A free, MIT-licensed course of 542 lessons on AI engineering, implementing model internals, retrieval, and agent runtimes from scratch.
Agent memory system that lets AI agents learn from experience over time, not just recall conversation history.
Co-op Anything: point your AI agent at any single-player game and play it with friends (online and/or splitscreen!). MCP server and playbook to let you reverse engineer, fix, test, ship and share what it learns.
《深入理解 AI Agent:设计原理与工程实践》(李博杰 著)开源主仓库:全书正文、编译版 PDF 与按章配套代码
Claudemy: an open-source alternative to Udemy and Coursera. Give Claude a topic, it researches the web and builds a course of 2-minute audio lessons with cited sources. A free Claude Code skill.
DeepTutor: Lifelong Personalized Tutoring. https://deeptutor.info/.
YuE2: frontier music generation with symbolic planning, zero-shot covers, and agentic music editing.
The 30 Days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than 100 days. Follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw
Curated list of project-based tutorials
A trilingual (繁中 / English / 简中) learning roadmap for agentic AI: from LLM basics to multi-agent systems, with 240+ curated resources and hands-on examples. 中文 AI agent 學習地圖。
Autoharness — a self-learning skill layer for Claude Code — distills skills from your real sessions, updates them as you work, and prunes the ones that stop getting used. No daemon, no benchmark.
SGLang is a high-performance serving framework for large language models and multimodal models.
收录最全、更新最快的技能Skills商店:精选原创技能包(涵盖文档处理、内容创作、编程开发、机器学习、自动化工作流),全部打包好可直接安装使用!同时自动抓取GitHub上万个Skills项目,按分类、更新时间、Star数量整理。The most comprehensive and frequently updated AI Agent skill library, featuring curated skill packs across document processing, content creation, programming, machine learning, automated workflows, and many more domains.
Bash is all you need - A nano claude code–like 「agent harness」, built from 0 to 1
Curated reverse-engineering tools and learning resources for binaries, apps, firmware, formats, and protocols.
Open Data Platform for analysts, quants and AI agents.
Face Recognition, Face Liveness Detection, Face Anti-Spoofing, Face Detection, Face Landmarks, Face Compare, Face Matching, Face Pose, Face Expression, Face Attributes, Face Templates Extraction, Face Landmarks
Ultralytics YOLO27, YOLO26, YOLO11, YOLOv8 — object detection, instance segmentation, semantic segmentation, image classification, pose estimation, object tracking
戒指老爷爷 laoyeye · 21 件辅助思考的提示语和 skill,原文逐字收录。问清问题、学习、解决问题、决策、动手、认识你自己。说出你卡在哪,敲 /laoyeye 让老爷爷指路。
Local UI to run and train LLMs and diffusion models. Supports GGUF, MLX, Qwen3.8, DeepSeek-V4, MiniMax-H3, Gemma 4, FLUX and more.
Fine-tune LLMs from one YAML. Layer streaming trains an 8B model on a 4 GB laptop GPU.
An open-source AI agent for social media — discover trends, create content, publish everywhere, and learn what works across Xiaohongshu, Douyin, Zhihu, Bilibili, and more.🎨一个开源的 AI 社交媒体智能体——发现热点趋势、创作内容、一键发布至各大平台,并学习分析哪些内容真正有效,覆盖小红书、抖音、知乎、哔哩哔哩等平台。
DiscoveryRSI: Recursively Learning to Discover Valuable Hypotheses
Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, including supervised learning, market dynamics modeling, and RL, and is now equipped with https://github.com/microsoft/RD-Agent to automate R&D process.
ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework, no lock-in — works with Claude Code, Codex, OpenClaw, or any LLM agent.
A community-supported supercharged document management system: scan, index and archive all your documents
My Python scripts to make high-quality figures for publications in top AI conferences and journals.
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
FastAPI framework, high performance, easy to learn, fast to code, ready for production
VoxCPM2: Tokenizer-Free TTS for Multilingual Speech Generation, Creative Voice Design, and True-to-Life Cloning
Efficient Long-Context LLM Serving with Head-Aware KV Reuse and SegPagedAttention
Remory: Learning Residual Memory for Context Compaction
Infrastructure for continually self‑improving agents
🤗 LeRobot: Making AI for Robotics more accessible with end-to-end learning
Official PyTorch implementation of VisionHOPE: Visual Backbones as Self-Modifying Learning Systems.
More Methods, Better Agents? A Reality Check on Self-Distillation in Agentic Reinforcement Learning
Faster Whisper transcription with CTranslate2
Symbolic Learning in Python and Julia
Streamlit — A faster way to build and share data apps.
The absolute trainer to light up AI agents.
A Claude Code / Codex plugin that helps you learn how to build while AI writes the code.
All Algorithms implemented in Python
GEO-first SEO skill for Claude Code. Comprehensive AI search optimization for any website — citability scoring, AI crawler analysis, brand authority, schema markup, platform-specific optimization, and PDF reports. If you want learn how to sell this to real businesses, check out the skool community
Learning to Recover from LLM Agent Failures at Test Time
Experiments on whether language models can learn to detect read-only activation probes
Memory library for building stateful agents
Interactive, source-linked ML workflow diagrams written by your coding assistant (Copilot, Codex or Claude Code) and opened in VS Code.
Neuro-symbolic agents with verified memory. MCP math tools, an algebra repair Skill, and an open challenge. Verify solutions, learn checked rules, and reuse.
The first exam for code agents in the physical world: frozen robot tasks from many benchmark suites, each answered with a program and verified in a separate sandbox. Runs on Harbor.
Welcome to the SDE Interview Preparation Roadmap! This repository is not just about my personal journey; it's a collaborative space for collective learning. As I prepare for Software Development Engineer (SDE) interviews, I've created a comprehensive checklist to guide my preparation.
🔥 On-Policy Self-Distillation in Diffusion Models
Creating realistic RL environments requires collaboration between researchers, engineers, and domain experts across many dimensions: artifacts, environments tools, dynamism of the environment, reproducibility, and more. There is no open source framework for building these environments effectively. Until now.
An open-source solution for a SMS API Server hosted locally
A 4B grounding foundation model for physical intelligence. Unifies detection, referring, pointing, OCR, GUI, and layout grounding, achieving SOTA overall across 34 benchmarks and improving robot generalization and data efficiency in controlled evaluations.
阿布量化交易系统(股票,期权,期货,比特币,机器学习) 基于python的开源量化交易,量化投资架构
RF-DETR is a real-time object detection and segmentation model architecture developed by Roboflow, SOTA on COCO, designed for fine-tuning. [ICLR 2026]
⚡ TabPFN: Foundation Model for Tabular Data ⚡
Algorithmic Trading in Python with Machine Learning
A local acoustic control system that learns user-defined labels for the surface or location of knocks, such as desk or wall. It also counts threshold-qualified knocks within each captured window. By default, a frozen Microsoft BEATs Iter3+ AS2M encoder creates 768-D audio embeddings and an incremental classifier learns the user's labels.
SOTA: strategy-level reinforcement learning for options portfolio management — code release for the paper