Free to explore · curated by OneWave AI
1,004 apps, agents and plugins from GitHub, ranked on how actively they are maintained rather than on stars. Security review is in progress and every project shows exactly what has and has not been checked.
Review is in progress. 1,004 projects are queued and none has been approved yet. Everything unreviewed is discovery metadata imported from GitHub — listed, not vetted.
Ranked by total stars at the last daily refresh. This is not stars earned during the selected period. Star counts in the AI category are known to be inflated by fake-star campaigns, so activity and license are shown first.
🤗 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.
Stable Diffusion web UI
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
Making large AI models cheaper, faster and more accessible
🚀Clone a voice in 5 seconds to generate arbitrary speech in real-time
💫 Industrial-strength Natural Language Processing (NLP) in Python
Visualizer for neural network, deep learning and machine learning models
Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.
Machine Learning Systems
ncnn is a high-performance neural network inference framework optimized for the mobile platform
🤗 The largest hub of ready-to-use datasets for AI models with fast, easy-to-use and efficient data manipulation tools
Best Practices on Recommendation Systems
☁️ Build multimodal AI applications with cloud-native stack
Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.
Open standard for machine learning interoperability
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.
🐫 CAMEL: The first and the best multi-agent framework. Finding the Scaling Law of Agents. https://www.camel-ai.org
🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.
Essential Cheat Sheets for deep learning and machine learning researchers https://medium.com/@kailashahirwar/essential-cheat-sheets-for-machine-learning-and-deep-learning-researchers-efb6a8ebd2e5
Open-source simulator for autonomous driving research.
20+ high-performance LLMs with recipes to pretrain, finetune and deploy at scale.
人工智能学习路线图，整理近200个实战案例与项目，免费提供配套教材，零基础入门，就业实战！包括：Python，数学，机器学习，数据分析，深度学习，计算机视觉，自然语言处理，PyTorch tensorflow machine-learning,deep-learning data-analysis data-mining mathematics data-science artificial-intelligence python tensorflow tensorflow2 caffe keras pytorch algorithm numpy pandas matplotlib seaborn nlp cv等热门领域
🐍 Geometric Computer Vision Library for Spatial AI