| Introducing Claude Opus 5 |
https://www.anthropic.com/news/claude-opus-5 |
I've been offline [kayaking with sea otters](https://en.wikipedia.org/wiki/Elkhorn_Slough) for much of today so I haven't had a chance to put Anthropic's new model Claude Opus 5 through its paces yet. The buzz is positive, and Anthropic's description of it as a "thoughtful and proactive model that comes close to the frontier intelligence of Claude Fable 5 at half the price" sounds promising. It's currently [leading the Artificial Analysis leaderboard](https://twitter.com/artificialanlys/status/2080777718933995967), in front of even Fable 5.
It's priced the same as Opus 4.8, and continues to offer a "fast mode" at twice the cost of the base model.
Based on this anecdote in the release post it sounds like it might be [relentlessly proactive](https://simonwillison.net/2026/Jun/11/fable-is-relentlessly-proactive/):
> On one Frontier-Bench task, Opus 5 was given a drawing of a machine part and asked to write code to rebuild it as a 3D FreeCAD model. However, in this task, the model was intentionally given no way to directly viewthe drawing. Opus 5 responded by writing its own computer vision pipeline to pull the geometry from the raw pixels, then reconstructed the full machine part.
It's better at finding vulnerabilities but has deliberately not been trained on how to exploit them. Hopefully this means the US government won't shut it down!
> As with its predecessor, Opus 4.8, we’ve intentionally avoided training Opus 5 on cyber tasks. The model has nevertheless improved substantially on these tasks as a result of becoming more generally capable, and it comes close to Mythos 5 at *finding* cybersecurity vulnerabilities. However, it remains substantially behind Mythos 5 on the *exploitation* of those vulnerabilities—that is, in turning vulnerabilities into material cyber threats.
Anthropic have published a [prompting guide for Claude Opus 5](https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/prompting-claude-opus-5). Thariq Shihipar has also written [The new rules of context engineering for Claude 5 generation models](https://claude.com/blog/the-new-rules-of-context-engineering-for-claude-5-generation-models).
The [first pelican I got](https://tools.simonwillison.net/markdown-svg-renderer#url=https%3A%2F%2Fraw.githubusercontent.com%2Fsimonw%2Fllm-anthropic%2F8272dfee5bdb65d5c88eef083da3ad885539b7df%2Flog.md) was missing the bicycle wheels; the [second attempt](https://tools.simonwillison.net/markdown-svg-renderer#url=https%3A%2F%2Fraw.githubusercontent.com%2Fsimonw%2Fllm-anthropic%2Ffeaab840ea20eb15e29d8f72a9e42feceb23876a%2Flog.md) was better. |
2026-07-24 23:48:50+00:00 |
| The first known runaway AI agent - or a very bad marketing stunt? |
https://martinalderson.com/posts/huggingface-openai-exploit/ |
Martin Alderson's commentary on the [OpenAI accidental cyberattack against Hugging Face](https://simonwillison.net/2026/Jul/22/openai-cyberattack/) includes a couple of details I hadn't considered.
First, Hugging Face offers a truly rich target if you're trying to find potential vulnerabilities that require executing arbitrary code:
> Hugging Face has an *enormous* attack surface. They have more interfaces than I can count which run untrusted models and code. While they definitely have invested in defences, by nature of their operating model they do have many more opportunities to be attacked than many other services. I certainly don't envy their cybersecurity teams.
Secondly, one of the things that has puzzled me is how OpenAI didn't notice that their sandbox had been so thoroughly breached by the agent. Surely they'd be monitoring network traffic closely?
Martin points out that:
> It's also likely they were running a huge amount of benchmarks simultaneously with ~unlimited token budgets - you want as many samples as possible to figure out how good a model is at a certain benchmark. It may also be they are testing various different checkpoints of the model too, understanding how the model is improving as it goes through the various training stages.
The mistakes made by the OpenAI team running this benchmark are easier to imagine when you think about the scale at which benchmarks of this kind usually operate. For all we know they could have been subjecting a new model to dozens of benchmarks at the same time, in dozens of different environments. |
2026-07-23 22:53:08+00:00 |
| Are AI labs pelicanmaxxing? |
https://dylancastillo.co/posts/pelicanmaxxing.html |
Excellent piece of work by Dylan Castillo, who took a deep-dive into the frequently pondered question of whether the AI labs have been deliberately training models to draw pelicans riding bicycles in response to my [deeply unscientific benchmark](https://simonwillison.net/tags/pelican-riding-a-bicycle/).
I've been randomly spot-checking this in the past by testing models against other animals riding other types of vehicle, but never with anything close to the diligence of Dylan's methodology here.
Dylan took 8 animals × 6 vehicles = 48 prompts and ran them three times each through 7 different models ( GPT-5.6 Terra, Claude Sonnet 5, Gemini 3.5 Flash, Grok 4.5, Qwen3.7-Max, GLM-5.2, and DeepSeek V4 Pro). He then used GPT-5.6 Luna and Gemini 3.1 Flash-Lite to help evaluate the results.
There's a neat filter view for exploring the results:
For the models he tested he could find no evidence of pelimaxxing:
> - [The pelicans on bicycles don’t look any better](https://dylancastillo.co/posts/pelicanmaxxing.html#evidence-1-the-pelicans-on-bicycles-dont-look-any-better)
> - [Labs are not better at drawing pelicans](https://dylancastillo.co/posts/pelicanmaxxing.html#evidence-2-labs-are-not-better-at-drawing-pelicans)
> - [Labs are not better at drawing bicycles](https://dylancastillo.co/posts/pelicanmaxxing.html#evidence-3-labs-are-not-better-at-drawing-bicycles)
> - [Labs are not better at drawing pelicans on bicycles, even adjusting for difficulty](https://dylancastillo.co/posts/pelicanmaxxing.html#evidence-4-labs-are-not-better-at-drawing-pelicans-on-bicycles-even-adjusting-for-difficulty)
> - [The pelican-bicycle scenes don’t look memorized](https://dylancastillo.co/posts/pelicanmaxxing.html#evidence-5-the-pelican-bicycle-scenes-dont-look-memorized) [...]
>
> Pelicans aren’t drawn any better than other animals. Bicycles aren’t drawn any better than other vehicles. And no lab draws the combination better than its pelicans and bicycles already predict. GLM-5.2 comes closest: it has the largest boost on the exact pelican-bicycle cell, and and its first pelican-on-bicycle sample caught my eye. But the effect is small and not significant, so I wouldn’t put too much weight on it. |
2026-07-22 23:01:00+00:00 |
| Nativ: Run AI models locally on your Mac |
https://blaizzy.github.io/nativ/ |
Prince Canuma is the developer behind the excellent [MLX-VLM](https://github.com/Blaizzy/mlx-vlm) Python library for running vision-LLMs using MLX on a Mac.
I'm really excited about his new project, which wraps MLX in a full macOS desktop application. It's similar in shape to LM Studio, providing both a chat interface and a localhost API server for accessing models.
The app picked up MLX models I had already tried that were present in my Hugging Face cache directory, which was a nice touch. |
2026-07-21 14:22:27+00:00 |
| Who’s Afraid of Chinese Models? |
https://stratechery.com/2026/whos-afraid-of-chinese-models/ |
Interesting proposal from Ben Thompson that both addresses the hypocrisy of labs outlawing distillation against their models despite training on unlicensed data, and could help US open models compete more effectively with their Chinese counterparts:
> The U.S. should pass a law that (1) makes explicit that collecting data for training models is fair use, and (2) bars terms of service that forbid distillation, for U.S. companies at a minimum. Stopping distillation — which is literally just querying the API — is nearly impossible; the U.S. should go the other way and lean into a new copyright policy that both indemnifies the labs and also guarantees that what they learned fuels further innovation for everyone else.
Ben also theorizes that Alibaba's decision to release Qwen 3.8 Max as open weights - a reversal from their decision [not to release Qwen 3.7 Max](https://qwen.ai/blog?id=qwen3.7) in May - may have been influenced by a [recent speech](http://english.scio.gov.cn/topnews/2026-07/18/content_118605932.html) by Xi Jinping, who said:
> We should seize this rare, historic opportunity to encourage open source, openness, collaboration and sharing.
And on the subject of [Qwen 3.8 Max](https://twitter.com/Alibaba_Qwen/status/2078759124914098291) - a new 2.4T parameter model (nearly as large as the 2.8T Kimi K3) - here's [a pelican it drew](https://tools.simonwillison.net/markdown-svg-renderer#url=https%3A%2F%2Fgist.github.com%2Fsimonw%2F735f2cf19b795517cb2ff6cae1c71c64):
I particularly enjoyed seeing these notes in the (extensive) reasoning trace: "Could add helmet? No." and "Maybe add small bell? no." and "Need maybe add small fish in basket? Not necessary." |
2026-07-20 17:09:19+00:00 |
| AI Mania Is Eviscerating Global Decision-Making |
https://ludic.mataroa.blog/blog/ai-mania-is-eviscerating-global-decision-making/ |
Here's an entertaining perspective from Nik Suresh on the AI mania that is overwhelming the large companies that he consults with. It's crammed with spicy anecdotes from anonymous sources.
> In one extreme case, I have seen an executive confess that they had never even used ChatGPT or any AI tool in their life, immediately after producing a technical strategy for an organisation with $2B+ in revenue which was entirely centered around AI.
Here's a report from an engineer at a company with a token leaderboard:
> Checking out a parallel copy of our Go repository and telling the AI to rewrite the whole thing in Zig while I work on something else just so I can keep my job.
I particularly enjoyed this conversation with a skeptical executive at an over-enthusiastic company:
> I asked *why* this was being repeated without opposition. Was it just sales fluff?
>
> The answer was a lot more interesting. It was *partially* ridiculous sales material being delivered to an easily excitable audience, but this was not the dominant factor constraining honesty. Executives at their *customers* were saying absurd things about achieving 100x productivity, and this meant that if any executive at the *vendor* said that these gains were not plausible, it would undermine the credibility of the customer’s executive, be perceived as an attack (or heresy), and possibly result in an enterprise contract cancellation. And getting enterprise contracts cancelled because you wanted to opine on something that doesn’t really matter to your organisation’s mission is a great way to get fired. |
2026-07-19 05:06:21+00:00 |
| Claude make Fable 5 permanent |
https://twitter.com/claudeai/status/2078302415804379218 |
An update from the `@claudeai` account on Twitter:
> Beginning July 20, Claude Fable 5 will be included in all Max and Team Premium plans, at 50% of limits.
>
> Pro and Team Standard users will continue to have access to Fable via usage credits, and will receive a one-time $100 credit.
As I was saying [last week](https://simonwillison.net/2026/Jul/12/bump/), the competition from [GPT-5.6 Sol](https://simonwillison.net/2026/Jul/9/gpt-5-6/) (and maybe to a lesser extent [Kimi 3](https://simonwillison.net/2026/Jul/16/kimi-k3/)) made untenable Anthropic's plan to remove Fable 5 from their subscription accounts and make it available exclusively through API pricing.
Why pay $100 or $200/month for a subscription plan that *doesn't* include Anthropic's best model?
Their original plan was driven by concerns over compute capacity. I wonder if they'll have to dial back their training efforts in order to make more GPUs available to help serve the model.
A lot of people were losing sleep over trying to make the most of Fable 5 before subscriber access was withdrawn. It's nice not to have to worry about the Fablepocalypse any more.
**Update**: Important to note that users on the $20/month plan will still not have access to Fable 5 on that subscription. The Max plans are $100 and $200/month. |
2026-07-18 06:00:13+00:00 |
| nascheme/quixote |
https://github.com/nascheme/quixote |
A certain vintage of Python web nerd might be delighted to learn that the most recent commit to the Quixote web framework was [six hours ago]((https://github.com/nascheme/quixote/commit/7f775cf9d1e7e80fcbb2706b4a1d971e55ca74a3)).
The [oldest commit](https://github.com/nascheme/quixote/commit/d6b73c5768c2d041b68b54cc71863604249abc18) in that repo is from 21 years ago, and that was the initial import of Quixote 2.4 from Subversion into Git. |
2026-07-18 05:27:49+00:00 |
| Firefox in WebAssembly |
https://developer.puter.com/labs/firefox-wasm/ |
This is absurdly cool: Puter compiled Firefox to WebAssembly such that the whole browser runs in another browser.
Here's my blog, running in Firefox, running in WebAssembly, running in Chrome:
They chose Firefox/Gecko because it has strong single-process support. The project used an estimated $25,000 worth of Claude Opus and Fable tokens, but took advantage of a Claude Max subscription plan so cost much less in actual dollars.
The demo funnels all traffic over a WebSocket protocol (using the [Wisp protocol](https://github.com/MercuryWorkshop/wisp-protocol)) through Puter's server - a requirement to get this kind of thing to work because code running in browsers can't open arbitrary network connections.
(That proxying sounds expensive! The team [had to scale the servers up](https://news.ycombinator.com/item?id=48926939#48936563) to handle the traffic during the Hacker News conversation about the project.)
Puter claim this supports end-to-end encryption and that looks to be true - I inspected the WebSocket messages and traffic to my own HTTPS site was encrypted whereas requests and responses to `http://www.example.com/` were in cleartext.
[Here's the repo](https://github.com/HeyPuter/firefox-wasm) for `firefox-wasm`. [theogbob/WebkitWasm](https://github.com/theogbob/WebkitWasm) is a similar project that compiles WebKit to WASM, but that one doesn't currently have an accessible online demo. |
2026-07-16 23:34:16+00:00 |
| Inkling: Our open-weights model |
https://thinkingmachines.ai/news/introducing-inkling/ |
Mira Murati's Thinking Machines Lab just released their first open-weights model. Inkling is "a Mixture-of-Experts transformer with 975B total parameters, 41B active" - an Apache-2.0 licensed multimodal model trained on 45 trillion tokens of text, images, audio and video.
They're also promising Inkling-Small, a 276B (12B active) model, but that's still being tested and the weights will be released "once that work is complete".
The [model card](https://thinkingmachines.ai/model-card/inkling/) is much shorter than I've come to expect from US AI labs. It links to even shorter [Training Data Documentation](https://thinkingmachines.ai/training-data-documentation/) with almost nothing of interest in it - it's best summarized by these two paragraphs:
> The datasets Thinking Machines Lab uses to develop its AI services includes content that is in the public domain as well as content that may be subject to intellectual property protection.
>
> Thinking Machines Lab’s services were developed using publicly available content obtained from the open internet and publicly accessible data repositories. Certain datasets were also obtained from third parties.
By Thinking Machines' own admission, this is not a frontier model. It's instead intended as a strong base model for fine-tuning using their own [Tinker training platform](https://thinkingmachines.ai/tinker/):
> Inkling is not the strongest overall model available today, open or closed. Instead, a combination of qualities makes it a good open-weights base for customization: multimodal capabilities, efficient thinking, and availability on Tinker for fine-tuning.
There's a lot to like about this release. It's Apache-2.0 licensed, and looks competitive with the open weight models coming out of China - it's good to see the US open weights ecosystem gain a new viable contender to join NVIDIA Nemotron and Gemma 4.
Here's its attempt at an SVG pelican riding a bicycle, which I generated using this `curl` command against the Thinking Machines API:
<div class="highlight highlight-source-shell"><pre>curl <span class="pl-s"><span class="pl-pds">"</span>https://tinker.thinkingmachines.dev/services/tinker-prod/oai/api/v1/chat/completions<span class="pl-pds">"</span></span> \
-H <span class="pl-s"><span class="pl-pds">"</span>Authorization: Bearer <span class="pl-smi">$TINKER_API_KEY</span><span class="pl-pds">"</span></span> \
-H <span class="pl-s"><span class="pl-pds">"</span>Content-Type: application/json<span class="pl-pds">"</span></span> \
-d <span class="pl-s"><span class="pl-pds">'</span>{</span>
<span class="pl-s"> "model": "thinkingmachines/Inkling",</span>
<span class="pl-s"> "messages": [</span>
<span class="pl-s"> {"role": "user", "content": "Generate an SVG of a pelican riding a bicycle"}</span>
<span class="pl-s"> ],</span>
<span class="pl-s"> "stream": false</span>
<span class="pl-s"> }<span class="pl-pds">'</span></span></pre></div>
Full [response here](https://gist.github.com/simonw/8117ac4376371dd3fc2b5dbce27e0855).
Since it's a multi-modal model I had it describe its own image (after I rendered it to a JPEG) by sending this JSON:
<div class="highlight highlight-source-json"><pre>{
<span class="pl-ent">"model"</span>: <span class="pl-s"><span class="pl-pds">"</span>thinkingmachines/Inkling<span class="pl-pds">"</span></span>,
<span class="pl-ent">"messages"</span>: [{
<span class="pl-ent">"role"</span>: <span class="pl-s"><span class="pl-pds">"</span>user<span class="pl-pds">"</span></span>,
<span class="pl-ent">"content"</span>: [
{<span class="pl-ent">"type"</span>: <span class="pl-s"><span class="pl-pds">"</span>image_url<span class="pl-pds">"</span></span>, <span class="pl-ent">"image_url"</span>: {<span class="pl-ent">"url"</span>: <span class="pl-s"><span class="pl-pds">"</span>data:image/jpeg;base64,$BASE_64_JPEG<span class="pl-pds">"</span></span>}},
{<span class="pl-ent">"type"</span>: <span class="pl-s"><span class="pl-pds">"</span>text<span class="pl-pds">"</span></span>, <span class="pl-ent">"text"</span>: <span class="pl-s"><span class="pl-pds">"</span>Describe this image in detail<span class="pl-pds">"</span></span>},
],
}],
<span class="pl-ent">"stream"</span>: <span class="pl-c1">false</span>
}</pre></div>
Here's [the full response](https://gist.github.com/simonw/dccebd0581f5c4056c5b0ca5bdc38cd9#reasoning_content). It thought it had drawn a "stork or seagull":
> This is a cheerful, flat-vector cartoon illustration featuring a white bird riding a bicycle across a green landscape.
>
> **The Bird:** The central figure is a stylized white bird—resembling a stork or seagull—with a plump, rounded body. It has a long, prominent orange beak that is slightly open, revealing a black line for the mouth. Its eye is small and black with a tiny white highlight. The bird has black wing and tail feathers with white edges, and thin orange legs ending in black feet that rest on the bicycle's orange pedals.
>
> **The Bicycle:** The bike has a simple black frame with two large wheels featuring black tires, gray inner rims, and black spokes arranged in a cross pattern. The handlebars are black, and there are bright orange pedals attached to a black crank. A white seat is visible behind the bird's body.
>
> **The Setting:** The scene is set against a clear, light blue sky. There are two fluffy white clouds—one on the left side and a smaller one near the bird's head on the right. A bright, solid yellow sun shines in the upper right corner. At the bottom, there are rolling green hills with a smooth, curved top edge where the bicycle rests.
>
> The overall style is playful, simple, and colorful, with clean lines and a bright, sunny atmosphere. |
2026-07-16 15:35:25+00:00 |