Simon Willison on sandboxed Python execution via MicroPython and WASM
Simon Willison documents a technique for running Python code in a sandboxed environment using MicroPython compiled to WebAssembly (WASM). The approach is relevant to AI agent and tool-use contexts where safe code execution is needed. This is a practical engineering post from a respected practitioner voice.
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Building a safe, effective sandbox to enable Codex on Windows
OpenAI describes the engineering work behind a secure sandbox environment for running Codex coding agents on Windows. The sandbox enforces controlled file access and network restrictions to enable safe, efficient agentic code execution. This is part of OpenAI's broader effort to deploy coding agents in production environments with appropriate isolation guarantees.
Sandcastle: TypeScript Library for Orchestrating Sandboxed Coding Agents
Sandcastle is an open-source TypeScript library by Matt Pocock that provides a `sandcastle.run()` API for orchestrating sandboxed coding agents. The project has gained significant traction on GitHub with 5,549 total stars and 159 stars added in a single day. It targets developers building agent workflows that require isolated code execution environments.
Quoting Armin Ronacher
Simon Willison quotes Armin Ronacher in a brief commentary post. The body content is empty, so the specific substance of the quote is unavailable, but given the source and subjects involved—both prominent figures in Python/developer tooling communities who have written extensively about AI coding tools and agents—the post likely touches on AI-assisted development or related tooling themes.
Simon Willison adds document context to OpenAI WebRTC Audio Session tool
Simon Willison documents an update to his OpenAI WebRTC Audio Session tool that adds document context capabilities, allowing audio sessions to incorporate document content. The post covers practical integration of OpenAI's real-time audio API with document-grounded context. This is a hands-on tooling walkthrough relevant to practitioners building voice-enabled AI applications.
Running Codex Safely at OpenAI
OpenAI published a blog post describing the security architecture used to run Codex as a coding agent internally, covering sandboxing, human approval workflows, network policies, and agent-native telemetry. The post is aimed at supporting enterprise adoption of coding agents by demonstrating safe and compliant deployment patterns. It provides operational detail on how OpenAI itself governs agentic code execution in production.
Microsoft RAMPART: pytest-native safety and security testing framework for agentic AI
Microsoft has released RAMPART, an open-source Python framework for safety and security testing of agentic AI applications, built natively on pytest. The repository is gaining traction on GitHub with 301 total stars and 77 new stars today. It targets the growing need for structured evaluation tooling specifically designed for AI agents rather than traditional software.
E2B: Open-Source Secure Sandbox Environment for Enterprise AI Agents
E2B is an open-source project providing secure, sandboxed execution environments designed for enterprise-grade AI agents with access to real-world tools. The repository has accumulated 12,290 GitHub stars with 31 new stars today, indicating steady community interest. It targets the agent-tool ecosystem by offering isolated runtime environments where agents can safely execute code and interact with external systems.
Wasmer used OpenAI Codex with GPT-5.5 to build a Node.js edge runtime 10-20x faster
Wasmer used OpenAI's Codex powered by GPT-5.5 to build a Node.js runtime for edge computing, reporting 10x to 20x development acceleration and shipping in weeks instead of months. The case study is published on the OpenAI blog as a deployment showcase. It provides concrete evidence of agentic coding tools compressing development timelines for systems-level infrastructure work.