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A little curiosity. A world of open source.
THE FIRST COLLECTIONHello-Agents is a systematic intelligent agent learning tutorial launched by the Datawhale community, covering core agent principles, classic paradigm construction, framework development, memory and retrieval, context engineering, communication protocols, Agentic-RL training and comprehensive project practice, suitable for AI developers and self-learners to build AI-native agents from scratch.
AgentGuide is an open-source knowledge base for AI Agent development, research, and job hunting, offering systematic learning paths, hands-on projects, and interview question banks to guide learners from beginner to job offer.
A hands-on introduction to digital video technology for developers and engineers, covering image representation, color models, codecs, and streaming with practical FFmpeg examples.
A curated list of programming tutorials for learning by building real applications from scratch, organized by programming language and covering compilers, kernels, games, web and mobile apps.
17-lesson tutorial teaching harness engineering for AI agents: build the tools, knowledge, permissions, and context management around an LLM agent loop, using Claude Code as the reference design. Runnable Python examples progress from the agent loop to a goal loop.
A beginner-friendly open-source guide that walks new contributors through the standard GitHub workflow: fork, clone, branch, edit, commit, push, and pull request, with extensive translations and GUI tool tutorials.
Free, open-source AI engineering curriculum from first principles: 523 lessons in 20 phases (~342 hours), with math-to-production coverage in Python, TypeScript, Rust, and Julia. Every lesson ships a reusable prompt, skill, agent, or MCP server; includes an installable AI tutor and Claude certification prep.
Noob2Builder is an open-source course where an Agent teaches in the main chat, designed for absolute beginners. It covers everything from computer and AI basics to Git/GitHub, building and publishing your first project, and creating Agents—14 fully elective courses emphasizing real hands-on work and verifiable learning evidence.
A self-contained computer graphics course: 13 chapters of notes, interactive WebGL demos, worked exercises and automated checks covering the rendering pipeline, transformations, lighting, textures, collision detection and ray tracing.
A hands-on tutorial teaching how to use FFmpeg as a library (libav) in C, covering video basics, command-line operations, and coding chapters on decoding, remuxing, and transcoding.
A curated directory of the React ecosystem, covering frameworks, component libraries, state management, styling, routing, testing, forms, charts, renderers, and React Native resources.
This open-source collection features AI Agent books, tutorials, and code repositories from GitHub, covering LLM Agent resources, courses, and frameworks. It supports daily automatic Star count updates, sorting by popularity, and provides recommended reading paths for developers and learners.
