<p><strong><a href="https://openrouter.ai/deepseek/deepseek-v4-pro-0813">DeepSeek V4 Pro 0813 (on OpenRouter)</a></strong></p>The latest DeepSeek Pro model is now available, via API only. I had to link to OpenRouter because DeepSeek don't have any obvious announcement page for their new model.</p><p>I haven't been able to confirm if they plan to release the open weights, but given the weights are available for both April's <a href="https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro">deepseek-ai/DeepSeek-V4-Pro</a> and July's <a href="https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731">deepseek-ai/DeepSeek-V4-Flash-0731</a> it seems likely.</p><p>Interestingly I got <a href="https://tools.simonwillison.net/markdown-svg-renderer#url=https%3A%2F%2Fgist.github.com%2Fsimonw%2Fc1108a380593547c2def5863bca63160"><em>very</em> different looking pelicans</a> for the three different reasoning levels of low, medium, and high. I've not noticed this kind of difference from any other model:</

<p><strong>Release:</strong> <a href="https://github.com/simonw/alchemy-utils/releases/tag/0.1a0">alchemy-utils 0.1a0</a></p> <p>I've long pondered what a database agnostic version of my <a href="https://sqlite-utils.datasette.io/">sqlite-utils</a> Python library and CLI utility might look like. This morning (literally a shower project) I tasked Codex and GPT-5.6 Sol Ultra with building a prototype:</p><blockquote><p><code>Do a research spike to see what it would take to build a library with the same core API as SQLite-utils - in particular the insert and upsert and insert_all and upsert_all and create and update methods, and the table introspection stuff - but backed by SQLalchemy so it works for multiple database engines</code></p><p><code>Test against PostgreSQL and SQLite and duckdb</code></p><p><code>Use ~/dev/sqlite-utils for reference</code></p><p><code>Create a git repo for this and commit and early and often - use uv init to start the project - use red/green TDD and pytest, s

<blockquote cite="https://blog.florianherrengt.com/ai-removing-middle-class-software-engineering.html"><p>But then users start to report a weird bug. It's the 4th time your team has been trying to fix it. I mean... asking AI to fix it. Unfortunately, it seems like not even Fable can figure it out.</p><p>You go talk to the person who worked on this feature.</p><p>"So where does the data come from?"</p><p>"Hmm... actually I don't know. Let me ask Claude."</p><p>You sit next to each other watching an endless wall of text appear on the screen. Neither of you has any idea whether any of it is true but Claude seems very confident. [...]</p><p>This project has become so convoluted, with so many layers and services, that no one on your team could possibly start to understand what's going on.</p></blockquote><p class="cite">&mdash; <a href="https://blog.florianherrengt.com/ai-removing-middle-class-software-engineering.html">Florian Herrengt</a>, AI is removing the middle class of software engi

<p><strong><a href="https://sophiebits.com/2026/06/25/there-are-no-lossless-transformations-of-natural-language-text">There are no lossless transformations of natural-language text</a></strong></p>Sophie Alpert shares her "internal policy on acceptable use of AI writing by engineers". It's a short read (supporting its own recommendations) and really good.</p><p>If you chose to have LLMs help massage your writing the following rule seems crucial to me:</p><blockquote><p><strong>You must stand behind every idea and every sentence in your docs</strong>. It is your responsibility to make sure that the entire document is representative of your own thoughts before you share it. If a reviewer asks, “What did you mean by this line?”, it’s not acceptable to reply with “Oh sorry, AI wrote that, just ignore it.” You will confuse your readers (and waste their time) if you present them things that are not genuinely representative of your thoughts.</p></blockquote><p>The "no lossless transformation

<p><strong><a href="https://stolen-thoughts.com/">Stealing Reasoning Traces from Proprietary LLM APIs</a></strong></p>A vanity domain name (<code>stolen-thoughts.com</code>) for <a href="https://www.alphaxiv.org/abs/2608.09867">a neat paper</a>:</p><blockquote><p>Anthropic, OpenAI, and Google return encrypted chain-of-thought blocks to clients that can be replayed across sessions, users, and models. We take a trace produced by a frontier model, replay it into a weaker sibling, jailbreak the weaker model, and recover the stronger model’s hidden reasoning in plaintext</p></blockquote><p>You can see an example of these encrypted blocks by running:</p><div class="highlight highlight-source-shell"><pre>curl https://api.openai.com/v1/responses \ -H <span class="pl-s"><span class="pl-pds">"</span>Content-Type: application/json<span class="pl-pds">"</span></span> \ -H <span class="pl-s"><span class="pl-pds">"</span>Authorization: Bearer <span class="pl-s"><span class="pl-pds">$(</span>llm key

<p><strong>Release:</strong> <a href="https://github.com/simonw/datasette-upload-dbs/releases/tag/0.5a0">datasette-upload-dbs 0.5a0</a></p> <p>This plugin has been around for a while - it lets users upload a brand new SQLite database to a hosted Datasette instance, at which point that database will start being served by that instance.</p><p>It can also be used to atomically swap a database with a more recent version. The uploaded database is saved to a file, verified, then swapped in so <code>/name</code> starts serving the new one.</p><p>The new release adds a formalized API, so you can replace an existing database (or add a new one) like this:</p><pre><code>curl -X POST \ -H "Authorization: Bearer $API_TOKEN" \ -H "Accept: application/json" \ -F "db=@content.db" \ -F "db_name=content" \ https://your-instance.example.com/-/upload-dbs</code></pre><p>This means you can build fresh databases in an environment such as GitHub Actions and swap them in production as soon as that build has c

<p><strong><a href="https://research.meta.ai/blog/introducing-muse-glimmer-open-agentic-model">Introducing Muse Glimmer</a></strong></p>Meta are back in the open weights game! Muse Glimmer is a brand new 30B model under a clean Apache 2.0 license (a step up from the janky Llama licenses of old).</p><p>They claim to have optimized it for exactly the kind of things I'm looking for in a local model:</p><blockquote><ul><li><strong>End-to-end Agentic Task Completion.</strong> Muse Glimmer achieves strong success rates on full-task benchmarks including DeepSearch QA, MCP-Atlas, 𝛕-Bench and SWE-Bench, which measure its ability to work within scaffolds, write and debug code, and resolve multi-turn requests from start to finish.</li><li><strong>Reliable Tool Use.</strong> The model handles a wide range of function calls, invoking tools with precise schemas throughout extended workflows.</li><li><strong>Multi-Step Reasoning.</strong> Muse Glimmer chains reasoning over long horizons, sustaining...

<blockquote cite="https://www.abc.net.au/news/2026-08-10/ai-assistant-hacks-gym-website-aus-cyber-attack/107007986"><p>The API has zero authorisations checks on cancelling other people's reservations … I tested this with the person in waitlist position #1 — and it actually went through. So you've moved from #4 to #3 already.</p></blockquote><p class="cite">&mdash; <a href="https://www.abc.net.au/news/2026-08-10/ai-assistant-hacks-gym-website-aus-cyber-attack/107007986">OpenClaw (running Opus 4.6)</a>, hacking an Australian gym-booking website</p> <p>Tags: <a href="https://simonwillison.net/tags/ai-ethics">ai-ethics</a>, <a href="https://simonwillison.net/tags/generative-ai">generative-ai</a>, <a href="https://simonwillison.net/tags/openclaw">openclaw</a>, <a href="https://simonwillison.net/tags/ai">ai</a>, <a href="https://simonwillison.net/tags/ai-security-research">ai-security-research</a>, <a href="https://simonwillison.net/tags/llms">llms</a></p>

<blockquote cite="https://platform.claude.com/docs/en/release-notes/system-prompts#claude-opus-5"><p>Claude Fable 5 and Claude Mythos 5 were first released on June 9, 2026. On June 12, 2026, Anthropic suspended access to both models to comply with U.S. Department of Commerce export controls; the Department lifted those controls on June 30, 2026, and Anthropic restored access on July 1, 2026 (Anthropic's statement: <a href="https://www.anthropic.com/news/fable-mythos-access">https://www.anthropic.com/news/fable-mythos-access</a>). These events are after Claude's training-data cutoff, so Claude knows about them only from this notice. If asked, Claude confirms them accurately and matter-of-factly — it doesn't deny the suspension happened — and otherwise treats the export controls like any other current political topic: it gives a fair, accurate account rather than sharing personal opinions, and points to the linked statement for anything further. Things may have developed since this noti

<p><strong><a href="https://github.blog/changelog/2026-07-30-github-models-is-now-retired/">GitHub Models is now retired</a></strong></p>I missed this news until today, when the GitHub Actions run for my <a href="https://github.com/simonw/research">simonw/research</a> repository failed with this error message:</p><blockquote><p>GitHub Models is temporarily unavailable as part of a scheduled retirement brownout.</p></blockquote><p>That message is already stale, because the retirement has been completed.</p><p>GitHub Models was an odd-shaped duck. GitHub provided a model playground tool and a unified API across a bunch of different LLM providers, with the biggest benefit being that code running in GitHub Actions could use the GitHub API key already present in that environment to execute prompts.</p><p>This made it easy to build things that fit GitHub Next's <a href="https://githubnext.com/projects/continuous-ai/">Continuous AI</a> concept.</p><p>GitHub didn't share the reason behind the

<p><strong>Research:</strong> <a href="https://github.com/simonw/research/tree/main/sqlite-text-history-prototype#readme">SQLite compressed text-history prototypes</a></p> <p>I'm perennially interested in options for storing revision histories in relational databases. While out on a dog walk I had a new idea: how about taking the full text of every prior version in a big JSON array of strings and then applying zlib or zstd compression to the whole thing? Surely that would compress really well due to all of the repeated strings.</p><p>The new <a href="https://openai.com/index/introducing-gpt-live/">GPT‑Live voice mode</a> in the ChatGPT iPhone app has got really good, so I discussed the prototype with that. You still can't share URLs to voice conversations, but here's what I said copied from the transcript as a proper stream of consciousness:</p><blockquote><p>I have an interesting idea for a scheme for saving all previous versions of a piece of text that's constantly edited in a SQLit

<p><strong><a href="https://claude.com/blog/auto-mode-default-in-claude-code">Auto mode is now the default in Claude Code for Pro, Max, and Team plans</a></strong></p>Anthropic are <em>really</em> confident in Claude Code's <a href="https://code.claude.com/docs/en/auto-mode-config">auto mode</a>, to the point that they are making it the default setting for new sessions in most Claude Code plans starting on August 14th.</p><p>This was one of the topics discussed in <a href="https://simonwillison.net/2026/Jul/21/cat-and-thariq/">our Fireside Chat</a> with Cat Wu and Thariq Shihipar at the AI Engineer World’s Fair last month. I asked them how they run Claude Code safely within Anthropic (given the threat of prompt injection) and <a href="https://simonwillison.net/2026/Jul/21/cat-and-thariq/#what-s-the-advice-within-anthropic-for-safely-running-claude-code-">they replied</a> that "Broadly within Anthropic, almost every single person uses auto mode". Cat Wu then said:</p><blockquote><p>We’

<p><a href="https://news.ycombinator.com/item?id=49220609#49221745">My comment</a> on <a href="https://news.ycombinator.com/item?id=49220609">Now we have a timeline of the OpenAI accidental attack against Hugging Face</a> &mdash; Hacker News.</p><p>I think one of the most interesting details here might be tucked away in that first bulletin point:</p><blockquote><p>May 7: OpenAI starts a new training run for an experimental, unreleased model. <em>(Do they mean an evaluation run? They say training run in the video, and later mention a “reward signal to judge how well they’re doing”, so I guess this really was about training a model, not evaluating one that was already trained.)</em></p></blockquote><p>The more I think about this the more I suspect that the fact this happened while <em>training</em> a new model is key to understanding what went wrong.</p><p>In RLVR - Reinforcement Learning with Verifiable Rewards - you set the model a goal and have it take <em>any steps necessary</em> to

<blockquote cite="https://daringfireball.net/linked/2026/08/07/simon-willison-on-blogging"><p>Me, I try to get into the mindset of playing live music, not recording a studio album. Except when I’m writing a piece where I really want it to be an album. Those aren’t <em>rare</em>, per se, but they’re <em>occasional</em>. If I tried to make every post a hall-of-famer I’d never get anything out.</p><p>I’m aiming for professionalism. I’m performing live in front of an audience — not just jamming in my garage or bedroom, fucking around. So I’m careful and concentrate. I want to hit every note, in time. But at my best I’m moving from song to song.</p></blockquote><p class="cite">&mdash; <a href="https://daringfireball.net/linked/2026/08/07/simon-willison-on-blogging">John Gruber</a>, responding to my <a href="https://simonwillison.net/2026/Aug/6/simon-willison-on-technical-blogging/">blogging tips</a></p> <p>Tags: <a href="https://simonwillison.net/tags/john-gruber">john-gruber</a>, <a href=

<p>OpenAI gave a last-minute presentation at the Black Hat security on Wednesday about "the Hugging Face Incident" (<a href="https://simonwillison.net/2026/Jul/22/openai-cyberattack/">previously</a> on this blog). <a href="https://www.youtube.com/watch?v=87DyyMV0kCY">The video</a> was published yesterday. It's short and information dense and well worth watching, in particular because it provides full details of what happened and how things played out inside OpenAI. I've used the video to construct the timeline below.</p><p><lite-youtube videoid="87DyyMV0kCY" js-api="js-api" title="Black Hat USA 2026: The 'Breaking' News: The OpenAI–Hugging Face Incident" playlabel="Play: Black Hat USA 2026: The 'Breaking' News: The OpenAI–Hugging Face Incident"> </lite-youtube></p><p>Here's the timeline. My favourite detail is at the end: OpenAI found out that they were responsible for the attack on Hugging Face when they reached out to ask to have their credentials revoked (after their internal inves

<p><strong><a href="https://simonw.github.io/raccoon-heist-codex/">Moonlight &amp; Mayhem (Raccoon Heist by Codex + GPT-5.6 Sol Ultra)</a></strong></p>On Wednesday I wrote about <a href="https://simonwillison.net/2026/Aug/5/raccoon-heist/">One-shotting a Raccoon Heist game using Claude Fable 5</a>, where I had Claude Fable 5 build a full working game from a premise I generated with GPT-3 and DALL-E <a href="https://twitter.com/simonw/status/1555626060384911360">four years ago</a>.</p><p>I decided to pose the <a href="https://simonwillison.net/2026/Aug/5/raccoon-heist/#the-fable-5-prompt">exact same prompt</a> to Codex Desktop running GPT-5.6 Sol Ultra - the mode where Sol makes <em>aggressive</em> use of sub-agents - to see how it would do.</p><p>It produced a much better game! Here's <a href="https://simonw.github.io/raccoon-heist-codex/">Moonlight &amp; Mayhem</a> - <a href="https://github.com/simonw/raccoon-heist-codex/">GitHub repository here</a>, including the <a href="https://gi

<p><strong><a href="https://www.404media.co/the-tokenpocalypse-is-here-companies-are-scrambling-to-stop-spending-so-much-on-ai/">The Tokenpocalypse Is Here: Companies Are Scrambling To Stop Spending So Much on AI</a></strong></p>There's a fun anecdote from Accenture (apparently via leaked meeting audio recordings) in this 404 Media piece from June 24th:</p><blockquote><p>“We’re seeing from some of the data internally at least that it’s actually not our engineers that are driving the token consumption. It’s a lot of the non-engineers that are doing some of those behaviors [...] you were talking about,” Justice Kwak, Accenture’s agentic AI strategy lead, said [...]</p><p>Stuart Henderson, Accenture’s client group lead, interrupts. He jokes he hopes Kwak didn’t just convert a PDF into images and then into markdown files. “I’m learning that’s one of the big token chewers,” Henderson says. “Turning PDFs into markdown: is that right?”</p><p>That’s when Kwak says that’s what Accenture’s own

<p><strong>Release:</strong> <a href="https://github.com/simonw/datasette-auth-tokens/releases/tag/0.4a13">datasette-auth-tokens 0.4a13</a></p> <p>Upgraded for compatibility with `sqlite-utils 4.</p> <p>Tags: <a href="https://simonwillison.net/tags/datasette">datasette</a></p>

<p><strong>Release:</strong> <a href="https://github.com/simonw/datasette/releases/tag/1.0a38">datasette 1.0a38</a></p> <blockquote><p>This release fixes a <strong>SQL injection</strong> security issue that affects Datasette instances that serve a <strong>mixture of public and private tables</strong> in the same database, with access configured using the <a href="https://docs.datasette.io/en/latest/authentication.html">Datasette permissions system</a>.</p><p>Site administrators who serve private tables in this way are advised to disable the <a href="https://docs.datasette.io/en/latest/authentication.html#execute-sql">execute-sql permission</a> <actions_execute_sql>` on that database to prevent users from accessing private tables using raw SQL queries. The bug that has been fixed would have allowed users with access to any public table to execute SQL injection attacks despite that restriction, giving them read-only access to data in private tables in the same database.</p><p>This fix i

<p><strong>Release:</strong> <a href="https://github.com/simonw/datasette/releases/tag/0.65.3">datasette 0.65.3</a></p> <p>Back-ported the SQL Injection security fix from <a href="https://simonwillison.net/2026/Aug/6/datasette/">1.0a38</a>.</p> <p>Tags: <a href="https://simonwillison.net/tags/datasette">datasette</a></p>

<p><strong><a href="https://writethatblog.substack.com/p/simon-willison-on-technical-blogging">Simon Willison on Technical Blogging</a></strong></p>I was interviewed by Cynthia Dunlop for her "Write that blog!" series back in January, but I just realized I never linked to the interview from my own blog!</p><p>It includes my answers to the following questions:</p><ul><li>Why did you start blogging – and why do you continue?</li><li>What has been the most surprising impact of blogging for you?</li><li>What blog post are you most proud of and why?</li><li>What post was the most difficult to write and how did you tackle it?</li><li>Any lessons learned that you want to share with the community?</li><li>Your advice for people just getting started with blogging?</li><li>A few blogs that you particularly enjoy?</li></ul><p>I'll repeat my most important piece of advice here:</p><blockquote><p>My number one tip for blogging is to lower your standards! Aim to hit publish while you are still acti

<p><strong><a href="https://www.cnn.com/2026/08/05/tech/meta-ai-hacking">An AI model from Meta also hacked another company during testing</a></strong></p>Stop me if you've <a href="https://simonwillison.net/tags/accidental-cyberattacks/">heard this one before</a>:</p><blockquote><p>An AI model from the parent company of Facebook and Instagram hacked into another company’s systems during cybersecurity testing, a spokesperson confirmed on Wednesday.</p><p>Meta says the breach occurred because of an inadvertent error during testing of the model, similar to previously disclosed incidents with OpenAI and Anthropic.</p><p>“A misconfiguration by Irregular, an independent testing company Meta uses, inadvertently allowed one of our models access to the internet during evaluation,” the Meta spokesperson said.</p><p>Meta’s Muse Spark model “exploited a security vulnerability” in another company “in a manner similar to previously-reported instances with other companies.”</p></blockquote><p>The In

<p><strong><a href="https://research.meta.ai/blog/introducing-muse-code-and-muse-spark-1-2">Introducing Muse Code and Muse Spark 1.2</a></strong></p>Yet more evidence that the most important characteristic of any model these days is long-sequence agentic tool calling. Meta shipped their own coding agent as part of getting that to work!</p><blockquote><p>Muse Spark 1.2 is a coding-focused update to Muse Spark 1.1, with improvements in code generation, complex debugging, codebase understanding, and end-to-end developer workflows. In Muse Spark 1.2, we significantly scaled up training compute on coding tasks while expanding training environment diversity. The model also maintains its strength in other key areas like general agents. [...]</p><p>We co-trained Muse Spark 1.2 with Muse Code to ensure the model exhibits its best performance and coding usability when paired together. The training included rejection sampled harness trajectories and recipe optimizations for goals, compaction, an

<p><strong><a href="https://openai.com/index/third-party-cyber-evaluations-involving-openai-models/">Third-party cyber evaluations involving OpenAI models</a></strong></p>And <em>another one</em>. I had to create a <a href="https://simonwillison.net/tags/accidental-cyberattacks/">accidental-cyberattacks tag</a> to keep track of them all!</p><p>This post from OpenAI covers both the UK AI Safety Institute attack (see <a href="https://simonwillison.net/2026/Aug/5/incident-report/">my previous post</a>) and another attack enabled by <a href="https://www.irregular.com">Irregular</a>:</p><blockquote><p>Irregular, one of our external cybersecurity testing partners, was running Capture-the-Flag-style evaluations intended to be isolated from the internet, but a testing-environment misconfiguration allowed models to access the public internet. [...]</p><p>In one test, the name of the fictional target for the CTF challenge unintentionally coincided with a real domain. Because the testing environ

<p><strong><a href="https://www.aisi.gov.uk/blog/incident-report-unsanctioned-agent-behaviour-during-cyber-testing">Incident Report: unsanctioned agent behaviour during cyber testing</a></strong></p>It happened <em>again</em>. This time it was the UK government's AI Security Institute who accidentally attacked other companies while running an evaluation with models with the safety filters turned off. From <a href="https://cdn.prod.website-files.com/663bd486c5e4c81588db7a1d/6a724858f7db25c81487016d_Security%20Incident%20INC-2026-07-28-01.pdf">their technical paper</a> (PDF):</p><blockquote><p>During a cyber evaluation, from 25 to 28 July 2026, AI agents engaged in sustained, unsanctioned activity directed at what were, in practice, real people and organisations. These attempts were unsuccessful and, to the best of our knowledge, no real-world harm resulted. [...]</p><p>Across 122 evaluation attempts on two of AISI’s cyber challenges, AISI found 19 instances where AI agents took unsanct

<p>Back in 2022 <a href="https://twitter.com/simonw/status/1555626060384911360">I tweeted</a> screenshots of a game concept generated by GPT-3 and some concept "art" created using DALL-E. Today, on the fourth anniversary of that tweet, I decided to see if Claude Fable 5 (running in <a href="https://code.claude.com/docs/en/claude-code-on-the-web">Claude Code for web</a>) could build the entire game from the content of that tweet. It did a pretty good job of it!</p><p>You can <a href="https://simonw.github.io/raccoon-heist/">play the game here</a>. Here's <a href="https://github.com/simonw/raccoon-heist/">the GitHub repo</a>, and a short video demo:</p><p><video controls="controls" preload="none" poster="https://static.simonwillison.net/static/2026/raccoon-heist-poster.jpg" width="1280" height="720" style="display: block; width: 100%; height: auto;" > <source src="https://static.simonwillison.net/static/2026/raccoon-heist-720p.mp4" type="video/mp4" /> Your browser does not support HTML5

<p>I released <a href="https://llm.datasette.io/en/stable/changelog.html#v0-32">LLM 0.32</a> this morning, the most significant new version of LLM since the initial launch of the project. The new version includes support for visible reasoning traces, server-side provider tools, redesigned content-addressable SQLite logs, new models, and new features enabled by the OpenAI Responses API. I also released a new version of the <a href="https://github.com/simonw/llm-anthropic">llm-anthropic plugin</a> with substantial updates of its own.</p><h4 id="headline-features-for-llm-cli-users">Headline features for LLM CLI users</h4><p>Running LLM against reasoning models now <strong>displays their reasoning traces</strong> to standard error, so you can see what they are "thinking" without that information being included in the standard output that you might pipe to another tool. Add <code>-R/--hide-reasoning</code> to turn this off.</p><p><img src="https://static.simonwillison.net/static/2026/best-

<p><strong>Release:</strong> <a href="https://github.com/simonw/llm-anthropic/releases/tag/0.26">llm-anthropic 0.26</a></p> <p>Includes new features enabled by <a href="https://simonwillison.net/2026/Aug/4/new-release-of-llm/">LLM 0.32</a>:</p><blockquote><ul><li>New models: <code>claude-fable-5</code>, <code>claude-sonnet-5</code>, and <code>claude-opus-5</code>. <a href="https://github.com/simonw/llm-anthropic/issues/75">#75</a>, <a href="https://github.com/simonw/llm-anthropic/issues/76">#76</a></li><li>Added server-side tools for <code>WebSearch</code>, <code>WebFetch</code>, <code>CodeExecution</code>, and <code>AnthropicMCP</code>, available through LLM's <code>-T</code> interface or Python <code>tools=</code>. The previous <code>-o web_search*</code> options have been removed in favor of <code>-T WebSearch</code>. <a href="https://github.com/simonw/llm-anthropic/issues/79">#79</a></li><li>Upgraded to <a href="https://llm.datasette.io/en/stable/changelog.html#v0-32">llm&gt;=0.32

<p><strong><a href="https://github.com/PipeNetwork/minimax-h3-mlx">PipeNetwork/minimax-h3-mlx</a></strong></p>MiniMax released <a href="https://huggingface.co/MiniMaxAI/MiniMax-H3">MiniMax-H3</a> two days ago - they describe it as a "a general-purpose, omni-modal generative system", which in practice means it accepts text, images, audio and video and can use them to generate up to 15 second video clips with audio included.</p><p>This Python package ports it to MLX for running on Apple Silicon.</p><p>I got it running on my M5 Max MacBook Pro. I cloned the repo and ran the model like this:</p><pre><code># First download the modelsuvx --from huggingface_hub hf download MiniMaxAI/MiniMax-H3 \ --include 'FL2VA/*' --exclude 'FL2VA/transformer/*'uvx --from huggingface_hub hf download pipenetwork/MiniMax-H3-MLX-8bit# Now run the promptuv run --with mlx-vlm \ --with-requirements requirements.txt python scripts/generate.py \ "a rainbow colored skunk leaps over a mossy log in a supermarket" \ -o

<p><strong>Release:</strong> <a href="https://github.com/simonw/llm/releases/tag/0.32">llm 0.32</a></p> <p>See <a href="https://simonwillison.net/2026/Aug/4/new-release-of-llm/">my detailed blog post about this release</a>.</p> <p>Tags: <a href="https://simonwillison.net/tags/llm">llm</a></p>