A comprehensive open-source guide covering prompt engineering techniques, RAG, AI agents, and LLM optimization. Includes tutorials, research papers, tools, and notebooks for working effectively with large language models.
Open source. Open possibilities.
Discover quality open-source projects, submit projects anonymously, and claim and edit your own project.
A little curiosity. A world of open source.
THE FIRST COLLECTIONTabby is a self-hosted, open-source AI coding assistant designed as an on-premises alternative to GitHub Copilot.
A curated collection of 90+ hands-on AI engineering tutorials and projects covering LLMs, RAG, agents, MCP, multimodal apps, fine-tuning and evaluation, organized by beginner, intermediate and advanced difficulty.
Made With ML is an open-source course and codebase teaching how to design, develop, deploy and iterate on production-grade machine learning applications, covering MLOps, testing, serving, CI/CD and continual learning.
Open-source collection of 100+ AI agents, agent skills, and RAG apps. Apache-2.0, hand-built and tested end-to-end, works with Claude, GPT, Gemini, DeepSeek, Llama, Qwen.
AutoAgent is a zero-code framework that lets users create and deploy LLM agents and workflows using natural language alone, with features like automated agent profiling, tool creation, and multi-agent orchestration.
A self-hosted web UI for AI coding agents: Rust backend (axum, tokio, SQLite) with an embedded Flutter web frontend, pluggable providers for Devin CLI, OpenCode and Codex CLI, browser login, sessions and file handling.
Oumi is an open-source platform for the full foundation-model lifecycle: data preparation, training and fine-tuning (SFT, LoRA, QLoRA, GRPO), evaluation, data synthesis with LLM judges, and deployment via vLLM/SGLang, with a CLI and cloud job launching.
FastMCP is a Python framework for building Model Context Protocol (MCP) applications, enabling seamless integration of LLMs with tools and data via servers, clients, and interactive UIs.
A free 21-lesson Microsoft course for beginners to build generative AI applications, covering LLMs, prompt engineering, RAG, agents, fine-tuning, and more, with Python/TypeScript examples.