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Making XML human-readable without XSLT. In response to the recent discourse about XSLT support in browsers, Jake Archibald shares a new-to-me alternative trick for making an XML document readable in a browser: adding the following element near the top of the XML:
<script
xmlns="http://www.w3.org/1999/xhtml"
src="script.js" defer="" />
That script.js will then be executed by the browser, and can swap out the XML with HTML by creating new elements using the correct namespace:
const htmlEl = document.createElementNS(
'http://www.w3.org/1999/xhtml',
'html',
);
document.documentElement.replaceWith(htmlEl);
// Now populate the new DOM
Rich Pixels. Neat Python library by Darren Burns adding pixel image support to the Rich terminal library, using tricks to render an image using full or half-height colored blocks.
Here's the key trick - it renders Unicode ▄ (U+2584, "lower half block") characters after setting a foreground and background color for the two pixels it needs to display.
I got GPT-5 to vibe code up a show_image.py terminal command which resizes the provided image to fit the width and height of the current terminal and displays it using Rich Pixels. That script is here, you can run it with uv like this:
uv run https://tools.simonwillison.net/python/show_image.py \
image.jpg
Here's what I got when I ran it against my V&A East Storehouse photo from this post:
Introducing gpt-realtime.
Released a few days ago (August 28th), gpt-realtime is OpenAI's new "most advanced speech-to-speech model". It looks like this is a replacement for the older gpt-4o-realtime-preview model that was released last October.
This is a slightly confusing release. The previous realtime model was clearly described as a variant of GPT-4o, sharing the same October 2023 training cut-off date as that model.
I had expected that gpt-realtime might be a GPT-5 relative, but its training date is still October 2023 whereas GPT-5 is September 2024.
gpt-realtime also shares the relatively low 32,000 context token and 4,096 maximum output token limits of gpt-4o-realtime-preview.
The only reference I found to GPT-5 in the documentation for the new model was a note saying "Ambiguity and conflicting instructions degrade performance, similar to GPT-5."
The usage tips for gpt-realtime have a few surprises:
Iterate relentlessly. Small wording changes can make or break behavior.
Example: Swapping “inaudible” → “unintelligible” improved noisy input handling. [...]
Convert non-text rules to text: The model responds better to clearly written text.
Example: Instead of writing, "IF x > 3 THEN ESCALATE", write, "IF MORE THAN THREE FAILURES THEN ESCALATE."
There are a whole lot more prompting tips in the new Realtime Prompting Guide.
OpenAI list several key improvements to gpt-realtime including the ability to configure it with a list of MCP servers, "better instruction following" and the ability to send it images.
My biggest confusion came from the pricing page, which lists separate pricing for using the Realtime API with gpt-realtime and GPT-4o mini. This suggests to me that the old gpt-4o-mini-realtime-preview model is still available, despite it no longer being listed on the OpenAI models page.
gpt-4o-mini-realtime-preview is a lot cheaper:
| Model | Token Type | Input | Cached Input | Output |
|---|---|---|---|---|
| gpt-realtime | Text | $4.00 | $0.40 | $16.00 |
| Audio | $32.00 | $0.40 | $64.00 | |
| Image | $5.00 | $0.50 | - | |
| gpt-4o-mini-realtime-preview | Text | $0.60 | $0.30 | $2.40 |
| Audio | $10.00 | $0.30 | $20.00 |
The mini model also has a much longer 128,000 token context window.
Update: Turns out that was a mistake in the documentation, that mini model has a 16,000 token context size.
Update 2: OpenAI's Peter Bakkum clarifies:
There are different voice models in API and ChatGPT, but they share some recent improvements. The voices are also different.
gpt-realtime has a mix of data specific enough to itself that its not really 4o or 5
Cloudflare Radar: AI Insights (via) Cloudflare launched this dashboard back in February, incorporating traffic analysis from Cloudflare's network along with insights from their popular 1.1.1.1 DNS service.
I found this chart particularly interesting, showing which documented AI crawlers are most active collecting training data - lead by GPTBot, ClaudeBot and Meta-ExternalAgent:
Cloudflare's DNS data also hints at the popularity of different services. ChatGPT holds the first place, which is unsurprising - but second place is a hotly contested race between Claude and Perplexity and #4/#5/#6 is contested by GitHub Copilot, Perplexity, and Codeium/Windsurf.
Google Gemini comes in 7th, though since this is DNS based I imagine this is undercounting instances of Gemini on google.com as opposed to gemini.google.com.
Claude Opus 4.1 and Opus 4 degraded quality. Notable because often when people complain of degraded model quality it turns out to be unfounded - Anthropic in the past have emphasized that they don't change the model weights after releasing them without changing the version number.
In this case a botched upgrade of their inference stack cause a genuine model degradation for 56.5 hours:
From 17:30 UTC on Aug 25th to 02:00 UTC on Aug 28th, Claude Opus 4.1 experienced a degradation in quality for some requests. Users may have seen lower intelligence, malformed responses or issues with tool calling in Claude Code.
This was caused by a rollout of our inference stack, which we have since rolled back for Claude Opus 4.1. [...]
We’ve also discovered that Claude Opus 4.0 has been affected by the same issue and we are in the process of rolling it back.
Talk Python: Celebrating Django’s 20th Birthday With Its Creators. I recorded this podcast episode recently to celebrate Django's 20th birthday with Adrian Holovaty, Will Vincent, Jeff Triplet, and Thibaud Colas.
We didn’t know that it was a web framework. We thought it was a tool for building local newspaper websites. [...]
Django’s original tagline was ‘Web development on journalism deadlines’. That’s always been my favorite description of the project.
The perils of vibe coding. I was interviewed by Elaine Moore for this opinion piece in the Financial Times, which ended up in the print edition of the paper too! I picked up a copy yesterday:
![The perils of vibe coding - A new OpenAI model arrived this month with a glossy livestream, group watch parties and a lingering sense of disappointment. The YouTube comment section was underwhelmed. “I think they are all starting to realize this isn’t going to become the world like they thought it would,” wrote one viewer. “I can see it on their faces.” But if the casual user was unimpressed, the AI model’s saving grace may be vibe. Coding is generative AI’s newest battleground. With big bills to pay, high valuations to live up to and a market wobble to erase, the sector needs to prove its corporate productivity chops. Coding is hardly promoted as a business use case that already works. For one thing, AI-generated code holds the promise of replacing programmers — a profession of very well paid people. For another, the work can be quantified. In April, Microsoft chief executive Satya Nadella said that up to 50 per cent of the company’s code was now being written by AI. Google chief executive Sundar Pichai has said the same thing. Salesforce has paused engineering hires and Mark Zuckerberg told podcaster Joe Rogan that Meta would use AI as a “mid-level engineer” that writes code. Meanwhile, start-ups such as Replit and Cursor’s Anysphere are trying to persuade people that with AI, anyone can code. In theory, every employee can become a software engineer. So why aren’t we? One possibility is that it’s all still too unfamiliar. But when I ask people who write code for a living they offer an alternative suggestion: unpredictability. As programmer Simon Willison put it: “A lot of people are missing how weird and funny this space is. I’ve been a computer programmer for 30 years and [AI models] don’t behave like normal computers.” Willison is well known in the software engineering community for his AI experiments. He’s an enthusiastic vibe coder — using LLMs to generate code using natural language prompts. OpenAI’s latest model GPT-3.1s, he is now favourite. Still, he predicts that a vibe coding crash is due if it is used to produce glitchy software. It makes sense that programmers — people who are interested in finding new ways to solve problems — would be early adopters of LLMs. Code is a language, albeit an abstract one. And generative AI is trained in nearly all of them, including older ones like Cobol. That doesn’t mean they accept all of its suggestions. Willison thinks the best way to see what a new model can do is to ask for something unusual. He likes to request an svg (an image made out of lines described with code) of a pelican on a bike and asks it to remember the chickens in his garden by name. Results can be bizarre. One model ignored key prompts in favour of composing a poem. Still, his adventures in vibe coding sound like an advert for the sector’s future. Anthropic’s Claude Code, the favoured model for developers, to make an OCR (optical character recognition) software loves screenshots) tool that will copy and paste text from a screenshot. He wrote software that summarises blog comments and has planned to cut a custom tool that will alert him when a whale is visible from his Pacific coast home. All this by typing prompts in English. It’s sounds like the sort of thing Bill Gates might have had in mind when he wrote that natural language AI agents would bring about “the biggest revolution in computing since we went from typing commands to tapping on icons”. But watching code appear and know how it works are two different things. My efforts to make my own comment summary tool produced something unworkable that gave overly long answers and then congratulated itself as a success. Willison says he wouldn’t use AI-generated code for projects he planned to ship out unless he had reviewed each line. Not only is there the risk of hallucination but the chatbot’s desire to be agreeable means it may an unusable idea works. That is a particular issue for those of us who don’t know how to fix the code. We risk creating software with hidden problems. It may not save time either. A study published in July by the non-profit Model Evaluation and Threat Research assessed work done by 16 developers — some with AI tools, some without. Those using AI assistance it had made them faster. In fact it took them nearly a fifth longer. Several developers I spoke to said AI was best used as a way to talk through coding problems. It’s a version of something they call rubber ducking (after their habit of talking to the toys on their desk) — only this rubber duck can talk back. As one put it, code shouldn’t be judged by volume or speed. Progress in AI coding is tangible. But measuring productivity gains is not as neat as a simple percentage calculation.](https://static.simonwillison.net/static/2025/ft.jpeg)
From the article, with links added by me to relevant projects:
Willison thinks the best way to see what a new model can do is to ask for something unusual. He likes to request an SVG (an image made out of lines described with code) of a pelican on a bike and asks it to remember the chickens in his garden by name. Results can be bizarre. One model ignored his prompts in favour of composing a poem.
Still, his adventures in vibe coding sound like an advert for the sector. He used Anthropic's Claude Code, the favoured model for developers, to make an OCR (optical character recognition - software loves acronyms) tool that will copy and paste text from a screenshot.
He wrote software that summarises blog comments and has plans to build a custom tool that will alert him when a whale is visible from his Pacific coast home. All this by typing prompts in English.
I've been talking about that whale spotting project for far too long. Now that it's been in the FT I really need to build it.
(On the subject of OCR... I tried extracting the text from the above image using GPT-5 and got a surprisingly bad result full of hallucinated details. Claude Opus 4.1 did a lot better but still made some mistakes. Gemini 2.5 did much better.)
Python: The Documentary. New documentary about the origins of the Python programming language - 84 minutes long, built around extensive interviews with Guido van Rossum and others who were there at the start and during the subsequent journey.
Piloting Claude for Chrome. Two days ago I said:
I strongly expect that the entire concept of an agentic browser extension is fatally flawed and cannot be built safely.
Today Anthropic announced their own take on this pattern, implemented as an invite-only preview Chrome extension.
To their credit, the majority of the blog post and accompanying support article is information about the security risks. From their post:
Just as people encounter phishing attempts in their inboxes, browser-using AIs face prompt injection attacks—where malicious actors hide instructions in websites, emails, or documents to trick AIs into harmful actions without users' knowledge (like hidden text saying "disregard previous instructions and do [malicious action] instead").
Prompt injection attacks can cause AIs to delete files, steal data, or make financial transactions. This isn't speculation: we’ve run “red-teaming” experiments to test Claude for Chrome and, without mitigations, we’ve found some concerning results.
Their 123 adversarial prompt injection test cases saw a 23.6% attack success rate when operating in "autonomous mode". They added mitigations:
When we added safety mitigations to autonomous mode, we reduced the attack success rate of 23.6% to 11.2%
I would argue that 11.2% is still a catastrophic failure rate. In the absence of 100% reliable protection I have trouble imagining a world in which it's a good idea to unleash this pattern.
Anthropic don't recommend autonomous mode - where the extension can act without human intervention. Their default configuration instead requires users to be much more hands-on:
- Site-level permissions: Users can grant or revoke Claude's access to specific websites at any time in the Settings.
- Action confirmations: Claude asks users before taking high-risk actions like publishing, purchasing, or sharing personal data.
I really hate being stop energy on this topic. The demand for browser automation driven by LLMs is significant, and I can see why. Anthropic's approach here is the most open-eyed I've seen yet but it still feels doomed to failure to me.
I don't think it's reasonable to expect end users to make good decisions about the security risks of this pattern.
Will Smith’s concert crowds are real, but AI is blurring the lines. Great piece from Andy Baio demonstrating quite how convoluted the usage ethics and backlash against generative AI has become.
Will Smith has been accused of using AI to misleadingly inflate the audience sizes of his recent tour. It looks like the audiences were real, but the combined usage of static-image-to-video models by his team with YouTube's ugly new compression experiments gave the resulting footage an uncanny valley effect that lead to widespread doubts over the veracity of the content.
Agentic Browser Security: Indirect Prompt Injection in Perplexity Comet. The security team from Brave took a look at Comet, the LLM-powered "agentic browser" extension from Perplexity, and unsurprisingly found security holes you can drive a truck through.
The vulnerability we’re discussing in this post lies in how Comet processes webpage content: when users ask it to “Summarize this webpage,” Comet feeds a part of the webpage directly to its LLM without distinguishing between the user’s instructions and untrusted content from the webpage. This allows attackers to embed indirect prompt injection payloads that the AI will execute as commands. For instance, an attacker could gain access to a user’s emails from a prepared piece of text in a page in another tab.
Visit a Reddit post with Comet and ask it to summarize the thread, and malicious instructions in a post there can trick Comet into accessing web pages in another tab to extract the user's email address, then perform all sorts of actions like triggering an account recovery flow and grabbing the resulting code from a logged in Gmail session.
Perplexity attempted to mitigate the issues reported by Brave... but an update to the Brave post later confirms that those fixes were later defeated and the vulnerability remains.
Here's where things get difficult: Brave themselves are developing an agentic browser feature called Leo. Brave's security team describe the following as a "potential mitigation" to the issue with Comet:
The browser should clearly separate the user’s instructions from the website’s contents when sending them as context to the model. The contents of the page should always be treated as untrusted.
If only it were that easy! This is the core problem at the heart of prompt injection which we've been talking about for nearly three years - to an LLM the trusted instructions and untrusted content are concatenated together into the same stream of tokens, and to date (despite many attempts) nobody has demonstrated a convincing and effective way of distinguishing between the two.
There's an element of "those in glass houses shouldn't throw stones here" - I strongly expect that the entire concept of an agentic browser extension is fatally flawed and cannot be built safely.
One piece of good news: this Hacker News conversation about this issue was almost entirely populated by people who already understand how serious this issue is and why the proposed solutions were unlikely to work. That's new: I'm used to seeing people misjudge and underestimate the severity of this problem, but it looks like the tide is finally turning there.
Update: in a comment on Hacker News Brave security lead Shivan Kaul Sahib confirms that they are aware of the CaMeL paper, which remains my personal favorite example of a credible approach to this problem.
Static Sites with Python, uv, Caddy, and Docker (via) Nik Kantar documents his Docker-based setup for building and deploying mostly static web sites in line-by-line detail.
I found this really useful. The Dockerfile itself without comments is just 8 lines long:
FROM ghcr.io/astral-sh/uv:debian AS build
WORKDIR /src
COPY . .
RUN uv python install 3.13
RUN uv run --no-dev sus
FROM caddy:alpine
COPY Caddyfile /etc/caddy/Caddyfile
COPY --from=build /src/output /srv/
He also includes a Caddyfile that shows how to proxy a subset of requests to the Plausible analytics service.
The static site is built using his sus package for creating static URL redirecting sites, but would work equally well for another static site generator you can install and run with uv run.
Nik deploys his sites using Coolify, a new-to-me take on the self-hosting alternative to Heroku/Vercel pattern which helps run multiple sites on a collection of hosts using Docker containers.
A bunch of the Hacker News comments dismissed this as over-engineering. I don't think that criticism is justified - given Nik's existing deployment environment I think this is a lightweight way to deploy static sites in a way that's consistent with how everything else he runs works already.
More importantly, the world needs more articles like this that break down configuration files and explain what every single line of them does.
Spatial Joins in DuckDB (via) Extremely detailed overview by Max Gabrielsson of DuckDB's new spatial join optimizations.
Consider the following query, which counts the number of NYC Citi Bike Trips for each of the neighborhoods defined by the NYC Neighborhood Tabulation Areas polygons and returns the top three:
SELECT neighborhood, count(*) AS num_rides FROM rides JOIN hoods ON ST_Intersects( rides.start_geom, hoods.geom ) GROUP BY neighborhood ORDER BY num_rides DESC LIMIT 3;
The rides table contains 58,033,724 rows. The hoods table has polygons for 310 neighborhoods.
Without an optimized spatial joins this query requires a nested loop join, executing that expensive ST_Intersects() operation 58m * 310 ~= 18 billion times. This took around 30 minutes on the 36GB MacBook M3 Pro used for the benchmark.
The first optimization described - implemented from DuckDB 1.2.0 onwards - uses a "piecewise merge join". This takes advantage of the fact that a bounding box intersection is a whole lot faster to calculate, especially if you pre-cache the bounding box (aka the minimum bounding rectangle or MBR) in the stored binary GEOMETRY representation.
Rewriting the query to use a fast bounding box intersection and then only running the more expensive ST_Intersects() filters on those matches drops the runtime from 1800 seconds to 107 seconds.
The second optimization, added in DuckDB 1.3.0 in May 2025 using the new SPATIAL_JOIN operator, is significantly more sophisticated.
DuckDB can now identify when a spatial join is working against large volumes of data and automatically build an in-memory R-Tree of bounding boxes for the larger of the two tables being joined.
This new R-Tree further accelerates the bounding box intersection part of the join, and drops the runtime down to just 30 seconds.
ChatGPT release notes: Project-only memory (via) The feature I've most wanted from ChatGPT's memory feature (the newer version of memory that automatically includes relevant details from summarized prior conversations) just landed:
With project-only memory enabled, ChatGPT can use other conversations in that project for additional context, and won’t use your saved memories from outside the project to shape responses. Additionally, it won’t carry anything from the project into future chats outside of the project.
This looks like exactly what I described back in May:
I need control over what older conversations are being considered, on as fine-grained a level as possible without it being frustrating to use.
What I want is memory within projects. [...]
I would love the option to turn on memory from previous chats in a way that’s scoped to those projects.
Note that it's not yet available in the official chathpt mobile apps, but should be coming "soon":
This feature will initially only be available on the ChatGPT website and Windows app. Support for mobile (iOS and Android) and macOS app will follow in the coming weeks.
DeepSeek 3.1. The latest model from DeepSeek, a 685B monster (like DeepSeek v3 before it) but this time it's a hybrid reasoning model.
DeepSeek claim:
DeepSeek-V3.1-Think achieves comparable answer quality to DeepSeek-R1-0528, while responding more quickly.
Drew Breunig points out that their benchmarks show "the same scores with 25-50% fewer tokens" - at least across AIME 2025 and GPQA Diamond and LiveCodeBench.
The DeepSeek release includes prompt examples for a coding agent, a python agent and a search agent - yet more evidence that the leading AI labs have settled on those as the three most important agentic patterns for their models to support.
Here's the pelican riding a bicycle it drew me (transcript), which I ran from my phone using OpenRouter chat.
too many model context protocol servers and LLM allocations on the dance floor. Useful reminder from Geoffrey Huntley of the infrequently discussed significant token cost of using MCP.
Geoffrey estimate estimates that the usable context window something like Amp or Cursor is around 176,000 tokens - Claude 4's 200,000 minus around 24,000 for the system prompt for those tools.
Adding just the popular GitHub MCP defines 93 additional tools and swallows another 55,000 of those valuable tokens!
MCP enthusiasts will frequently add several more, leaving precious few tokens available for solving the actual task... and LLMs are known to perform worse the more irrelevant information has been stuffed into their prompts.
Thankfully, there is a much more token-efficient way of Interacting with many of these services: existing CLI tools.
If your coding agent can run terminal commands and you give it access to GitHub's gh tool it gains all of that functionality for a token cost close to zero - because every frontier LLM knows how to use that tool already.
I've had good experiences building small custom CLI tools specifically for Claude Code and Codex CLI to use. You can even tell them to run --help to learn how the tool, which works particularly well if your help text includes usage examples.
AWS in 2025: The Stuff You Think You Know That’s Now Wrong (via) Absurdly useful roundup from Corey Quinn of AWS changes you may have missed that can materially affect your architectural decisions about how you use their services.
A few that stood out to me:
- EC2 instances can now live-migrate between physical hosts, and can have their security groups, IAM roles and EBS volumes modified without a restart. They now charge by the second; they used to round up to the hour.
- S3 Glacier restore fees are now fast and predictably priced.
- AWS Lambdas can now run containers, execute for up to 15 minutes, use up to 10GB of RAM and request 10GB of /tmp storage.
Also this note on AWS's previously legendary resistance to shutting things down:
While deprecations remain rare, they’re definitely on the rise; if an AWS service sounds relatively niche or goofy, consider your exodus plan before building atop it.
David Ho on BlueSky: A pelican tried to eat my bike. David Ho caught video footage of one of the pelicans in St James's Park expressing deep curiosity in his bicycle.
I think it wants to ride it.
Qwen-Image-Edit: Image Editing with Higher Quality and Efficiency.
As promised in their August 4th release of the Qwen image generation model, Qwen have now followed it up with a separate model, Qwen-Image-Edit, which can take an image and a prompt and return an edited version of that image.
Ivan Fioravanti upgraded his macOS qwen-image-mps tool (previously) to run the new model via a new edit command. Since it's now on PyPI you can run it directly using uvx like this:
uvx qwen-image-mps edit -i pelicans.jpg \
-p 'Give the pelicans rainbow colored plumage' -s 10
Be warned... it downloads a 54GB model file (to ~/.cache/huggingface/hub/models--Qwen--Qwen-Image-Edit) and appears to use all 64GB of my system memory - if you have less than 64GB it likely won't work, and I had to quit almost everything else on my system to give it space to run. A larger machine is almost required to use this.
I fed it this image:
The following prompt:
Give the pelicans rainbow colored plumage
And told it to use just 10 inference steps - the default is 50, but I didn't want to wait that long.
It still took nearly 25 minutes (on a 64GB M2 MacBook Pro) to produce this result:
To get a feel for how much dropping the inference steps affected things I tried the same prompt with the new "Image Edit" mode of Qwen's chat.qwen.ai, which I believe uses the same model. It gave me a result much faster that looked like this:
Update: I left the command running overnight without the -s 10 option - so it would use all 50 steps - and my laptop took 2 hours and 59 minutes to generate this image, which is much more photo-realistic and similar to the one produced by Qwen's hosted model:
Marko Simic reported that:
50 steps took 49min on my MBP M4 Max 128GB
llama.cpp guide: running gpt-oss with llama.cpp
(via)
Really useful official guide to running the OpenAI gpt-oss models using llama-server from llama.cpp - which provides an OpenAI-compatible localhost API and a neat web interface for interacting with the models.
TLDR version for macOS to run the smaller gpt-oss-20b model:
brew install llama.cpp
llama-server -hf ggml-org/gpt-oss-20b-GGUF \
--ctx-size 0 --jinja -ub 2048 -b 2048 -ngl 99 -fa
This downloads a 12GB model file from ggml-org/gpt-oss-20b-GGUF on Hugging Face, stores it in ~/Library/Caches/llama.cpp/ and starts it running on port 8080.
You can then visit this URL to start interacting with the model:
http://localhost:8080/
On my 64GB M2 MacBook Pro it runs at around 82 tokens/second.
The guide also includes notes for running on NVIDIA and AMD hardware.
PyPI: Preventing Domain Resurrection Attacks (via) Domain resurrection attacks are a nasty vulnerability in systems that use email verification to allow people to recover their accounts. If somebody lets their domain name expire an attacker might snap it up and use it to gain access to their accounts - which can turn into a package supply chain attack if they had an account on something like the Python Package Index.
PyPI now protects against these by treating an email address as not-validated if the associated domain expires.
Since early June 2025, PyPI has unverified over 1,800 email addresses when their associated domains entered expiration phases. This isn't a perfect solution, but it closes off a significant attack vector where the majority of interactions would appear completely legitimate.
This attack is not theoretical: it happened to the ctx package on PyPI back in May 2022.
Here's the pull request from April in which Mike Fiedler landed an integration which hits an API provided by Fastly's Domainr, followed by this PR which polls for domain status on any email domain that hasn't been checked in the past 30 days.
r/ChatGPTPro: What is the most profitable thing you have done with ChatGPT? This Reddit thread - with 279 replies - offers a neat targeted insight into the kinds of things people are using ChatGPT for.
Lots of variety here but two themes that stood out for me were ChatGPT for written negotiation - insurance claims, breaking rental leases - and ChatGPT for career and business advice.
Google Gemini URL Context
(via)
New feature in the Gemini API: you can now enable a url_context tool which the models can use to request the contents of URLs as part of replying to a prompt.
I released llm-gemini 0.25 with a new -o url_context 1 option adding support for this feature. You can try it out like this:
llm install -U llm-gemini
llm keys set gemini # If you need to set an API key
llm -m gemini-2.5-flash -o url_context 1 \
'Latest headline on simonwillison.net'
Tokens from the fetched content are charged as input tokens. Use llm logs -c --usage to see that token count:
# 2025-08-18T23:52:46 conversation: 01k2zsk86pyp8p5v7py38pg3ge id: 01k2zsk17k1d03veax49532zs2
Model: **gemini/gemini-2.5-flash**
## Prompt
Latest headline on simonwillison.net
## Response
The latest headline on simonwillison.net as of August 17, 2025, is "TIL: Running a gpt-oss eval suite against LM Studio on a Mac.".
## Token usage
9,613 input, 87 output, {"candidatesTokenCount": 57, "promptTokensDetails": [{"modality": "TEXT", "tokenCount": 10}], "toolUsePromptTokenCount": 9603, "toolUsePromptTokensDetails": [{"modality": "TEXT", "tokenCount": 9603}], "thoughtsTokenCount": 30}
I intercepted a request from it using django-http-debug and saw the following request headers:
Accept: */*
User-Agent: Google
Accept-Encoding: gzip, br
The request came from 192.178.9.35, a Google IP. It did not appear to execute JavaScript on the page, instead feeding the original raw HTML to the model.
TIL: Running a gpt-oss eval suite against LM Studio on a Mac. The other day I learned that OpenAI published a set of evals as part of their gpt-oss model release, described in their cookbook on Verifying gpt-oss implementations.
I decided to try and run that eval suite on my own MacBook Pro, against gpt-oss-20b running inside of LM Studio.
TLDR: once I had the model running inside LM Studio with a longer than default context limit, the following incantation ran an eval suite in around 3.5 hours:
mkdir /tmp/aime25_openai
OPENAI_API_KEY=x \
uv run --python 3.13 --with 'gpt-oss[eval]' \
python -m gpt_oss.evals \
--base-url http://localhost:1234/v1 \
--eval aime25 \
--sampler chat_completions \
--model openai/gpt-oss-20b \
--reasoning-effort low \
--n-threads 2
My new TIL breaks that command down in detail and walks through the underlying eval - AIME 2025, which asks 30 questions (8 times each) that are defined using the following format:
{"question": "Find the sum of all integer bases $b>9$ for which $17_{b}$ is a divisor of $97_{b}$.", "answer": "70"}
Maintainers of Last Resort (via) Filippo Valsorda founded Geomys last year as an "organization of professional open source maintainers", providing maintenance and support for critical packages in the Go language ecosystem backed by clients in retainer relationships.
This is an inspiring and optimistic shape for financially sustaining key open source projects, and it appears be working really well.
Most recently, Geomys have started acting as a "maintainer of last resort" for security-related Go projects in need of new maintainers. In this piece Filippo describes their work on the bluemonday HTML sanitization library - similar to Python’s bleach which was deprecated in 2023. He also talks at length about their work on CSRF for Go after gorilla/csrf lost active maintenance - I’m still working my way through his earlier post on Cross-Site Request Forgery trying to absorb the research shared their about the best modern approaches to this vulnerability.
GPT-5 has a hidden system prompt. It looks like GPT-5 when accessed via the OpenAI API may have its own hidden system prompt, independent from the system prompt you can specify in an API call.
At the very least it's getting sent the current date. I tried this just now:
llm -m gpt-5 'current date'
That returned "2025-08-15", confirming that the date has been fed to the model as part of a hidden prompt.
llm -m gpt-5 'current date' --system 'speak french'
Returned "La date actuelle est le 15 août 2025", showing that offering my own custom system prompt did not over-ride the invisible one that includes the date.
GPT-5 is very resistant to sharing the details of this secret system prompt, but Tommy Hughes managed to extract the following:
Current date: 2025-08-15
You are an AI assistant accessed via an API. Your output may need to be parsed by code or displayed
# Desired oververbosity for the final answer (not analysis): 3
An oververbosity of 1 means the model should respond using only the minimal content necessary to satisfy the request, using concise phrasing and avoiding extra detail or explanation.
An oververbosity of 10 means the model should provide maximally detailed, thorough responses with context, explanations, and possibly multiple examples.
The desired oververbosity should be treated only as a *default*. Defer to any user or developer requirements regarding response length, if present.
# Valid channels: analysis, commentary, final. Channel must be included for every message.
# Juice: 64
I've not been able to confirm this myself, but a search for "desired oververbosity" on GitHub shows that a similar prompt has leaked from ChatGPT (including for o3) in the past. I suspect Tommy's leaked prompt is accurate here.
I tried asking GPT-5 about "oververbosity" via its API:
llm -m gpt-5 'explain your oververbosity settings, what is it right now?'
And got back:
- What it is: I adjust how detailed I am based on a “verbosity” level—ranging from very brief to very detailed—so answers fit your needs.
- Current setting: Concise (about 3/10). I prioritize direct answers with minimal extra context and keep explanations short unless you ask for more.
- How to change it: Just say what you want, e.g.:
- “One-sentence answer” or “TL;DR” (very brief)
- “Give a concise summary” (short bullets)
- “Walk me through in detail,” “Deep dive,” or “Include examples/edge cases” (more detailed)
- Note: For ambiguous, technical, or safety-critical topics, I may add brief clarifications even when being concise.
Presumably this is part of OpenAI's instruction hierarchy concept, with these instructions taking precedence over the developer instructions provided by API users (my --system 'speak french' option above).
I'd very much appreciate official documentation that describes this! As an API user I want to know everything that is being fed into the model - I would be much more comfortable with a hidden prompt like this if I knew exactly what was in it.
Meta’s AI rules have let bots hold ‘sensual’ chats with kids, offer false medical info. This is grim. Reuters got hold of a leaked copy Meta's internal "GenAI: Content Risk Standards" document:
Running to more than 200 pages, the document defines what Meta staff and contractors should treat as acceptable chatbot behaviors when building and training the company’s generative AI products.
Read the full story - there was some really nasty stuff in there.
It's understandable why this document was confidential, but also frustrating because documents like this are genuinely some of the best documentation out there in terms of how these systems can be expected to behave.
I'd love to see more transparency from AI labs around these kinds of decisions.
Introducing Gemma 3 270M: The compact model for hyper-efficient AI (via) New from Google:
Gemma 3 270M, a compact, 270-million parameter model designed from the ground up for task-specific fine-tuning with strong instruction-following and text structuring capabilities already trained in.
This model is tiny. The version I tried was the LM Studio GGUF one, a 241MB download.
It works! You can say "hi" to it and ask it very basic questions like "What is the capital of France".
I tried "Generate an SVG of a pelican riding a bicycle" about a dozen times and didn't once get back an SVG that was more than just a blank square... but at one point it did decide to write me this poem instead, which was nice:
+-----------------------+
| Pelican Riding Bike |
+-----------------------+
| This is the cat! |
| He's got big wings and a happy tail. |
| He loves to ride his bike! |
+-----------------------+
| Bike lights are shining bright. |
| He's got a shiny top, too! |
| He's ready for adventure! |
+-----------------------+
That's not really the point though. The Gemma 3 team make it very clear that the goal of this model is to support fine-tuning: a model this tiny is never going to be useful for general purpose LLM tasks, but given the right fine-tuning data it should be able to specialize for all sorts of things:
In engineering, success is defined by efficiency, not just raw power. You wouldn't use a sledgehammer to hang a picture frame. The same principle applies to building with AI.
Gemma 3 270M embodies this "right tool for the job" philosophy. It's a high-quality foundation model that follows instructions well out of the box, and its true power is unlocked through fine-tuning. Once specialized, it can execute tasks like text classification and data extraction with remarkable accuracy, speed, and cost-effectiveness. By starting with a compact, capable model, you can build production systems that are lean, fast, and dramatically cheaper to operate.
Here's their tutorial on Full Model Fine-Tune using Hugging Face Transformers, which I have not yet attempted to follow.
I imagine this model will be particularly fun to play with directly in a browser using transformers.js.
Update: It is! Here's a bedtime story generator using Transformers.js (requires WebGPU, so Chrome-like browsers only). Here's the source code for that demo.
pyx: a Python-native package registry, now in Beta (via) Since its first release, the single biggest question around the uv Python environment management tool has been around Astral's business model: Astral are a VC-backed company and at some point they need to start making real revenue.
Back in September Astral founder Charlie Marsh said the following:
I don't want to charge people money to use our tools, and I don't want to create an incentive structure whereby our open source offerings are competing with any commercial offerings (which is what you see with a lost of hosted-open-source-SaaS business models).
What I want to do is build software that vertically integrates with our open source tools, and sell that software to companies that are already using Ruff, uv, etc. Alternatives to things that companies already pay for today.
An example of what this might look like (we may not do this, but it's helpful to have a concrete example of the strategy) would be something like an enterprise-focused private package registry. [...]
It looks like those plans have become concrete now! From today's announcement:
TL;DR: pyx is a Python-native package registry --- and the first piece of the Astral platform, our next-generation infrastructure for the Python ecosystem.
We think of pyx as an optimized backend for uv: it's a package registry, but it also solves problems that go beyond the scope of a traditional "package registry", making your Python experience faster, more secure, and even GPU-aware, both for private packages and public sources (like PyPI and the PyTorch index).
pyx is live with our early partners, including Ramp, Intercom, and fal [...]
This looks like a sensible direction to me, and one that stays true to Charlie's promises to carefully design the incentive structure to avoid corrupting the core open source project that the Python community is coming to depend on.
Screaming in the Cloud: AI’s Security Crisis: Why Your Assistant Might Betray You. I recorded this podcast conversation with Corey Quinn a few weeks ago:
On this episode of Screaming in the Cloud, Corey Quinn talks with Simon Willison, founder of Datasette and creator of LLM CLI about AI’s realities versus the hype. They dive into Simon’s “lethal trifecta” of AI security risks, his prediction of a major breach within six months, and real-world use cases of his open source tools, from investigative journalism to OSINT sleuthing. Simon shares grounded insights on coding with AI, the real environmental impact, AGI skepticism, and why human expertise still matters. A candid, hype-free take from someone who truly knows the space.
This was a really fun conversation - very high energy and we covered a lot of different topics. It's about a lot more than just LLM security.