Simon Willison’s Weblog

nanochat (via) Really interesting new project from Andrej Karpathy, described at length in this discussion post.

It provides a full ChatGPT-style LLM, including training, inference and a web Ui, that can be trained for as little as $100:

This repo is a full-stack implementation of an LLM like ChatGPT in a single, clean, minimal, hackable, dependency-lite codebase.

It's around 8,000 lines of code, mostly Python (using PyTorch) plus a little bit of Rust for training the tokenizer.

Andrej suggests renting a 8XH100 NVIDA node for around $24/ hour to train the model. 4 hours (~$100) is enough to get a model that can hold a conversation - almost coherent example here. Run it for 12 hours and you get something that slightly outperforms GPT-2. I'm looking forward to hearing results from longer training runs!

The resulting model is ~561M parameters, so it should run on almost anything. I've run a 4B model on my iPhone, 561M should easily fit on even an inexpensive Raspberry Pi.

The model defaults to training on ~24GB from karpathy/fineweb-edu-100b-shuffle derived from FineWeb-Edu, and then midtrains on 568K examples from SmolTalk (460K), MMLU auxiliary train (100K), and GSM8K (8K), followed by supervised finetuning on 21.4K examples from ARC-Easy (2.3K), ARC-Challenge (1.1K), GSM8K (8K), and SmolTalk (10K).

Here's the code for the web server, which is fronted by this pleasantly succinct vanilla JavaScript HTML+JavaScript frontend.

Update: Sam Dobson pushed a build of the model to sdobson/nanochat on Hugging Face. It's designed to run on CUDA but I pointed Claude Code at a checkout and had it hack around until it figured out how to run it on CPU on macOS, which eventually resulted in this script which I've published as a Gist. You should be able to try out the model using uv like this:

cd /tmp
git clone https://huggingface.co/sdobson/nanochat
uv run https://gist.githubusercontent.com/simonw/912623bf00d6c13cc0211508969a100a/raw/80f79c6a6f1e1b5d4485368ef3ddafa5ce853131/generate_cpu.py \
--model-dir /tmp/nanochat \
--prompt "Tell me about dogs."

I got this (truncated because it ran out of tokens):

I'm delighted to share my passion for dogs with you. As a veterinary doctor, I've had the privilege of helping many pet owners care for their furry friends. There's something special about training, about being a part of their lives, and about seeing their faces light up when they see their favorite treats or toys.

I've had the chance to work with over 1,000 dogs, and I must say, it's a rewarding experience. The bond between owner and pet

# 13th October 2025, 8:29 pm / gpus, training-data, ai, llms, andrej-karpathy, rust, python, generative-ai, pytorch, uv, claude-code

Vibing a Non-Trivial Ghostty Feature (via) Mitchell Hashimoto provides a comprehensive answer to the frequent demand for a detailed description of shipping a non-trivial production feature to an existing project using AI-assistance. In this case it's a slick unobtrusive auto-update UI for his Ghostty terminal emulator, written in Swift.

Mitchell shares full transcripts of the 16 coding sessions he carried out using Amp Code across 2 days and around 8 hours of computer time, at a token cost of $15.98.

Amp has the nicest shared transcript feature of any of the coding agent tools, as seen in this example. I'd love to see Claude Code and Codex CLI and Gemini CLI and friends imitate this.

There are plenty of useful tips in here. I like this note about the importance of a cleanup step:

The cleanup step is really important. To cleanup effectively you have to have a pretty good understanding of the code, so this forces me to not blindly accept AI-written code. Subsequently, better organized and documented code helps future agentic sessions perform better.

I sometimes tongue-in-cheek refer to this as the "anti-slop session".

And this on how sometimes you can write manual code in a way that puts the agent the right track:

I spent some time manually restructured the view model. This involved switching to a tagged union rather than the struct with a bunch of optionals. I renamed some types, moved stuff around.

I knew from experience that this small bit of manual work in the middle would set the agents up for success in future sessions for both the frontend and backend. After completing it, I continued with a marathon set of cleanup sessions.

Here's one of those refactoring prompts:

Turn each @macos/Sources/Features/Update/UpdatePopoverView.swift case into a dedicated fileprivate Swift view that takes the typed value as its parameter so that we can remove the guards.

Mitchell advises ending every session with a prompt like this one, asking the agent about any obvious omissions:

Are there any other improvements you can see to be made with the @macos/Sources/Features/Update feature? Don't write any code. Consult the oracle. Consider parts of the code that can also get more unit tests added.

("Consult the oracle" is an Amp-specific pattern for running a task through a more expensive, more capable model.)

Is this all worthwhile? Mitchell thinks so:

Many people on the internet argue whether AI enables you to work faster or not. In this case, I think I shipped this faster than I would have if I had done it all myself, in particular because iterating on minor SwiftUI styling is so tedious and time consuming for me personally and AI does it so well.

I think the faster/slower argument for me personally is missing the thing I like the most: the AI can work for me while I step away to do other things.

Here's the resulting PR, which touches 21 files.

# 11th October 2025, 4:33 pm / coding-agents, ai-assisted-programming, generative-ai, ai, mitchell-hashimoto, llms

simonw/claude-skills. One of the tips I picked up from Jesse Vincent's Claude Code Superpowers post (previously) was this:

Skills are what give your agents Superpowers.

The first time they really popped up on my radar was a few weeks ago when Anthropic rolled out improved Office document creation. When the feature rolled out, I went poking around a bit -- I asked Claude to tell me all about its new skills. And it was only too happy to dish.

In that transcript Jesse asks how the skill system works and Claude replies:

Looking at the skills system, here's how it works:

Current Skills (in /mnt/skills/public/):

• docx - Word document handling
• pdf - PDF processing and form filling
• pptx - PowerPoint presentations
• xlsx - Excel spreadsheets

This looks like it's part of Claude's brand new Code Interpreter feature! I wrote about that extensively last month, but I missed that there was a /mnt/skills/public/ folder full of fascinating implementation details.

So I fired up a fresh Claude instance (fun fact: Code Interpreter also works in the Claude iOS app now, which it didn't when they first launched) and prompted:

Create a zip file of everything in your /mnt/skills folder

This worked, and gave me a .zip to download. You can run the prompt yourself here, though you'll need to enable the new feature first.

I've pushed the contents of that zip to my new simonw/claude-skills GitHub repo.

So now you can see the prompts Anthropic wrote to enable the creation and manipulation of the following files in their Claude consumer applications:

• pdf - PDF files
• docx - Microsoft Word
• pptx - Microsoft PowerPoint decks
• xlsx - Microsoft Excel

In each case the prompts spell out detailed instructions for manipulating those file types using Python, using libraries that come pre-installed on Claude's containers.

Skills are more than just prompts though: the repository also includes dozens of pre-written Python scripts for performing common operations.

pdf/scripts/fill_fillable_fields.py for example is a custom CLI tool that uses pypdf to find and then fill in a bunch of PDF form fields, specified as JSON, then render out the resulting combined PDF.

This is a really sophisticated set of tools for document manipulation, and I love that Anthropic have made those visible - presumably deliberately - to users of Claude who know how to ask for them.

# 10th October 2025, 11:57 pm / jesse-vincent, prompt-engineering, claude, generative-ai, code-interpreter, ai, llms, anthropic, pdf, python

Superpowers: How I’m using coding agents in October 2025. A follow-up to Jesse Vincent's post about September, but this is a really significant piece in its own right.

Jesse is one of the most creative users of coding agents (Claude Code in particular) that I know. He's put a great amount of work into evolving an effective process for working with them, encourage red/green TDD (watch the test fail first), planning steps, self-updating memory notes and even implementing a feelings journal ("I feel engaged and curious about this project" - Claude).

Claude Code just launched plugins, and Jesse is celebrating by wrapping up a whole host of his accumulated tricks as a new plugin called Superpowers. You can add it to your Claude Code like this:

/plugin marketplace add obra/superpowers-marketplace
/plugin install superpowers@superpowers-marketplace

There's a lot in here! It's worth spending some time browsing the repository - here's just one fun example, in skills/debugging/root-cause-tracing/SKILL.md:

---
name: Root Cause Tracing
description: Systematically trace bugs backward through call stack to find original trigger
when_to_use: Bug appears deep in call stack but you need to find where it originates
version: 1.0.0
languages: all
---

Overview

Bugs often manifest deep in the call stack (git init in wrong directory, file created in wrong location, database opened with wrong path). Your instinct is to fix where the error appears, but that's treating a symptom.

Core principle: Trace backward through the call chain until you find the original trigger, then fix at the source.

When to Use

digraph when_to_use {
    "Bug appears deep in stack?" [shape=diamond];
    "Can trace backwards?" [shape=diamond];
    "Fix at symptom point" [shape=box];
    "Trace to original trigger" [shape=box];
    "BETTER: Also add defense-in-depth" [shape=box];

    "Bug appears deep in stack?" -> "Can trace backwards?" [label="yes"];
    "Can trace backwards?" -> "Trace to original trigger" [label="yes"];
    "Can trace backwards?" -> "Fix at symptom point" [label="no - dead end"];
    "Trace to original trigger" -> "BETTER: Also add defense-in-depth";
}

[...]

This one is particularly fun because it then includes a Graphviz DOT graph illustrating the process - it turns out Claude can interpret those as workflow instructions just fine, and Jesse has been wildly experimenting with them.

I vibe-coded up a quick URL-based DOT visualizer, here's that one rendered:

There is so much to learn about putting these tools to work in the most effective way possible. Jesse is way ahead of the curve, so it's absolutely worth spending some time exploring what he's shared so far.

And if you're worried about filling up your context with a bunch of extra stuff, here's a reassuring note from Jesse:

The core of it is VERY token light. It pulls in one doc of fewer than 2k tokens. As it needs bits of the process, it runs a shell script to search for them. The long end to end chat for the planning and implementation process for that todo list app was 100k tokens.

It uses subagents to manage token-heavy stuff, including all the actual implementation.

(Jesse's post also tipped me off about Claude's /mnt/skills/public folder, see my notes here.)

# 10th October 2025, 11:30 pm / anthropic, claude, plugins, ai, claude-code, llms, vibe-coding, ai-assisted-programming, generative-ai, jesse-vincent, prompt-engineering, coding-agents, sub-agents

Video of GPT-OSS 20B running on a phone. GPT-OSS 20B is a very good model. At launch OpenAI claimed:

The gpt-oss-20b model delivers similar results to OpenAI o3‑mini on common benchmarks and can run on edge devices with just 16 GB of memory

Nexa AI just posted a video on Twitter demonstrating exactly that: the full GPT-OSS 20B running on a Snapdragon Gen 5 phone in their Nexa Studio Android app. It requires at least 16GB of RAM, and benefits from Snapdragon using a similar trick to Apple Silicon where the system RAM is available to both the CPU and the GPU.

The latest iPhone 17 Pro Max is still stuck at 12GB of RAM, presumably not enough to run this same model.

# 10th October 2025, 10:37 pm / gpt-oss, generative-ai, android, openai, ai, local-llms, llms

Deloitte to pay money back to Albanese government after using AI in $440,000 report. Ouch:

Deloitte will provide a partial refund to the federal government over a $440,000 report that contained several errors, after admitting it used generative artificial intelligence to help produce it.

(I was initially confused by the "Albanese government" reference in the headline since this is a story about the Australian federal government. That's because the current Australia Prime Minister is Anthony Albanese.)

Here's the page for the report. The PDF now includes this note:

This Report was updated on 26 September 2025 and replaces the Report dated 4 July 2025. The Report has been updated to correct those citations and reference list entries which contained errors in the previously issued version, to amend the summary of the Amato proceeding which contained errors, and to make revisions to improve clarity and readability. The updates made in no way impact or affect the substantive content, findings and recommendations in the Report.

# 6th October 2025, 11:35 pm / ai-ethics, generative-ai, ai, hallucinations, llms

gpt-image-1-mini. OpenAI released a new image model today: gpt-image-1-mini, which they describe as "A smaller image generation model that’s 80% less expensive than the large model."

They released it very quietly - I didn't hear about this in the DevDay keynote but I later spotted it on the DevDay 2025 announcements page.

It wasn't instantly obvious to me how to use this via their API. I ended up vibe coding a Python CLI tool for it so I could try it out.

I dumped the plain text diff version of the commit to the OpenAI Python library titled feat(api): dev day 2025 launches into ChatGPT GPT-5 Thinking and worked with it to figure out how to use the new image model and build a script for it. Here's the transcript and the the openai_image.py script it wrote.

I had it add inline script dependencies, so you can run it with uv like this:

export OPENAI_API_KEY="$(llm keys get openai)"
uv run https://tools.simonwillison.net/python/openai_image.py "A pelican riding a bicycle"

It picked this illustration style without me specifying it:

(This is a very different test from my normal "Generate an SVG of a pelican riding a bicycle" since it's using a dedicated image generator, not having a text-based model try to generate SVG code.)

My tool accepts a prompt, and optionally a filename (if you don't provide one it saves to a filename like /tmp/image-621b29.png).

It also accepts options for model and dimensions and output quality - the --help output lists those, you can see that here.

OpenAI's pricing is a little confusing. The model page claims low quality images should cost around half a cent and medium quality around a cent and a half. It also lists an image token price of $8/million tokens. It turns out there's a default "high" quality setting - most of the images I've generated have reported between 4,000 and 6,000 output tokens, which costs between 3.2 and 4.8 cents.

One last demo, this time using --quality low:

 uv run https://tools.simonwillison.net/python/openai_image.py \
  'racoon eating cheese wearing a top hat, realistic photo' \
  /tmp/racoon-hat-photo.jpg \
  --size 1024x1024 \
  --output-format jpeg \
  --quality low

This saved the following:

And reported this to standard error:

{
  "background": "opaque",
  "created": 1759790912,
  "generation_time_in_s": 20.87331541599997,
  "output_format": "jpeg",
  "quality": "low",
  "size": "1024x1024",
  "usage": {
    "input_tokens": 17,
    "input_tokens_details": {
      "image_tokens": 0,
      "text_tokens": 17
    },
    "output_tokens": 272,
    "total_tokens": 289
  }
}

This took 21s, but I'm on an unreliable conference WiFi connection so I don't trust that measurement very much.

272 output tokens = 0.2 cents so this is much closer to the expected pricing from the model page.

# 6th October 2025, 10:54 pm / openai, ai, text-to-image, generative-ai, vibe-coding, tools, uv, python, pelican-riding-a-bicycle

GPT-5 pro. Here's OpenAI's model documentation for their GPT-5 pro model, released to their API today at their DevDay event.

It has similar base characteristics to GPT-5: both share a September 30, 2024 knowledge cutoff and 400,000 context limit.

GPT-5 pro has maximum output tokens 272,000 max, an increase from 128,000 for GPT-5.

As our most advanced reasoning model, GPT-5 pro defaults to (and only supports) reasoning.effort: high

It's only available via OpenAI's Responses API. My LLM tool doesn't support that in core yet, but the llm-openai-plugin plugin does. I released llm-openai-plugin 0.7 adding support for the new model, then ran this:

llm install -U llm-openai-plugin
llm -m openai/gpt-5-pro "Generate an SVG of a pelican riding a bicycle"

It's very, very slow. The model took 6 minutes 8 seconds to respond and charged me for 16 input and 9,205 output tokens. At $15/million input and $120/million output this pelican cost me $1.10!

Here's the full transcript. It looks visually pretty simpler to the much, much cheaper result I got from GPT-5.

# 6th October 2025, 7:48 pm / llm-release, generative-ai, openai, pelican-riding-a-bicycle, ai, llms, gpt-5, llm-reasoning, llm-pricing

Let the LLM Write the Prompts: An Intro to DSPy in Compound Al Pipelines. I've had trouble getting my head around DSPy in the past. This half hour talk by Drew Breunig at the recent Databricks Data + AI Summit is the clearest explanation I've seen yet of the kinds of problems it can help solve.

Here's Drew's written version of the talk.

Drew works on Overture Maps, which combines Point Of Interest data from numerous providers to create a single unified POI database. This is an example of conflation, a notoriously difficult task in GIS where multiple datasets are deduped and merged together.

Drew uses an inexpensive local model, Qwen3-0.6B, to compare 70 million addresses and identity matches, for example between Place(address="3359 FOOTHILL BLVD", name="RESTAURANT LOS ARCOS") and Place(address="3359 FOOTHILL BLVD", name="Los Arcos Taqueria"').

DSPy's role is to optimize the prompt used for that smaller model. Drew used GPT-4.1 and the dspy.MIPROv2 optimizer, producing a 700 token prompt that increased the score from 60.7% to 82%.

Why bother? Drew points out that having a prompt optimization pipeline makes it trivial to evaluate and switch to other models if they can score higher with a custom optimized prompt - without needing to execute that trial-and-error optimization by hand.

# 4th October 2025, 10:48 pm / drew-breunig, prompt-engineering, generative-ai, overture, geospatial, ai, llms, gis, dspy

Daniel Stenberg’s note on AI assisted curl bug reports (via) Curl maintainer Daniel Stenberg on Mastodon:

Joshua Rogers sent us a massive list of potential issues in #curl that he found using his set of AI assisted tools. Code analyzer style nits all over. Mostly smaller bugs, but still bugs and there could be one or two actual security flaws in there. Actually truly awesome findings.

I have already landed 22(!) bugfixes thanks to this, and I have over twice that amount of issues left to go through. Wade through perhaps.

Credited "Reported in Joshua's sarif data" if you want to look for yourself

I searched for is:pr Joshua sarif data is:closed in the curl GitHub repository and found 49 completed PRs so far.

Joshua's own post about this: Hacking with AI SASTs: An overview of 'AI Security Engineers' / 'LLM Security Scanners' for Penetration Testers and Security Teams. The accompanying presentation PDF includes screenshots of some of the tools he used, which included Almanax, Amplify Security, Corgea, Gecko Security, and ZeroPath. Here's his vendor summary:

This result is especially notable because Daniel has been outspoken about the deluge of junk AI-assisted reports on "security issues" that curl has received in the past. In May this year, concerning HackerOne:

We now ban every reporter INSTANTLY who submits reports we deem AI slop. A threshold has been reached. We are effectively being DDoSed. If we could, we would charge them for this waste of our time.

He also wrote about this in January 2024, where he included this note:

I do however suspect that if you just add an ever so tiny (intelligent) human check to the mix, the use and outcome of any such tools will become so much better. I suspect that will be true for a long time into the future as well.

This is yet another illustration of how much more interesting these tools are when experienced professionals use them to augment their existing skills.

# 2nd October 2025, 3 pm / ai, llms, ai-ethics, daniel-stenberg, security, curl, generative-ai, ai-assisted-programming

aavetis/PRarena. Albert Avetisian runs this repository on GitHub which uses the Github Search API to track the number of PRs that can be credited to a collection of different coding agents. The repo runs this collect_data.py script every three hours using GitHub Actions to collect the data, then updates the PR Arena site with a visual leaderboard.

The result is this neat chart showing adoption of different agents over time, along with their PR success rate:

I found this today while trying to pull off the exact same trick myself! I got as far as creating the following table before finding Albert's work and abandoning my own project.

(Those "earliest" links are a little questionable, I tried to filter out false positives and find the oldest one that appeared to really be from the agent in question.)

It looks like OpenAI's Codex Cloud is massively ahead of the competition right now in terms of numbers of PRs both opened and merged on GitHub.

Update: To clarify, these numbers are for the category of autonomous coding agents - those systems where you assign a cloud-based agent a task or issue and the output is a PR against your repository. They do not (and cannot) capture the popularity of many forms of AI tooling that don't result in an easily identifiable pull request.

Claude Code for example will be dramatically under-counted here because its version of an autonomous coding agent comes in the form of a somewhat obscure GitHub Actions workflow buried in the documentation.

# 1st October 2025, 11:59 pm / coding-agents, ai-assisted-programming, generative-ai, git-scraping, ai, llms, openai, anthropic, github, claude-code

Armin Ronacher: 90% (via) The idea of AI writing "90% of the code" to-date has mostly been expressed by people who sell AI tooling.

Over the last few months, I've increasingly seen the same idea come coming much more credible sources.

Armin is the creator of a bewildering array of valuable open source projects - Flask, Jinja, Click, Werkzeug, and many more. When he says something like this it's worth paying attention:

For the infrastructure component I started at my new company, I’m probably north of 90% AI-written code.

For anyone who sees this as a threat to their livelihood as programmers, I encourage you to think more about this section:

It is easy to create systems that appear to behave correctly but have unclear runtime behavior when relying on agents. For instance, the AI doesn’t fully comprehend threading or goroutines. If you don’t keep the bad decisions at bay early it, you won’t be able to operate it in a stable manner later.

Here’s an example: I asked it to build a rate limiter. It “worked” but lacked jitter and used poor storage decisions. Easy to fix if you know rate limiters, dangerous if you don’t.

In order to use these tools at this level you need to know the difference between goroutines and threads. You need to understand why a rate limiter might want to"jitter" and what that actually means. You need to understand what "rate limiting" is and why you might need it!

These tools do not replace programmers. They allow us to apply our expertise at a higher level and amplify the value we can provide to other people.

# 29th September 2025, 4:03 pm / ai-assisted-programming, generative-ai, armin-ronacher, ai, llms, careers

Video models are zero-shot learners and reasoners. Fascinating new paper from Google DeepMind which makes a very convincing case that their Veo 3 model - and generative video models in general - serve a similar role in the machine learning visual ecosystem as LLMs do for text.

LLMs took the ability to predict the next token and turned it into general purpose foundation models for all manner of tasks that used to be handled by dedicated models - summarization, translation, parts of speech tagging etc can now all be handled by single huge models, which are getting both more powerful and cheaper as time progresses.

Generative video models like Veo 3 may well serve the same role for vision and image reasoning tasks.

From the paper:

We believe that video models will become unifying, general-purpose foundation models for machine vision just like large language models (LLMs) have become foundation models for natural language processing (NLP). [...]

Machine vision today in many ways resembles the state of NLP a few years ago: There are excellent task-specific models like “Segment Anything” for segmentation or YOLO variants for object detection. While attempts to unify some vision tasks exist, no existing model can solve any problem just by prompting. However, the exact same primitives that enabled zero-shot learning in NLP also apply to today’s generative video models—large-scale training with a generative objective (text/video continuation) on web-scale data. [...]

1. Analyzing 18,384 generated videos across 62 qualitative and 7 quantitative tasks, we report that Veo 3 can solve a wide range of tasks that it was neither trained nor adapted for.
2. Based on its ability to perceive, model, and manipulate the visual world, Veo 3 shows early forms of “chain-of-frames (CoF)” visual reasoning like maze and symmetry solving.
3. While task-specific bespoke models still outperform a zero-shot video model, we observe a substantial and consistent performance improvement from Veo 2 to Veo 3, indicating a rapid advancement in the capabilities of video models.

I particularly enjoyed the way they coined the new term chain-of-frames to reflect chain-of-thought in LLMs. A chain-of-frames is how a video generation model can "reason" about the visual world:

Perception, modeling, and manipulation all integrate to tackle visual reasoning. While language models manipulate human-invented symbols, video models can apply changes across the dimensions of the real world: time and space. Since these changes are applied frame-by-frame in a generated video, this parallels chain-of-thought in LLMs and could therefore be called chain-of-frames, or CoF for short. In the language domain, chain-of-thought enabled models to tackle reasoning problems. Similarly, chain-of-frames (a.k.a. video generation) might enable video models to solve challenging visual problems that require step-by-step reasoning across time and space.

They note that, while video models remain expensive to run today, it's likely they will follow a similar pricing trajectory as LLMs. I've been tracking this for a few years now and it really is a huge difference - a 1,200x drop in price between GPT-3 in 2022 ($60/million tokens) and GPT-5-Nano today ($0.05/million tokens).

The PDF is 45 pages long but the main paper is just the first 9.5 pages - the rest is mostly appendices. Reading those first 10 pages will give you the full details of their argument.

The accompanying website has dozens of video demos which are worth spending some time with to get a feel for the different applications of the Veo 3 model.

It's worth skimming through the appendixes in the paper as well to see examples of some of the prompts they used. They compare some of the exercises against equivalent attempts using Google's Nano Banana image generation model.

For edge detection, for example:

Veo: All edges in this image become more salient by transforming into black outlines. Then, all objects fade away, with just the edges remaining on a white background. Static camera perspective, no zoom or pan.

Nano Banana: Outline all edges in the image in black, make everything else white.

# 27th September 2025, 11:59 pm / gemini, google, generative-ai, video, ai, llms, video-models, paper-review