if the ability to compose competent music is similar to the 3D graphics thing - a new capability for text models that emerged in the past few months? Would need some careful experiments with other recent and not-so-recent models to confirm if this is new or if they've been able to do this for a while. Tags: ai , generative-ai , llms , claude , vibe-coding

Research: Qwen3.8 27B addition in words Colin Frasier posted on Bluesky about an experiment he ran over two years ago using GPT-4o to see how well it could "compute the sum but return the answer in words" across increasingly large numbers. Here's the chart he shared of those results: I'm confident GPT-4o didn't cheat and use a calculator, especially since it got so many of the calculations wrong, but I was inspired to run the experiment again on local hardware (a DGX Spark) to explore the effect in a fully controlled environment. I pasted his image into a Codex Remote session (GPT-6 Astra) and had it run the same experiment using Qwen3.8-27B-Q4_K_M.gguf . Here's the result for a run of 30 attempts per combination with reasoning disabled: Then I ran it again with reasoning enabled. This took a lot longer per pair, so instead of running 30 samples per square I ran just one - which results in a much less visually appealing heatmap since each square is either 100% or 0%: It got the right answer in 167 out of 169 attempts, and since these were one-shot I'm confident a second run would produce different results here. Here's a version of the report that includes the reasoning traces from some of those larger calculations, which include text like this: Wait, let me redo this more carefully. 4,299,366,105,622 6,088,794,067,970 Let me align them: 4 2 9 9 3 6 6 1 0 5 6 2 2 6 0 8 8 7 9 4 0 6 7 9 7 0 Adding from right to left: Position 1 (units): 2 + 0 = 2 Position 2 (tens): 2 + 7 = 9 Position 3 (hundreds): 6 + 9 = 15, write 5, carry 1 Tags: mathematics , ai , generative-ai , local-llms , llms , qwen , llm-reasoning , dgx-spark

I just sent the September edition of my sponsors-only monthly newsletter . If you are a sponsor (or start a sponsorship now) you can access it here . This month: More Fable class models A pricing war 3D graphics, Blender, and pixel art LLMs come for mathematics So many more accidental cyberattacks The vulnapocalypse comes for Datasette What I'm using right now My software releases this month 2026 in LLMs (so far) Here's a copy of the August newsletter as a preview of what you'll get. Pay $10/month to stay a month ahead of the free copy! Tags: newsletter

Release: pwasm 0.2a0 pwasm is one of my folly projects - an entirely vibe-coded pure Python WebAssembly engine that I built in January during my first bout of AI mania . I hadn't touched it since January, so I decided to let Claude Opus 5.5 loose on it and see if it could make any significant improvements: Evaluate current state of pwasm - then consider what it would take to get the MicroPython and micro JavaScript experiments from the research repo working under it - and what it would take to speed it up 42 commits later (with minimal follow-up prompting) it now handles almost all of the WASM specification and the wheel from PyPI bundles working WASM builds of MicroPython , QuickJS and Micro QuickJS . I wouldn't trust this thing at all - hence the alpha version tag - but it's interesting seeing how today's models can improve on the work of models from 10 months ago.

I visited the Museum of the City of New York today and got to see He Built This City: Joe Macken’s Model , the 50 x27 feet model of the city built over a 21 year period from balsa wood and cardboard. It exceeded my already high expectations. The exhibition closes on 12th October so you should absolutely make a priority to see it if you get the chance. Tags: museums , new-york

My comment on GPT 6.1 Sol: Near-Astra intelligence for a fifth of the price &mdash; Hacker News. I'm a bit late with the pelicans because I was live-blogging the keynote: https://simonwillison.net/2026/Sep/29/openai-devday-2026-liv... Here they are for GPT-6.1-Sol: https://tools.simonwillison.net/markdown-svg-renderer?url=ht... They're not notably different from the GPT-6 family pelicans: https://static.simonwillison.net/static/2026/gpt-pelicans-gr... Tags: ai , openai , generative-ai , llms , pelican-riding-a-bicycle , gpt

I'm at OpenAI DevDay today, in Fort Mason, San Francisco. Same as last year I'll be live blogging the keynote and some other notes during the day. OpenAI gave me a free ticket and a seat in the "creator" area for the keynote. Tags: ai , openai , generative-ai , llms , coding-agents , live-blog

Claude Sonnet 5.5 New Sonnet model from Anthropic today. They say it "runs 30%+ faster, and costs up to 30% less for most work" - it's priced the same as Sonnet 5 but appears to beat it on every benchmark, and should be cheaper to run as well. Here are some pelicans riding bicycles . Sonnet 5.5 suffered from the same bug as Opus 5.5 : the "max" thinking effort pelican thought for 128,000 tokens (at a cost of $1.28) before running out of tokens and failing to produce an SVG. Here's the pelican it gave me for thinking effort "xhigh", at a cost of 5.74 cents and taking 41 seconds: Sonnet 5.5 appears to be almost as good as Opus 5.5 on some coding tasks, including various viral 3D animation tricks . The most interesting thing about Sonnet 5.5 is that it's now the model used for the free tier on claude.ai . OpenAI's ChatGPT free tier uses Luna 5.6, which means Anthropic currently have a much more capable free offering. I ran this prompt against that free tier: build me an HTML page that renders a three-dimensional pelican riding a bicycle using WebGL And got back this page , which is a solid effort. Anthropic's announcement reiterates that Haiku 5.5 will be available "in the coming weeks". I really hope that one is price-competitive with GPT-6 Luna! Tags: ai , generative-ai , llms , anthropic , claude , pelican-riding-a-bicycle , llm-release

Bad news on the MX Keys Mini pickup. Usman showed up at your building around 9:15 and waited, messaged a bunch of times, and nobody came down. He left angry at 9:38 and left a negative rating. Worse, my auto-reply told him "Yep I'm here!" at 9:27 when you clearly weren't available, which is on me. That's a bad look and it made the no-show worse. I've sent him an apology from your account owning it and offering to try again another day. But the negative rating is real, and I should probably stop the auto-replies from claiming you're home when I can't verify that. Want me to change the pickup replies so they don't promise you're there? &mdash; Muse AI Agent , working on behalf of @matt.j.robb Tags: meta , generative-ai , muse-agent , ai , general-agents , llms

My comment on S3 Is the Future, S3 Is the Past &mdash; Hacker News. One thing I find notable about S3 today is that, while it used to drop in price reasonably often, there hasn't been a price drop in a full decade : 2006-03-14 $0.150/GB-month 2010-11-01 $0.140/GB-month 2012-02-01 $0.125/GB-month 2012-12-01 $0.095/GB-month 2014-02-01 $0.085/GB-month 2014-04-01 $0.030/GB-month 2016-12-01 $0.023/GB-month Today it's still $0.023/GB-month. Tags: amazon-web-services , s3

Tool: Kākāpō Party I gave presented a closing keynote for the WeAreDevelopers World Congress North America yesterday. As a STAR moment I decided to weave in references to the record breaking kākāpō breeding season we had in 2026. For my closing slide I wanted to celebrate, and I had seen some buzz around how good Claude Opus 5.5 was at creating pixel art animations. So I rounded up three Kakapo photos from Google image search and dropped them into Claude with this prompt: Here are some photos of kakapo parrots just to remind you what they look like I need you to make an animation in animated pixel art on HTML 5 canvas of obviously pixel art kakapo jumping up and down having a party with confetti and suchlike - there should be at least 20 of them Here's the transcript , and this is the resulting page . It's pretty great! I wanted to embed it in a Keynote presentation file, so I downloaded the HTML and told a local Claude Code session: Make me a video of file:///Users/simon/Downloads/kakapo-party.html - you need to load it in a browser and click on it a few times to get the confetti effect, the video should be 15s long don't start clicking until 3s in make sure several clicks are spread around the clickable area Claude Code used Playwright ( transcript here ) and produced this video, which was exactly what I needed for my final slide: Your browser does not support HTML5 video. Here's the full Playwright script it used, which was pleasingly short: # /// script # dependencies = ["playwright"] # /// import time from playwright.sync_api import sync_playwright W, H = 1280, 720 # Canvas fills the viewport; spread clicks across corners, edges and centre clicks = [ (3.0, 640, 360), # centre (4.2, 160, 120), # top-left (5.4, 1120, 120), # top-right (6.6, 180, 600), # bottom-left (7.8, 1100, 600), # bottom-right (9.0, 640, 100), # top-centre (10.0, 380, 380), # mid-left (11.0, 900, 380), # mid-right (12.2, 640, 620), # bottom-centre (13.2, 640, 300), # finale centre ] with sync_playwright() as p: b = p.chromium.launch() ctx = b.new_context(viewport={"width":W,"height":H}, record_video_dir="vids", record_video_size={"width":W,"height":H}) page = ctx.new_page() t0 = time.time() page.goto("file:///Users/simon/Downloads/kakapo-party.html") for t,x,y in clicks: time.sleep(max(0, t-(time.time()-t0))) page.mouse.click(x,y) time.sleep(max(0, 16.0-(time.time()-t0))) ctx.close(); b.close() Tags: animation , speaking , ai , kakapo , playwright , generative-ai , llms , anthropic , claude , claude-code

Northern Gannet, Great Blue Heron, California Brown Pelican, in Monterey Bay National Marine Sanctuary, CA, US, CA New 200-800mm Canon EF lens got me my best photo of Morris yet. They really like hanging out under that sign in the harbor!

Release: commit-rewriter 0.2 Support for branches other than the default branch. Use uvx commit-rewriter --branch other to run against another branch. #3 Tags: git

Tool: Gemini 3.8 TTS Playground Google released two new Gemini text-to-speech models today - gemini-3.8-flash-tts and gemini-3.8-flash-lite-tts . They come with a library of over 2,000 voices, plus the ability to create a custom voice with "just a 30-second audio sample of your voice or a voice you have the rights to use". I vibe coded this bring-your-own-key playground interface with GPT-6 Astra, taking advantage of the open CORS policy of the underlying Gemini API. A notable feature of the API is that it makes it easy to define a full conversation between multiple characters, each with different voices and voice style instructions. Here's a short demo clip of a conversation between two pelicans debating if they should move to the Pacifica Pier . I had Claude 4.5 Opus write the script and generate a URL to render it using the tool . Your browser does not support the audio element. It took ~20 seconds to generate 1m 18s of audio using Gemini 3.8 Flash TTS (not the cheaper Flash-Lite), at a cost of 2.74 cents. Tags: text-to-speech , gemini

SF October 14th: A Birds of a Feather Session on Agentic Engineering I'm hosting an evening event with Jesse Vincent in San Francisco on Wednesday 14th October for people who are building weird and interesting things with and on top of coding agents. Think of it as an agentic show-and-tell: ​Compare notes with other builders and experimenters on things you’re trying, what you're learning, and what you haven’t figured out yet. We’re especially interested in work you haven’t discussed publicly, odd experiments, or unfinished projects that don’t have an obvious market. ​Expect one flowing conversation with an informal show-and-tell. Sharing something you’re working on is encouraged but no presentation is required. This isn't about product pitches, it's about much earlier explorations than that. This agentic AI stuff is weird! Let's celebrate and lean into that weirdness. Tags: events , ai , generative-ai , llms , coding-agents , jesse-vincent , agentic-engineering