
0:00
1:05:44
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
Talk Python Courses
Python in Production
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
Theme Song: Developer Rap
🥁 Served in a Flask 🎸: talkpython.fm/flasksong
---== Don't be a stranger ==---
YouTube: youtube.com/@talkpython
Bluesky: @talkpython.fm
Mastodon: @[email protected]
X.com: @talkpython
Michael on Bluesky: @mkennedy.codes
Michael on Mastodon: @[email protected]
Michael on X.com: @mkennedy
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
Talk Python Courses
Python in Production
Links from the show
GuestSamuel 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
Theme Song: Developer Rap
🥁 Served in a Flask 🎸: talkpython.fm/flasksong
---== Don't be a stranger ==---
YouTube: youtube.com/@talkpython
Bluesky: @talkpython.fm
Mastodon: @[email protected]
X.com: @talkpython
Michael on Bluesky: @mkennedy.codes
Michael on Mastodon: @[email protected]
Michael on X.com: @mkennedy
Mais episódios de "Talk Python To Me"



Não percas um episódio de “Talk Python To Me” e subscrevê-lo na aplicação GetPodcast.








