Simon Willison’s Weblog

files-to-prompt 0.5. My files-to-prompt tool (originally built using Claude 3 Opus back in April) had been accumulating a bunch of issues and PRs - I finally got around to spending some time with it and pushed a fresh release:

• New -n/--line-numbers flag for including line numbers in the output. Thanks, Dan Clayton. #38
• Fix for utf-8 handling on Windows. Thanks, David Jarman. #36
• --ignore patterns are now matched against directory names as well as file names, unless you pass the new --ignore-files-only flag. Thanks, Nick Powell. #30

I use this tool myself on an almost daily basis - it's fantastic for quickly answering questions about code. Recently I've been plugging it into Gemini 2.0 with its 2 million token context length, running recipes like this one:

git clone https://github.com/bytecodealliance/componentize-py
cd componentize-py
files-to-prompt . -c | llm -m gemini-2.0-pro-exp-02-05 \
  -s 'How does this work? Does it include a python compiler or AST trick of some sort?'

I ran that question against the bytecodealliance/componentize-py repo - which provides a tool for turning Python code into compiled WASM - and got this really useful answer.

Here's another example. I decided to have o3-mini review how Datasette handles concurrent SQLite connections from async Python code - so I ran this:

git clone https://github.com/simonw/datasette
cd datasette/datasette
files-to-prompt database.py utils/__init__.py -c | \
  llm -m o3-mini -o reasoning_effort high \
  -s 'Output in markdown a detailed analysis of how this code handles the challenge of running SQLite queries from a Python asyncio application. Explain how it works in the first section, then explore the pros and cons of this design. In a final section propose alternative mechanisms that might work better.'

Here's the result. It did an extremely good job of explaining how my code works - despite being fed just the Python and none of the other documentation. Then it made some solid recommendations for potential alternatives.

I added a couple of follow-up questions (using llm -c) which resulted in a full working prototype of an alternative threadpool mechanism, plus some benchmarks.

One final example: I decided to see if there were any undocumented features in Litestream, so I checked out the repo and ran a prompt against just the .go files in that project:

git clone https://github.com/benbjohnson/litestream
cd litestream
files-to-prompt . -e go -c | llm -m o3-mini \
  -s 'Write extensive user documentation for this project in markdown'

Once again, o3-mini provided a really impressively detailed set of unofficial documentation derived purely from reading the source.

# 14th February 2025, 4:14 am / projects, llms, gemini, llm, ai-assisted-programming, generative-ai, ai, webassembly, python, async, datasette, sqlite, litestream

Datasette 1.0a17. New Datasette alpha, with a bunch of small changes and bug fixes accumulated over the past few months. Some (minor) highlights:

• The register_magic_parameters(datasette) plugin hook can now register async functions. (#2441)
• Breadcrumbs on database and table pages now include a consistent self-link for resetting query string parameters. (#2454)
• New internal methods datasette.set_actor_cookie() and datasette.delete_actor_cookie(), described here. (#1690)
• /-/permissions page now shows a list of all permissions registered by plugins. (#1943)
• If a table has a single unique text column Datasette now detects that as the foreign key label for that table. (#2458)
• The /-/permissions page now includes options for filtering or exclude permission checks recorded against the current user. (#2460)

I was incentivized to push this release by an issue I ran into in my new datasette-load plugin, which resulted in this fix:

• Fixed a bug where replacing a database with a new one with the same name did not pick up the new database correctly. (#2465)

# 6th February 2025, 8:56 pm / projects, annotated-release-notes, datasette

S1: The $6 R1 Competitor? Tim Kellogg shares his notes on a new paper, s1: Simple test-time scaling, which describes an inference-scaling model fine-tuned on top of Qwen2.5-32B-Instruct for just $6 - the cost for 26 minutes on 16 NVIDIA H100 GPUs.

Tim highlight the most exciting result:

After sifting their dataset of 56K examples down to just the best 1K, they found that the core 1K is all that's needed to achieve o1-preview performance on a 32B model.

The paper describes a technique called "Budget forcing":

To enforce a minimum, we suppress the generation of the end-of-thinking token delimiter and optionally append the string “Wait” to the model’s current reasoning trace to encourage the model to reflect on its current generation

That's the same trick Theia Vogel described a few weeks ago.

Here's the s1-32B model on Hugging Face. I found a GGUF version of it at brittlewis12/s1-32B-GGUF, which I ran using Ollama like so:

ollama run hf.co/brittlewis12/s1-32B-GGUF:Q4_0

I also found those 1,000 samples on Hugging Face in the simplescaling/s1K data repository there.

I used DuckDB to convert the parquet file to CSV (and turn one VARCHAR[] column into JSON):

COPY (
    SELECT 
      solution,
      question,
      cot_type,
      source_type,
      metadata,
      cot,
      json_array(thinking_trajectories) as thinking_trajectories,
      attempt
    FROM 's1k-00001.parquet'
) TO 'output.csv' (HEADER, DELIMITER ',');

Then I loaded that CSV into sqlite-utils so I could use the convert command to turn a Python data structure into JSON using json.dumps() and eval():

# Load into SQLite
sqlite-utils insert s1k.db s1k output.csv --csv
# Fix that column
sqlite-utils convert s1k.db s1u metadata 'json.dumps(eval(value))' --import json
# Dump that back out to CSV
sqlite-utils rows s1k.db s1k --csv > s1k.csv

Here's that CSV in a Gist, which means I can load it into Datasette Lite.

It really is a tiny amount of training data. It's mostly math and science, but there are also 15 cryptic crossword examples.

# 5th February 2025, 8 pm / duckdb, datasette-lite, inference-scaling, ai, ollama, llms, datasette, generative-ai, qwen

o3-mini is really good at writing internal documentation. I wanted to refresh my knowledge of how the Datasette permissions system works today. I already have extensive hand-written documentation for that, but I thought it would be interesting to see if I could derive any insights from running an LLM against the codebase.

o3-mini has an input limit of 200,000 tokens. I used LLM and my files-to-prompt tool to generate the documentation like this:

cd /tmp
git clone https://github.com/simonw/datasette
cd datasette
files-to-prompt datasette -e py -c | \
  llm -m o3-mini -s \
  'write extensive documentation for how the permissions system works, as markdown'

The files-to-prompt command is fed the datasette subdirectory, which contains just the source code for the application - omitting tests (in tests/) and documentation (in docs/).

The -e py option causes it to only include files with a .py extension - skipping all of the HTML and JavaScript files in that hierarchy.

The -c option causes it to output Claude's XML-ish format - a format that works great with other LLMs too.

You can see the output of that command in this Gist.

Then I pipe that result into LLM, requesting the o3-mini OpenAI model and passing the following system prompt:

write extensive documentation for how the permissions system works, as markdown

Specifically requesting Markdown is important.

The prompt used 99,348 input tokens and produced 3,118 output tokens (320 of those were invisible reasoning tokens). That's a cost of 12.3 cents.

Honestly, the results are fantastic. I had to double-check that I hadn't accidentally fed in the documentation by mistake.

(It's possible that the model is picking up additional information about Datasette in its training set, but I've seen similar high quality results from other, newer libraries so I don't think that's a significant factor.)

In this case I already had extensive written documentation of my own, but this was still a useful refresher to help confirm that the code matched my mental model of how everything works.

Documentation of project internals as a category is notorious for going out of date. Having tricks like this to derive usable how-it-works documentation from existing codebases in just a few seconds and at a cost of a few cents is wildly valuable.

# 5th February 2025, 6:07 am / llm, openai, o3, ai, llms, datasette, generative-ai, documentation, ai-assisted-programming, inference-scaling

Datasette Public Office Hours 31st Jan at 2pm Pacific. We're running another Datasette Public Office Hours session on Friday 31st January at 2pm Pacific (more timezones here). We'll be featuring demos from the community again - take a look at the videos of the six demos from our last session for an idea of what to expect.

If you have something you would like to show, please drop us a line! We still have room for a few more demos.

# 30th January 2025, 9:45 pm / discord, datasette, community, datasette-public-office-hours

Datasette Public Office Hours Application. We are running another Datasette Public Office Hours event on Discord tomorrow (Friday 17th January 2025) at 2pm Pacific / 5pm Eastern / 10pm GMT / more timezones here.

The theme this time around is lightning talks - we're looking for 5-8 minute long talks from community members about projects they are working on or things they have built using the Datasette family of tools (which includes LLM and sqlite-utils as well).

If you have a demo you'd like to share, please let us know via this form.

I'm going to be demonstrating my recent work on the next generation of Datasette Enrichments.

# 16th January 2025, 6:38 pm / lightning-talks, enrichments, discord, datasette, datasette-public-office-hours

datasette-enrichments-llm. Today's new alpha release is datasette-enrichments-llm, a plugin for Datasette 1.0a+ that provides an enrichment that lets you run prompts against data from one or more column and store the result in another column.

So far it's a light re-implementation of the existing datasette-enrichments-gpt plugin, now using the new llm.get_async_models() method to allow users to select any async-enabled model that has been registered by a plugin - so currently any of the models from OpenAI, Anthropic, Gemini or Mistral via their respective plugins.

Still plenty to do on this one. Next step is to integrate it with datasette-llm-usage and use it to drive a design-complete stable version of that.

# 5th December 2024, 11:46 pm / llm, plugins, ai, llms, enrichments, releases, datasette, generative-ai, projects

datasette-queries. I released the first alpha of a new plugin to replace the crusty old datasette-saved-queries. This one adds a new UI element to the top of the query results page with an expandable form for saving the query as a new canned query:

It's my first plugin to depend on LLM and datasette-llm-usage - it uses GPT-4o mini to power an optional "Suggest title and description" button, labeled with the becoming-standard ✨ sparkles emoji to indicate an LLM-powered feature.

I intend to expand this to work across multiple models as I continue to iterate on llm-datasette-usage to better support those kinds of patterns.

For the moment though each suggested title and description call costs about 250 input tokens and 50 output tokens, which against GPT-4o mini adds up to 0.0067 cents.

# 3rd December 2024, 11:59 pm / projects, releases, datasette, plugins, llm, generative-ai, openai, ai, llms

datasette-llm-usage. I released the first alpha of a Datasette plugin to help track LLM usage by other plugins, with the goal of supporting token allowances - both for things like free public apps that stop working after a daily allowance, plus free previews of AI features for paid-account-based projects such as Datasette Cloud.

It's using the usage features I added in LLM 0.19.

The alpha doesn't do much yet - it will start getting interesting once I upgrade other plugins to depend on it.

Design notes so far in issue #1.

# 2nd December 2024, 9:33 pm / llm, datasette-cloud, plugins, ai, llms, datasette, generative-ai, projects, releases

Datasette Public Office Hours, Friday Nov 8th at 2pm PT. Tomorrow afternoon (Friday 8th November) at 2pm PT we'll be hosting the first Datasette Public Office Hours - a livestream video session on Discord where Alex Garcia and myself will live code on some Datasette projects and hang out to chat about the project.

This is our first time trying this format. If it works out well I plan to turn it into a series.

# 7th November 2024, 7:10 pm / discord, open-source, datasette, alex-garcia, datasette-public-office-hours

Datasette 0.65. Python 3.13 was released today, which broke compatibility with the Datasette 0.x series due to an issue with an underlying dependency. I've fixed that problem by vendoring and fixing the dependency and the new 0.65 release works on Python 3.13 (but drops support for Python 3.8, which is EOL this month). Datasette 1.0a16 added support for Python 3.13 last month.

# 7th October 2024, 6:07 pm / projects, datasette, python

django-plugin-datasette. I did some more work on my DJP plugin mechanism for Django at the DjangoCon US sprints today. I added a new plugin hook, asgi_wrapper(), released in DJP 0.3 and inspired by the similar hook in Datasette.

The hook only works for Django apps that are served using ASGI. It allows plugins to add their own wrapping ASGI middleware around the Django app itself, which means they can do things like attach entirely separate ASGI-compatible applications outside of the regular Django request/response cycle.

Datasette is one of those ASGI-compatible applications!

django-plugin-datasette uses that new hook to configure a new URL, /-/datasette/, which serves a full Datasette instance that scans through Django’s settings.DATABASES dictionary and serves an explore interface on top of any SQLite databases it finds there.

It doesn’t support authentication yet, so this will expose your entire database contents - probably best used as a local debugging tool only.

I did borrow some code from the datasette-mask-columns plugin to ensure that the password column in the auth_user column is reliably redacted. That column contains a heavily salted hashed password so exposing it isn’t necessarily a disaster, but I like to default to keeping hashes safe.

# 26th September 2024, 9:57 pm / projects, sqlite, plugins, djp, datasette, django

Datasette 1.0a16. This latest release focuses mainly on performance, as discussed here in Optimizing Datasette a couple of weeks ago.

It also includes some minor CSS changes that could affect plugins, and hence need to be included before the final 1.0 release. Those are outlined in detail in issues #2415 and #2420.

# 6th September 2024, 5:55 am / projects, datasette

datasette-checkbox. I built this fun little Datasette plugin today, inspired by a conversation I had in Datasette Office Hours.

If a user has the update-row permission and the table they are viewing has any integer columns with names that start with is_ or should_ or has_, the plugin adds interactive checkboxes to that table which can be toggled to update the underlying rows.

This makes it easy to quickly spin up an interface that allows users to review and update boolean flags in a table.

I have ambitions for a much more advanced version of this, where users can do things like add or remove tags from rows directly in that table interface - but for the moment this is a neat starting point, and it only took an hour to build (thanks to help from Claude to build an initial prototype, chat transcript here).

# 16th August 2024, 9:28 pm / projects, claude-3-5-sonnet, datasette, plugins

Datasette 1.0a15. Mainly bug fixes, but a couple of minor new features:

• Datasette now defaults to hiding SQLite "shadow" tables, as seen in extensions such as SQLite FTS and sqlite-vec. Virtual tables that it makes sense to display, such as FTS core tables, are no longer hidden. Thanks, Alex Garcia. (#2296)
• The Datasette homepage is now duplicated at /-/, using the default index.html template. This ensures that the information on that page is still accessible even if the Datasette homepage has been customized using a custom index.html template, for example on sites like datasette.io. (#2393)

Datasette also now serves more user-friendly CSRF pages, an improvement which required me to ship asgi-csrf 0.10.

# 16th August 2024, 5:06 am / releases, datasette, projects, csrf

Using sqlite-vec with embeddings in sqlite-utils and Datasette. My notes on trying out Alex Garcia's newly released sqlite-vec SQLite extension, including how to use it with OpenAI embeddings in both Datasette and sqlite-utils.

# 11th August 2024, 11:37 pm / embeddings, sqlite-utils, sqlite, datasette, openai, alex-garcia

Announcing our DjangoCon US 2024 Talks! I'm speaking at DjangoCon in Durham, NC in September.

My accepted talk title was How to design and implement extensible software with plugins. Here's my abstract:

Plugins offer a powerful way to extend software packages. Tools that support a plugin architecture include WordPress, Jupyter, VS Code and pytest - each of which benefits from an enormous array of plugins adding all kinds of new features and expanded capabilities.

Adding plugin support to an open source project can greatly reduce the friction involved in attracting new contributors. Users can work independently and even package and publish their work without needing to directly coordinate with the project's core maintainers. As a maintainer this means you can wake up one morning and your software grew new features without you even having to review a pull request!

There's one catch: information on how to design and implement plugin support for a project is scarce.

I now have three major open source projects that support plugins, with over 200 plugins published across those projects. I'll talk about everything I've learned along the way: when and how to use plugins, how to design plugin hooks and how to ensure your plugin authors have as good an experience as possible.

I'm going to be talking about what I've learned integrating Pluggy with Datasette, LLM and sqlite-utils. I've been looking for an excuse to turn this knowledge into a talk for ages, very excited to get to do it at DjangoCon!

# 17th July 2024, 3:20 am / djangocon, python, plugins, django, speaking, sqlite-utils, llm, datasette

datasette-python. I just released a small new plugin for Datasette to assist with debugging. It adds a python subcommand which runs a Python process in the same virtual environment as Datasette itself.

I built it initially to help debug some issues in Datasette installed via Homebrew. The Homebrew installation has its own virtual environment, and sometimes it can be useful to run commands like pip list in the same environment as Datasette itself.

Now you can do this:

brew install datasette
datasette install datasette-python
datasette python -m pip list

I built a similar plugin for LLM last year, called llm-python - it's proved useful enough that I duplicated the design for Datasette.

# 12th July 2024, 11:17 pm / projects, datasette, python, plugins

UK Parliament election results, now with Datasette. The House of Commons Library maintains a website of UK parliamentary election results data, currently listing 2010 through 2019 and with 2024 results coming soon.

The site itself is a Rails and PostgreSQL app, but I was delighted to learn today that they're also running a Datasette instance with the election results data, linked to from their homepage!

The raw data is also available as CSV files in their GitHub repository. Here's their Datasette configuration, which includes a copy of their SQLite database.

# 5th July 2024, 11:36 pm / elections, datasette, sqlite