Simon Willison’s Weblog

open-interpreter (via) This "natural language interface for computers" open source ChatGPT Code Interpreter alternative has been around for a while, but today I finally got around to trying it out.

Here's how I ran it (without first installing anything) using uv:

uvx --from open-interpreter interpreter

The default mode asks you for an OpenAI API key so it can use gpt-4o - there are a multitude of other options, including the ability to use local models with interpreter --local.

It runs in your terminal and works by generating Python code to help answer your questions, asking your permission to run it and then executing it directly on your computer.

I pasted in an API key and then prompted it with this:

find largest files on my desktop

Here's the full transcript.

Since code is run directly on your machine there are all sorts of ways things could go wrong if you don't carefully review the generated code before hitting "y". The team have an experimental safe mode in development which works by scanning generated code with semgrep. I'm not convinced by that approach, I think executing code in a sandbox would be a much more robust solution here - but sandboxing Python is still a very difficult problem.

They do at least have an experimental Docker integration.

# 24th November 2024, 6:29 pm / llms, ai, generative-ai, uv, sandboxing, code-interpreter, openai, ai-assisted-programming, python

Foursquare Open Source Places: A new foundational dataset for the geospatial community (via) I did not expect this!

[...] we are announcing today the general availability of a foundational open data set, Foursquare Open Source Places ("FSQ OS Places"). This base layer of 100mm+ global places of interest ("POI") includes 22 core attributes (see schema here) that will be updated monthly and available for commercial use under the Apache 2.0 license framework.

The data is available as Parquet files hosted on Amazon S3.

Here's how to list the available files:

aws s3 ls s3://fsq-os-places-us-east-1/release/dt=2024-11-19/places/parquet/

I got back places-00000.snappy.parquet through places-00024.snappy.parquet, each file around 455MB for a total of 10.6GB of data.

I ran duckdb and then used DuckDB's ability to remotely query Parquet on S3 to explore the data a bit more without downloading it to my laptop first:

select count(*) from 's3://fsq-os-places-us-east-1/release/dt=2024-11-19/places/parquet/places-00000.snappy.parquet';

This got back 4,180,424 - that number is similar for each file, suggesting around 104,000,000 records total.

Update: DuckDB can use wildcards in S3 paths (thanks, Paul) so this query provides an exact count:

select count(*) from 's3://fsq-os-places-us-east-1/release/dt=2024-11-19/places/parquet/places-*.snappy.parquet';

That returned 104,511,073 - and Activity Monitor on my Mac confirmed that DuckDB only needed to fetch 1.2MB of data to answer that query.

I ran this query to retrieve 1,000 places from that first file as newline-delimited JSON:

copy (
    select * from 's3://fsq-os-places-us-east-1/release/dt=2024-11-19/places/parquet/places-00000.snappy.parquet'
    limit 1000
) to '/tmp/places.json';

Here's that places.json file, and here it is imported into Datasette Lite.

Finally, I got ChatGPT Code Interpreter to convert that file to GeoJSON and pasted the result into this Gist, giving me a map of those thousand places (because Gists automatically render GeoJSON):

# 20th November 2024, 5:52 am / open-source, gis, foursquare, datasette-lite, parquet, duckdb, code-interpreter, ai-assisted-programming, geojson

OpenAI Public Bug Bounty. Reading this investigation of the security boundaries of OpenAI's Code Interpreter environment helped me realize that the rules for OpenAI's public bug bounty inadvertently double as the missing details for a whole bunch of different aspects of their platform.

This description of Code Interpreter is significantly more useful than their official documentation!

Code execution from within our sandboxed Python code interpreter is out of scope. (This is an intended product feature.) When the model executes Python code it does so within a sandbox. If you think you've gotten RCE outside the sandbox, you must include the output of uname -a. A result like the following indicates that you are inside the sandbox -- specifically note the 2016 kernel version:

Linux 9d23de67-3784-48f6-b935-4d224ed8f555 4.4.0 #1 SMP Sun Jan 10 15:06:54 PST 2016 x86_64 x86_64 x86_64 GNU/Linux

Inside the sandbox you would also see sandbox as the output of whoami, and as the only user in the output of ps.

# 14th November 2024, 11:44 pm / security, generative-ai, openai, ai, llms, code-interpreter

pip install GPT (via) I've been uploading wheel files to ChatGPT in order to install them into Code Interpreter for a while now. Nico Ritschel built a better way: this GPT can download wheels directly from PyPI and then install them.

I didn't think this was possible, since Code Interpreter is blocked from making outbound network requests.

Nico's trick uses a new-to-me feature of GPT Actions: you can return up to ten files from an action call and ChatGPT will download those files to the same disk volume that Code Interpreter can access.

Nico wired up a Val Town endpoint that can divide a PyPI wheel into multiple 9.5MB files (if necessary) to fit the file size limit for files returned to a GPT, then uses prompts to tell ChatGPT to combine the resulting files and treat them as installable wheels.

# 21st July 2024, 5:54 am / python, generative-ai, code-interpreter, chatgpt, ai, pypi, llms

An example running DuckDB in ChatGPT Code Interpreter (via) I confirmed today that DuckDB can indeed be run inside ChatGPT Code Interpreter (aka "data analysis"), provided you upload the correct wheel file for it to install. The wheel file it needs is currently duckdb-1.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl from the PyPI releases page - I asked ChatGPT to identify its platform, and it said that it needs manylinux2014_x86_64.whl wheels.

Once the wheel in installed ChatGPT already knows enough of the DuckDB API to start performing useful operations with it - and any brand new features in 1.0 will work if you tell it how to use them.

# 17th July 2024, 9:04 pm / duckdb, generative-ai, code-interpreter, chatgpt, ai, llms

What AI can do with a toolbox... Getting started with Code Interpreter. Ethan Mollick has been doing some very creative explorations of ChatGPT Code Interpreter over the past few months, and has tied a lot of them together into this useful introductory tutorial.

# 12th July 2023, 8:57 pm / ethan-mollick, generative-ai, openai, chatgpt, ai, llms, code-interpreter

Latent Space: Code Interpreter == GPT 4.5 (via) I presented as part of this Latent Space episode over the weekend, talking about the newly released ChatGPT Code Interpreter mode with swyx, Alex Volkov, Daniel Wilson and more. swyx did a great job editing our Twitter Spaces conversation into a podcast and writing up a detailed executive summary, posted here along with the transcript. If you’re curious you can listen to the first 15 minutes to get a great high-level explanation of Code Interpreter, or stick around for the full two hours for all of the details.

Apparently our live conversation had 17,000+ listeners!

# 10th July 2023, 10:06 pm / swyx, generative-ai, chatgpt, ai, llms, speaking, podcasts, code-interpreter

ChatGPT Plugins Don’t Have PMF. Sam Altman was recently quoted (in a since unpublished blog post) noting that ChatGPT plugins have not yet demonstrated product market fit.

This matches my own usage patterns: I use the “browse” and “code interpreter” modes on a daily basis, but I’ve not found any of the third party developer plugins to stick for me yet.

I like Matt Rickard’s observation here: “Chat is not the right UX for plugins. If you know what you want to do, it’s often easier to just do a few clicks on the website. If you don’t, just a chat interface makes it hard to steer the model toward your goal.”

# 8th June 2023, 4:59 am / generative-ai, openai, chatgpt, ai, llms, code-interpreter