Sandboxing Python for LLM/agent-generated code is usually done with containers, microVMs, or server-side WASM runtimes. I wanted something that runs in the browser tab itself: no server, no round-trip, no host process. So I spend few months writing a bytecode compiler + stack VM in Rust that ships as a standalone 170 KB WASM. The demo is a Rosenblatt perceptron training on an OR gate entirely in the browser. Language surface covers classes, async/await, pattern matching, decorators, generators, f-strings… enough for non-trivial agent code. A bit of the design: * Sandboxed by construction; per-VM caps on heap, ops, call depth. * Native modules ship as separate .wasm via a sealed plugin ABI (handle-based, language-agnostic). * Optional SHA-256 integrity on URL imports; content-addressed cache with drift detection. * No stdlib by design, host explicitly grants every capability the script can touch. Demo: https://demo.edgepython.com/ Repo: https://github.com/dylan-sutton-chavez/edge-python Happy to discuss :). submitted by /u/Healthy_Ship4930
Originally posted by u/Healthy_Ship4930 on r/ArtificialInteligence