Simon Willison's Weblog: Blogmarks

Introducing Hy4 Preview

2026-08-29T23:53:13+00:00

New open weight text input (no vision) LLM from Chinese company Tencent today: 770B total parameters, 49B active parameters, 1M token context window, 1.56TB on Hugging Face.

This is a big size increase from their previous Hy3 in July, which was 295B, 21B active, 256,000 context, 598GB.

I recently started using model chat templates to better understand their capabilities. Here's Hy4's chat_template.jinja on Hugging Face, which includes this section:

{%- if not reasoning_effort is defined %}
    {%- set reasoning_effort = 'high' %}
{%- elif reasoning_effort not in ['high', 'no_think'] %}
    {%- if reasoning_effort is none %}
        {{- raise_exception('reasoning_effort error : None, should be no_think/high') }}
    {%- else %}
        {{- raise_exception('reasoning_effort error : ' + reasoning_effort + ', should be no_think/high') }}
    {%- endif %}
{%- endif %}

So it looks like there are just two reasoning effort levels: "high" (the default) and "no_think" (reason by disabled).

I tried my "Generate an SVG of a pelican riding a bicycle" prompt with the default high reasoning via OpenRouter and got this:

Quoting the reasoning trace:

[...] Let's maybe add a helmet? It could improve riding theme, but may obscure head. Maybe a small cycling cap or helmet? The user didn't ask; can add red helmet? Might be cute. But pelican with big beak; a helmet might obscure. Better maybe no.

Maybe add sunglasses? no.

Maybe add water? no.

It's interesting how the reasoning trace uses slightly truncated English, presumably because perfect grammar isn't useful or token efficient for hidden reasoning text.

Tags: ai, generative-ai, llms, pelican-riding-a-bicycle, llm-reasoning, llm-release, ai-in-china

Just a rumour of a bug is enough to find a security exploit these days

2026-08-28T22:12:02+00:00

Just a rumour of a bug is enough to find a security exploit these days

Anil Madhavapeddy is a professor of computer science at Cambridge and a core maintainer of the OCaml compiler. In this somewhat alarming post he reports that security issues in OCaml projects are seeing evidence of attempted exploits within minutes of patches being shared for discussion:

This normally takes a few days and a release within a week or two is reasonable. Within about ten minutes (!) this website was fielding probes for percent-encoded traversal sequences, indicating that automated watchers are keeping an eye on public repositories.

Modern coding agents have become so effective at finding flaws that the slightest hint at a new bug can be enough information for them to find it, something Anil has been able to demonstrate using his own agents, switching to DeepSeek V4 Pro⁠ when Claude Fable refused the task.

Anil points out that this rate of discovery appears incompatible with existing open source embargo practices for new issues. If an issue can become an exploit this fast, we need to figure out new processes for keeping our communities safe.

rclone maintainer Nick Craig-Wood confirms in the Hacker News comments that his project is seeing this problem:

In the first 10 years of the rclone project we received about 20 security disclosures through GitHub. We had to deal with over 40 in the last month! That has taken a huge amount of my time, even using AI tools to triage and come up with fixes for review.

The hit rate for those security disclosures is pretty good - about 75% of them have a nugget of something which needs looking at. [...]

GitHub assigns CVEs for the advisories. Before the AI apocalypse they took 2-3 days for an assignment but now it they are running at 3-4 weeks so I have to send the point releases out with CVE-PENDING in the changelog which isn't ideal.

Via Hacker News

Tags: open-source, security, ai, generative-ai, llms, coding-agents, ocaml, ai-security-research

Breaking Claude Code Opus 5 Auto Mode

2026-08-27T22:50:25+00:00

Breaking Claude Code Opus 5 Auto Mode

Anthropic are putting a great deal of faith in Claude Code's auto mode for protecting their coding agent users against prompt injection attacks. They recently made that the default and have made bold claims about its effectiveness.

Johann Rehberger is one of the most credible prompt injection researchers active today. He found an attack against auto mode which he claims works 80% of the time, by tricking Claude Code into downloading and uncompressing a zip archive, then executing code that imports base64 without noticing that this will import and execute a local struct.py file extracted from the archive.

In a few cases auto mode directly prevented the agent from preventing harmful code from continuing to execute!

In a few runs Claude tried to terminate the malware process once it noticed the compromise, but Auto Mode denied the cleanup command.

Claude detects the compromise, but Auto Mode blocks its cleanup command

The safety mechanism itself can become part of the failure. The classifier allowed the creation of the malware process, but then it blocked the command intended to stop it!

I agree with Johann's conclusion here: the only safe way to run agents if there's any risk of attracting the attention of an adversarial attack is with a sandbox:

Run unattended coding agents in a container, VM or OS sandbox.

Restrict network egress.

Monitor your agents.

Do not expose home directories, SSH keys, cloud credentials,… to the agent runtime. [...]

Update 30th August 2026: On Lobste.rs hyperpape points out that this doesn't fit the bill of a classic prompt injection attack because at no point are malicious instructions from the website accidentally followed by the LLM. They're right: this is more of a confused environment attack where the nature of the environment that the agent is exposed to results in an exploit.

Tags: sandboxing, security, ai, prompt-injection, generative-ai, llms, anthropic, claude, johann-rehberger, claude-code

Qwen3.8-Flash-Next

2026-08-26T23:52:58+00:00

Qwen3.8-Flash-Next

Another open weights model from Qwen. This one is "a multimodal MoE model that also serves as an early preview of the architecture used in Qwen4".

It's pretty big: 125B tokens, but only 6B active which means it gets a significant performance boost.

I've been trying it out on a DGX Spark using these Unsloth quantized models. I'm still exploring the model - so far I've tried the 72.5GB UD-IQ1_S one (producing these pelicans) and the 78.9GB UD-Q2_K_XL (producing these).

My favorite so far was this xhigh reasoning effort one from UD-Q2_K_XL:

Via Hacker News

Tags: ai, generative-ai, llms, qwen, pelican-riding-a-bicycle, ai-in-china, nvidia-spark

EVE Online: The Move to Python 3 Begins!

2026-08-25T22:59:30+00:00

EVE Online: The Move to Python 3 Begins!

EVE Online has been one of the most interesting case studies in Python at scale for over twenty years now.

They've been running on Stackless Python since their launch in 2003, and their last major upgrade was 16 years ago, to Stackless Python 2.7 in 2010.

Their upgrade to Python 3 will start using the futurize script against 2.4 million lines of code, followed by careful manual review of the ~20,000 places where Python 2 and 3 behavior differ - for example 1 / 2 is 0 in Python 2 but is 0.5 in Python 3.

There's nothing in this announcement about how they plan to replace Stackless, but at their conference last year they presented Scheduling in Carbon: Leaving Stackless Python Behind describing how they replaced Stackless in the Carbon engine for their more recent game EVE Frontier, using their (now open source) carbonengine/scheduler library.

Via Lobster.rs

Tags: eve-online, migrations, python, python3, stackless

Your executable is a SQLite database

2026-08-24T11:38:15+00:00

Your executable is a SQLite database

Farid Zakaria describes a neat Linux pattern for creating a SQLite database file that can be directly used as an executable binary.

The trick sets the SQLite file format's 4-byte application ID (68 bytes into the file) to SELF, standing for Structured Executable & Linkable Format. The various components of the ELF executable format are then arranged into a number of different SQLite tables, using this schema.

Their self-exec interpreter (C code here) can then extract and execute the necessary pieces.

You can additionally use a Linux mechanism called binfmt_misc to teach the kernel to execute that any time it encounters an executable matching that binary pattern. Farid uses NixOS here, but without NixOS I think registration looks something like this:

printf '%s\n' ':self:M:68:SELF::/usr/local/bin/self-exec:' \
  > /proc/sys/fs/binfmt_misc/register

Via Hacker News

Tags: c, linux, sqlite

Anthropic’s best AI model struggles to attract users as cheaper tools thrive

2026-08-23T20:24:52+00:00

Anthropic’s best AI model struggles to attract users as cheaper tools thrive

A few interesting numbers in this FT story gathered from "people with knowledge of the matter":

Anthropic's "annualized revenue" for July is up to $65bn - it was $47bn in May, and I collected more historic numbers here.
Anthropic expect Q3 to be profitable according to the same model they used to declare Q2 profitable. "It also told investors that it had 6,000 customers that spend $100,000 annually or more."
As for OpenAI, "annualised revenue has jumped 35 per cent in the quarter to date and is now over $40bn, with the launch of GPT 5.6 in July jolting the company’s performance after a sluggish start to the year".

This article also introduced me to the Ramp AI index, which uses billing data from 70,000 Ramp credit card using companies to estimate model adoption.

Here's Ramp's breakdown of Anthropic model spend for July 2026, which looks reasonable given that Opus 5 was only released on July 24th, and supports the idea that Fable's cost has made it a less popular model:

Opus 4.8: 28.0%
Sonnet 4.6: 8.3%
Fable 5: 8.0%
Opus 4.6: 6.9%
Sonnet 5: 3.6%
Opus 5: 3.5%
Opus 4.7: 1.7%
Sonnet 4.5: 1.3%
Haiku 4.5: 1.0%
Opus 4.5: 0.7%

Via Hacker News

Tags: ai, openai, generative-ai, llms, anthropic, claude, claude-mythos-fable

Stop Making TUIs

2026-08-21T16:07:32+00:00

Stop Making TUIs

Thomas Ptacek advocates for building real native user interfaces for even the smallest of personal tools, because coding agents have reduced the cost of getting a usable-enough GUI up and running to almost nothing.

I wrote about my vibe-coded bandwidth and GPU monitoring macOS task bar apps back in March, and I'm still using both of those on a daily basis.

I'm not habitually knocking out real UIs for my other projects yet, but I'm running out of excuses!

Thomas:

If you haven’t tried your hand at turning one of your 500 throwaway CLIs into a native app, you’re doing yourself a disservice. Go build a native UI. It’ll probably change the way you think.

Tags: thomas-ptacek, ai, generative-ai, llms, vibe-coding, coding-agents

ChatGPT search now uses the site:operator at scale

2026-08-20T23:57:32+00:00

ChatGPT search now uses the site:operator at scale

Promptwatch is part of the emerging "GEO" space, for Generative Engine Optimization - the chatbot version of SEO, where companies offer tools and consulting to help your site increase its presence in replies to prompts inside tools like ChatGPT.

The Promptwatch product uses automation to track responses to prompts across end-user chat products like ChatGPT, Claude, and Gemini. They publish aggregate reports on this as part of their own content marketing strategy, which do seem to provide credible hints as to otherwise invisible design changes to those products.

Their own tracking shows a notable change aligned with the GPT-5.6 rollout earlier this month:

The percentage of all ChatGPT Search fanout queries that contain the site:operator, per day. The share hovered between 0.3% and 0.5% for weeks, dipped briefly to 0.15% on August 3 to 5 (consistent with a staged rollout or pre-launch experiment), then jumped to 16-17% on August 8.

It's important to note that these figures only reflect the prompts for which they have automated tracking enabled.

This corresponds to OpenAI's somewhat vague August 6th announcement:

For Plus and Pro users, we’re updating GPT‑5.6 Sol in Chat to be more reliable with facts and provide more focused answers.

Once again I am hampered by OpenAI's decision to actively obscure their system prompts, but from poking at ChatGPT I believe their latest search tool has a shape like search(query, recency, domains) rather than encouraging a site: operator directly.

In a follow-up on August 18th Promptwatch reported that ChatGPT appeared to have greatly reduced the likelihood of Reddit being used in those searches. My own attempts to ascertain if the system prompt has been updated to discourage Reddit sourcing have been unsuccessful - the most thorough leaked system prompt collection I know of doesn't yet show any relevant changes.

Tags: reddit, seo, openai, chatgpt, ai-assisted-search, system-prompts

Mojo🔥 is now open source

2026-08-18T21:39:20+00:00

Mojo🔥 is now open source

The Mojo programming language has been promising an open source release since May 2023. Last week they shipped their 1.0 and today they have followed through on that original promise, releasing the compiler and toolchain under an Apache 2 license.

When Mojo first launched the stated goal was to produce a superset of Python, so existing Python code could be used to bootstrap their own ecosystem. That plan changed around August 2025:

Mojo may or may not evolve into a full superset of Python, and it’s okay if it doesn’t.

We’re encouraged by how well AI-assisted coding tools already help migrate Python to Mojo today, and we’re confident that future tooling and ecosystem maturity will make this evolution even smoother.

Today Mojo is its own language, optimized to make GPU programming as painless as possible using syntax inspired by Python, if not 100% compatible with existing code.

Via Lobste.rs

Tags: open-source, python, mojo

Qwen 3.8 27B scores 52 on the Artificial Analysis Intelligence Index

2026-08-17T23:58:14+00:00

Qwen 3.8 27B scores 52 on the Artificial Analysis Intelligence Index

That's the same score as GPT-5.6 Luna (max), and just one point behind GLM-5.2 (max) and DeepSeek V4 Pro 0813 (max) - that GLM is 753B and that DeepSeek is 1.7T parameters, and Luna is size unknown but presumably a whole lot bigger than 27B.

Qwen 3.8 27B is a truly astonishing model.

Via Hacker News

Tags: ai, generative-ai, llms, qwen, ai-in-china, artificial-analysis

We Tracked a Shipment of Rare Books. It Ended at an Amazon AI Training Facility

2026-08-17T15:21:29+00:00

We Tracked a Shipment of Rare Books. It Ended at an Amazon AI Training Facility

Excellent piece of reporting from 404 Media. For a while now there have been stories of book dealers receiving orders for large volumes of books from apparently price-insensitive anonymous customers, widely suspected to be companies looking to scan them for AI training (see my previous coverage of Anthropic's book scanning from June 2025.)

404 Media investigated with an AirTag!

In July, one bookseller told me they received a very large order of around 1,000 books on Biblio, one of these marketplaces. The seller agreed to put an Apple AirTag provided by 404 Media in one of the books included in this order so we could see where the book was going. And by extension, which company, AI or otherwise, was behind this massive order.

The book ended up delivered to the VGT3 corner of the LAS8 Amazon facility in the north east of Las Vegas, where the entrance carried this on-the-nose logo of a dinosaur with a book!

Photo credit: 404 Media

Online forum discussions between Amazon workers confirmed that VGT3 destructively scans large volumes of books.

Tags: amazon, journalism, ai, training-data, ai-ethics, 404-media

Don't classify. Hallucinate!

2026-08-14T21:54:35+00:00

Don't classify. Hallucinate!

I still have quite a bit of older content on my blog that I never got round to tagging. My blog has 1,856 tags - likely too many to feed to an LLM in one go and say "which of these tags match the following content".

Doug Turnbull has a neat solution. Tell the model to output tags without any details of the existing vocabulary, then use vector embeddings against the existing corpus to find the concrete tags that are closest to the ones the model imagined might fit!

His example prompt suggests including an example of the shape of your tags to help the model make a more useful guess:

Your task is to create novel, never seen before, furniture, home goods, or hardware classification that best fit a search query.

Product classifications might look like:

Furniture / Living Room Furniture / Coffee Tables & End Tables / Coffee Tables
Décor & Pillows / Decorative Pillows & Blankets / Throw Pillows
Furniture / Bedroom Furniture / Dressers & Chests
Kitchen & Tabletop / Kitchen Organization / Food Storage & Canisters
School Furniture and Supplies / School Furniture / School Chairs & Seating / Stackable Chairs
Baby & Kids / Toddler & Kids Bedroom Furniture / Kids Beds

Here's the query to generate classifications for:

brown coffee table

Tags: search, ai, generative-ai, llms, embeddings, doug-turnbull

DeepSeek V4 Pro 0813 (on OpenRouter)

2026-08-12T23:59:23+00:00

DeepSeek V4 Pro 0813 (on OpenRouter)

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.

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 deepseek-ai/DeepSeek-V4-Pro and July's deepseek-ai/DeepSeek-V4-Flash-0731 it seems likely. Update: the weights are now available on Hugging Face, 1.7T parameters, 893 GB.

Interestingly I got very different looking pelicans for the three different reasoning levels of low, medium, and high. I've not noticed this kind of difference from any other model:

Low:

Medium:

High:

In terms of benchmarks... as far as I can tell those were released to the Official DeepSeek WeChat Group, then copied and pasted into a post on Reddit which was deleted by the moderators for being "low-effort", then copied into this ASCII-art table on Hacker News.

Tags: ai, generative-ai, llms, pelican-riding-a-bicycle, deepseek, llm-release, ai-in-china

There are no lossless transformations of natural-language text

2026-08-11T23:48:35+00:00

There are no lossless transformations of natural-language text

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.

If you chose to have LLMs help massage your writing the following rule seems crucial to me:

You must stand behind every idea and every sentence in your docs. 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.

The "no lossless transformations" idea from the post title is expanded on here:

There are no lossless transformations of natural-language text — every rewrite and rephrase changes the meaning of your writing, and if this is done by an entity that doesn’t have the most detailed mental representation of what you personally were trying to communicate, information will be lost.

Tags: writing, ai, generative-ai, llms, ai-misuse