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Docker Sandboxes

Docker Sandboxes is Docker’s isolated execution environment for AI coding agents such as Claude Code, Gemini CLI, Copilot CLI, Codex, OpenCode, and Kiro. It lets agents run package installs, modify files, start services, and even use Docker inside a disposable microVM while keeping the host machine protected. The product is aimed at developers and teams that want the speed of unattended agent work without giving agents uncontrolled access to local credentials, filesystem paths, or networks. It offers configurable network and filesystem controls, fast setup on macOS and Windows, and team-oriented admin options. It is notable because safe YOLO-mode execution is becoming a core requirement as coding agents move from supervised demos to long-running development tasks.

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codemap is an MIT-licensed project brain for AI coding tools that gives LLMs instant architectural context from your codebase without burning tokens. It generates a fast tree/context view, dependency flow, dependency blast-radius analysis, and a layered handoff format for cross-agent continuation, then exposes everything through a JSON context bundle and an MCP server compatible with Claude Code and Codex. A built-in Codex plugin and community skill registry make it easy to install and share. Developers use codemap to onboard agents to large repos in seconds, keep session continuity across handoffs, and scope the impact of a change before running it.

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Ollama is a local AI platform for running, managing, and sharing open models on your own machine or private infrastructure. It makes it easy to pull models, serve them through an API, and integrate local inference into developer workflows without relying on a fully managed cloud stack. Teams use Ollama for privacy-sensitive assistants, internal tools, offline experimentation, and rapid testing of open-weight models across laptops, workstations, and servers. It is especially useful for developers, operators, and AI builders who want quick setup with less operational overhead. What makes Ollama distinctive is how approachable it is: it packages model runtime, distribution, and deployment into a streamlined experience that helps people get productive with local AI in minutes instead of spending days on configuration.

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