When LLMs write code to accomplish a task, that code has to actually run somewhere. And right now, the options aren't great. Spin up a sandboxed container and you're paying a full second of cold start overhead plus the complexity of another service. Let the LLM loose on your actual machine and... well, you'd better be watching.

On this episode, I sit down with Samuel Colvin, creator of Pydantic, now at 10 billion downloads, to explore Monty, a Python interpreter written from scratch in Rust, purpose-built to run LLM-generated code. It starts in microseconds, is completely sandboxed by design, and can even serialize its entire state to a database and resume later. We dig into why this deliberately limited interpreter might be exactly what the AI agent era needs.

Episode sponsors

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Links from the show

Guest
Samuel Colvin: github.com

CPython: github.com
IronPython: ironpython.net
Jython: www.jython.org
Pyodide: pyodide.org
monty: github.com
Pydantic AI: pydantic.dev
Python AI conference: pyai.events
bashkit: github.com
just-bash: github.com
Narwhals: narwhals-dev.github.io
Polars: pola.rs
Strands Agents: aws.amazon.com
Subscribe Running Pydantic’s Monty Rust sandboxed Python subset in WebAssembly: simonwillison.net
Rust Python: github.com
Valgrind: valgrind.org
Cod Speed: codspeed.io

Watch this episode on YouTube: youtube.com
Episode #541 deep-dive: talkpython.fm/541
Episode transcripts: talkpython.fm

Theme Song: Developer Rap
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