I’m especially curious about where the Pydantic team wants to take Monty. The minimal-interpreter approach feels like a good starting point for AI workloads, but the long tail of Python semantics is brutal. There is a trade-off between keeping the surface area small (for security and predictability) and providing sufficient language capabilities to handle non-trivial snippets that LLMs generate to do complex tasks
disclaimer: i work at E2B, opinions my own
But to be clear, we're not even targeting the same "computer use" use case I think e2b, daytona, cloudflare, modal, fly.io, deno, google, aws are going after - we're aiming to support programmatic tool calling with minimal latency and complexity - it's a fundamentally different offering.
Chill, e2b has its use case, at least for now.
Perhaps you're using v8 isolates, which then you're back into the "heavily restricted environment within the process" and you lose the things you'd want your AI to be able to do, and even then you still have to sandbox the hell out of it to be safe and you have to seriously consider side channel leaks.
And even after all of that you'd better hope you're staying up to date with patches.
MicroVMs are going to just be way simpler IMO. I don't really get the appeal of using V8 for this unless you have platform/ deployment limitations. Talking over Firecracker's vsock is extremely fast. Firecracker is also insanely safe - 3 CVEs ever, and IMO none are exploitable.