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.

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Links from the show

Samuel Colvin: github.com

CPython: github.com

IronPython: ironpython.net

Jython: www.jython.org

Pyodide: pyodide.com

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

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