> Instead, it let's you run safely run Python code written by an LLM embedded in your agent, with startup times measured in single digit microseconds not hundreds of milliseconds.

Perhaps if the interpreter is in turn embedded in the executable and runs in-process, but even a do-nothing `uv` invocation takes ~10ms on my system.

I like the idea of a minimal implementation like this, though. I hadn't even considered it from an AI sandboxing perspective; I just liked the idea of a stdlib-less alternative upon which better-thought-out "core" libraries could be stacked, with less disk footprint.

Have to say I didn't expect it to come out of Pydantic.

I'm of the mind that it will be better to construct more strict/structured languages for AI use than to reuse existing ones.

My reasoning is 1) AIs can comprehend specs easily, especially if simple, 2) it is only valuable to "meet developers where they are" if really needing the developers' history/experience which I'd argue LLMs don't need as much (or only need because lang is so flexible/loose), and 3) human languages were developed to provide extreme human subjectivity which is way too much wiggle-room/flexibility (and is why people have to keep writing projects like these to reduce it).

We should be writing languages that are super-strict by default (e.g. down to the literal ordering/alphabetizing of constructs, exact spacing expectations) and only having opt-in loose modes for humans and tooling to format. I admit I am toying w/ such a lang myself, but in general we can ask more of AI code generations than we can of ourselves.

I like the idea a lot but it's still unclear from the docs what the hard security boundary is once you start calling LLMs - can it avoid "breaking out" into the host env in practice?