Simon Willison’s Weblog

llm-whisper-api. I wanted to run an experiment through the OpenAI Whisper API this morning so I knocked up a very quick plugin for LLM that provides the following interface:

llm install llm-whisper-api
llm whisper-api myfile.mp3 > transcript.txt

It uses the API key that you previously configured using the llm keys set openai command. If you haven't configured one you can pass it as --key XXX instead.

It's a tiny plugin: the source code is here.

# 27th October 2024, 6:19 pm / llm, projects, plugins, openai, whisper, ai

Running prompts against images and PDFs with Google Gemini. New TIL. I've been experimenting with the Google Gemini APIs for running prompts against images and PDFs (in preparation for finally adding multi-modal support to LLM) - here are my notes on how to send images or PDF files to their API using curl and the base64 -i macOS command.

I figured out the curl incantation first and then got Claude to build me a Bash script that I can execute like this:

prompt-gemini 'extract text' example-handwriting.jpg

Playing with this is really fun. The Gemini models charge less than 1/10th of a cent per image, so it's really inexpensive to try them out.

# 23rd October 2024, 6:25 pm / vision-llms, gemini, llm, bash, ai, llms, ai-assisted-programming, google, generative-ai, ocr, projects

Dashboard: Tools. I used Django SQL Dashboard to spin up a dashboard that shows all of the URLs to my tools.simonwillison.net site that I've shared on my blog so far. It uses this (Claude assisted) regular expression in a PostgreSQL SQL query:

select distinct on (tool_url)
    unnest(regexp_matches(
        body,
        '(https://tools\.simonwillison\.net/[^<"\s)]+)',
        'g'
    )) as tool_url,
    'https://simonwillison.net/' || left(type, 1) || '/' || id as blog_url,
    title,
    date(created) as created
from content

I've been really enjoying having a static hosting platform (it's GitHub Pages serving my simonw/tools repo) that I can use to quickly deploy little HTML+JavaScript interactive tools and demos.

# 21st October 2024, 3:33 am / django-sql-dashboard, ai-assisted-programming, tools, projects, postgresql, sql, javascript

files-to-prompt 0.4. New release of my files-to-prompt tool adding an option for filtering just for files with a specific extension.

The following command will output Claude XML-style markup for all Python and Markdown files in the current directory, and copy that to the macOS clipboard ready to be pasted into an LLM:

files-to-prompt . -e py -e md -c | pbcopy

# 16th October 2024, 11:29 pm / projects, python, llms

My Jina Reader tool. I wanted to feed the Cloudflare Durable Objects SQLite documentation into Claude, but I was on my iPhone so copying and pasting was inconvenient. Jina offer a Reader API which can turn any URL into LLM-friendly Markdown and it turns out it supports CORS, so I got Claude to build me this tool (second iteration, third iteration, final source code).

Paste in a URL to get the Jina Markdown version, along with an all important "Copy to clipboard" button.

# 14th October 2024, 4:47 pm / projects, markdown, ai-assisted-programming, jina, claude-3-5-sonnet, claude, generative-ai, ai, llms

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

simonw/docs cookiecutter template. Over the last few years I’ve settled on the combination of Sphinx, the Furo theme and the myst-parser extension (enabling Markdown in place of reStructuredText) as my documentation toolkit of choice, maintained in GitHub and hosted using ReadTheDocs.

My LLM and shot-scraper projects are two examples of that stack in action.

Today I wanted to spin up a new documentation site so I finally took the time to construct a cookiecutter template for my preferred configuration. You can use it like this:

pipx install cookiecutter
cookiecutter gh:simonw/docs

Or with uv:

uv tool run cookiecutter gh:simonw/docs

Answer a few questions:

[1/3] project (): shot-scraper
[2/3] author (): Simon Willison
[3/3] docs_directory (docs):

And it creates a docs/ directory ready for you to start editing docs:

cd docs
pip install -r requirements.txt
make livehtml

# 23rd September 2024, 9:45 pm / uv, markdown, sphinx-docs, cookiecutter, read-the-docs, python, projects, documentation

LLM 0.16. New release of LLM adding support for the o1-preview and o1-mini OpenAI models that were released today.

# 12th September 2024, 11:20 pm / llm, projects, generative-ai, openai, ai, llms, o1

files-to-prompt 0.3. New version of my files-to-prompt CLI tool for turning a bunch of files into a prompt suitable for piping to an LLM, described here previously.

It now has a -c/--cxml flag for outputting the files in Claude XML-ish notation (XML-ish because it's not actually valid XML) using the format Anthropic describe as recommended for long context:

files-to-prompt llm-*/README.md --cxml | llm -m claude-3.5-sonnet \
  --system 'return an HTML page about these plugins with usage examples' \
  > /tmp/fancy.html

Here's what that gave me.

The format itself looks something like this:

<documents>
<document index="1">
<source>llm-anyscale-endpoints/README.md</source>
<document_content>
# llm-anyscale-endpoints
...
</document_content>
</document>
</documents>

# 9th September 2024, 5:57 am / anthropic, claude, tools, projects, generative-ai, ai, llms, prompt-engineering

json-flatten, now with format documentation. json-flatten is a fun little Python library I put together a few years ago for converting JSON data into a flat key-value format, suitable for inclusion in an HTML form or query string. It lets you take a structure like this one:

{"foo": {"bar": [1, True, None]}

And convert it into key-value pairs like this:

foo.bar.[0]$int=1
foo.bar.[1]$bool=True
foo.bar.[2]$none=None

The flatten(dictionary) function function converts to that format, and unflatten(dictionary) converts back again.

I was considering the library for a project today and realized that the 0.3 README was a little thin - it showed how to use the library but didn't provide full details of the format it used.

On a hunch, I decided to see if files-to-prompt plus LLM plus Claude 3.5 Sonnet could write that documentation for me. I ran this command:

files-to-prompt *.py | llm -m claude-3.5-sonnet --system 'write detailed documentation in markdown describing the format used to represent JSON and nested JSON as key/value pairs, include a table as well'

That *.py picked up both json_flatten.py and test_json_flatten.py - I figured the test file had enough examples in that it should act as a good source of information for the documentation.

This worked really well! You can see the first draft it produced here.

It included before and after examples in the documentation. I didn't fully trust these to be accurate, so I gave it this follow-up prompt:

llm -c "Rewrite that document to use the Python cog library to generate the examples"

I'm a big fan of Cog for maintaining examples in READMEs that are generated by code. Cog has been around for a couple of decades now so it was a safe bet that Claude would know about it.

This almost worked - it produced valid Cog syntax like the following:

[[[cog
example = {
"fruits": ["apple", "banana", "cherry"]
}

cog.out("```json\n")
cog.out(str(example))
cog.out("\n```\n")
cog.out("Flattened:\n```\n")
for key, value in flatten(example).items():
    cog.out(f"{key}: {value}\n")
cog.out("```\n")
]]]
[[[end]]]

But that wasn't entirely right, because it forgot to include the Markdown comments that would hide the Cog syntax, which should have looked like this:

<!-- [[[cog -->
...
<!-- ]]] -->
...
<!-- [[[end]]] -->

I could have prompted it to correct itself, but at this point I decided to take over and edit the rest of the documentation by hand.

The end result was documentation that I'm really happy with, and that I probably wouldn't have bothered to write if Claude hadn't got me started.

# 7th September 2024, 5:43 am / claude-3-5-sonnet, llm, anthropic, claude, ai, llms, ai-assisted-programming, generative-ai, projects, json

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

New improved commit messages for scrape-hacker-news-by-domain. My simonw/scrape-hacker-news-by-domain repo has a very specific purpose. Once an hour it scrapes the Hacker News /from?site=simonwillison.net page (and the equivalent for datasette.io) using my shot-scraper tool and stashes the parsed links, scores and comment counts in JSON files in that repo.

It does this mainly so I can subscribe to GitHub's Atom feed of the commit log - visit simonw/scrape-hacker-news-by-domain/commits/main and add .atom to the URL to get that.

NetNewsWire will inform me within about an hour if any of my content has made it to Hacker News, and the repo will track the score and comment count for me over time. I wrote more about how this works in Scraping web pages from the command line with shot-scraper back in March 2022.

Prior to the latest improvement, the commit messages themselves were pretty uninformative. The message had the date, and to actually see which Hacker News post it was referring to, I had to click through to the commit and look at the diff.

I built my csv-diff tool a while back to help address this problem: it can produce a slightly more human-readable version of a diff between two CSV or JSON files, ideally suited for including in a commit message attached to a git scraping repo like this one.

I got that working, but there was still room for improvement. I recently learned that any Hacker News thread has an undocumented URL at /latest?id=x which displays the most recently added comments at the top.

I wanted that in my commit messages, so I could quickly click a link to see the most recent comments on a thread.

So... I added one more feature to csv-diff: a new --extra option lets you specify a Python format string to be used to add extra fields to the displayed difference.

My GitHub Actions workflow now runs this command:

csv-diff simonwillison-net.json simonwillison-net-new.json \
  --key id --format json \
  --extra latest 'https://news.ycombinator.com/latest?id={id}' \
  >> /tmp/commit.txt

This generates the diff between the two versions, using the id property in the JSON to tie records together. It adds a latest field linking to that URL.

The commits now look like this:

# 6th September 2024, 5:40 am / shot-scraper, github-actions, projects, hacker-news, git-scraping, json

llm-claude-3 0.4.1. New minor release of my LLM plugin that provides access to the Claude 3 family of models. Claude 3.5 Sonnet recently upgraded to a 8,192 output limit recently (up from 4,096 for the Claude 3 family of models). LLM can now respect that.

The hardest part of building this was convincing Claude to return a long enough response to prove that it worked. At one point I got into an argument with it, which resulted in this fascinating hallucination:

I eventually got a 6,162 token output using:

cat long.txt | llm -m claude-3.5-sonnet-long --system 'translate this document into french, then translate the french version into spanish, then translate the spanish version back to english. actually output the translations one by one, and be sure to do the FULL document, every paragraph should be translated correctly. Seriously, do the full translations - absolutely no summaries!'

# 30th August 2024, 11:28 pm / llm, anthropic, claude, generative-ai, projects, ai, llms, prompt-engineering, claude-3-5-sonnet

Gemini Chat App. Google released three new Gemini models today: improved versions of Gemini 1.5 Pro and Gemini 1.5 Flash plus a new model, Gemini 1.5 Flash-8B, which is significantly faster (and will presumably be cheaper) than the regular Flash model.

The Flash-8B model is described in the Gemini 1.5 family of models paper in section 8:

By inheriting the same core architecture, optimizations, and data mixture refinements as its larger counterpart, Flash-8B demonstrates multimodal capabilities with support for context window exceeding 1 million tokens. This unique combination of speed, quality, and capabilities represents a step function leap in the domain of single-digit billion parameter models.

While Flash-8B’s smaller form factor necessarily leads to a reduction in quality compared to Flash and 1.5 Pro, it unlocks substantial benefits, particularly in terms of high throughput and extremely low latency. This translates to affordable and timely large-scale multimodal deployments, facilitating novel use cases previously deemed infeasible due to resource constraints.

The new models are available in AI Studio, but since I built my own custom prompting tool against the Gemini CORS-enabled API the other day I figured I'd build a quick UI for these new models as well.

Building this with Claude 3.5 Sonnet took literally ten minutes from start to finish - you can see that from the timestamps in the conversation. Here's the deployed app and the finished code.

The feature I really wanted to build was streaming support. I started with this example code showing how to run streaming prompts in a Node.js application, then told Claude to figure out what the client-side code for that should look like based on a snippet from my bounding box interface hack. My starting prompt:

Build me a JavaScript app (no react) that I can use to chat with the Gemini model, using the above strategy for API key usage

I still keep hearing from people who are skeptical that AI-assisted programming like this has any value. It's honestly getting a little frustrating at this point - the gains for things like rapid prototyping are so self-evident now.

# 27th August 2024, 10:48 pm / claude-3-5-sonnet, gemini, ai-assisted-programming, javascript, generative-ai, ai, llms, projects, anthropic, claude

My @covidsewage bot now includes useful alt text. I've been running a @covidsewage Mastodon bot for a while now, posting daily screenshots (taken with shot-scraper) of the Santa Clara County COVID in wastewater dashboard.

Prior to today the screenshot was accompanied by the decidedly unhelpful alt text "Screenshot of the latest Covid charts".

I finally fixed that today, closing issue #2 more than two years after I first opened it.

The screenshot is of a Microsoft Power BI dashboard. I hoped I could scrape the key information out of it using JavaScript, but the weirdness of their DOM proved insurmountable.

Instead, I'm using GPT-4o - specifically, this Python code (run using a python -c block in the GitHub Actions YAML file):

import base64, openai
client = openai.OpenAI()
with open('/tmp/covid.png', 'rb') as image_file:
    encoded_image = base64.b64encode(image_file.read()).decode('utf-8')
messages = [
    {'role': 'system',
     'content': 'Return the concentration levels in the sewersheds - single paragraph, no markdown'},
    {'role': 'user', 'content': [
        {'type': 'image_url', 'image_url': {
            'url': 'data:image/png;base64,' + encoded_image
        }}
    ]}
]
completion = client.chat.completions.create(model='gpt-4o', messages=messages)
print(completion.choices[0].message.content)

I'm base64 encoding the screenshot and sending it with this system prompt:

Return the concentration levels in the sewersheds - single paragraph, no markdown

Given this input image:

Here's the text that comes back:

The concentration levels of SARS-CoV-2 in the sewersheds from collected samples are as follows: San Jose Sewershed has a high concentration, Palo Alto Sewershed has a high concentration, Sunnyvale Sewershed has a high concentration, and Gilroy Sewershed has a medium concentration.

The full implementation can be found in the GitHub Actions workflow, which runs on a schedule at 7am Pacific time every day.

# 25th August 2024, 4:09 pm / shot-scraper, openai, covid19, gpt-4, ai, llms, generative-ai, projects, alt-attribute, accessibility

Fix @covidsewage bot to handle a change to the underlying website. I've been running @covidsewage on Mastodon since February last year tweeting a daily screenshot of the Santa Clara County charts showing Covid levels in wastewater.

A few days ago the county changed their website, breaking the bot. The chart now lives on their new COVID in wastewater page.

It's still a Microsoft Power BI dashboard in an <iframe>, but my initial attempts to scrape it didn't quite work. Eventually I realized that Cloudflare protection was blocking my attempts to access the page, but thankfully sending a Firefox user-agent fixed that problem.

The new recipe I'm using to screenshot the chart involves a delightfully messy nested set of calls to shot-scraper - first using shot-scraper javascript to extract the URL attribute for that <iframe>, then feeding that URL to a separate shot-scraper call to generate the screenshot:

shot-scraper -o /tmp/covid.png $(
  shot-scraper javascript \
    'https://publichealth.santaclaracounty.gov/health-information/health-data/disease-data/covid-19/covid-19-wastewater' \
    'document.querySelector("iframe").src' \
    -b firefox \
    --user-agent 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:128.0) Gecko/20100101 Firefox/128.0' \
    --raw
) --wait 5000 -b firefox --retina

# 18th August 2024, 5:26 pm / projects, covid19, shot-scraper

Upgrading my cookiecutter templates to use python -m pytest. Every now and then I get caught out by weird test failures when I run pytest and it turns out I'm running the wrong installation of that tool, so my tests fail because that pytest is executing in a different virtual environment from the one needed by the tests.

The fix for this is easy: run python -m pytest instead, which guarantees that you will run pytest in the same environment as your currently active Python.

Yesterday I went through and updated every one of my cookiecutter templates (python-lib, click-app, datasette-plugin, sqlite-utils-plugin, llm-plugin) to use this pattern in their READMEs and generated repositories instead, to help spread that better recipe a little bit further.

# 17th August 2024, 5:12 am / cookiecutter, projects, python, pytest

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

Share Claude conversations by converting their JSON to Markdown. Anthropic's Claude is missing one key feature that I really appreciate in ChatGPT: the ability to create a public link to a full conversation transcript. You can publish individual artifacts from Claude, but I often find myself wanting to publish the whole conversation.

Before ChatGPT added that feature I solved it myself with this ChatGPT JSON transcript to Markdown Observable notebook. Today I built the same thing for Claude.

Here's how to use it:

The key is to load a Claude conversation on their website with your browser DevTools network panel open and then filter URLs for chat_. You can use the Copy -> Response right click menu option to get the JSON for that conversation, then paste it into that new Observable notebook to get a Markdown transcript.

I like sharing these by pasting them into a "secret" Gist - that way they won't be indexed by search engines (adding more AI generated slop to the world) but can still be shared with people who have the link.

Here's an example transcript from this morning. I started by asking Claude:

I want to breed spiders in my house to get rid of all of the flies. What spider would you recommend?

When it suggested that this was a bad idea because it might attract pests, I asked:

What are the pests might they attract? I really like possums

It told me that possums are attracted by food waste, but "deliberately attracting them to your home isn't recommended" - so I said:

Thank you for the tips on attracting possums to my house. I will get right on that! [...] Once I have attracted all of those possums, what other animals might be attracted as a result? Do you think I might get a mountain lion?

It emphasized how bad an idea that would be and said "This would be extremely dangerous and is a serious public safety risk.", so I said:

OK. I took your advice and everything has gone wrong: I am now hiding inside my house from the several mountain lions stalking my backyard, which is full of possums

Claude has quite a preachy tone when you ask it for advice on things that are clearly a bad idea, which makes winding it up with increasingly ludicrous questions a lot of fun.

# 8th August 2024, 8:40 pm / anthropic, claude, markdown, ai, llms, tools, generative-ai, projects, json, observable