A curated guide to code sandboxing solutions, covering technologies like MicroVMs, application kernels, language runtimes, and containerization. It provides a feature matrix, in-depth platform profiles (e2b, Daytona, microsandbox, WebContainers, Replit, Cloudflare Workers, Fly.io, Kata Containers), and a decision framework for choosing the right sandboxing solution based on security, performance, workload type, and hosting preferences.
This article details a method for training large language models (LLMs) for code generation using a secure, local WebAssembly-based code interpreter and reinforcement learning with Group Relative Policy Optimization (GRPO). It covers the setup, training process, evaluation, and potential next steps.
A Microsoft engineer demonstrates how WebAssembly modules can run alongside containers in Kubernetes environments, offering benefits like reduced size and faster cold start times for certain workloads.
