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.
Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. 🐳Docker-friendly.⚡Always in sync with Sharepoint, Google Drive, S3, Kafka, PostgreSQL, real-time data APIs, and more.
Manually curated collection of resources for frontend web developers.
Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
AirLLM 70B inference with single 4GB GPU
Turn any technical book PDF into a Claude Code skill — ready to study, reference, and use while you work.
Machine Learning Systems
50+ tutorials and implementations for Generative AI Agent techniques, from basic conversational bots to complex multi-agent systems.
A reactive notebook for Python — run reproducible experiments, query with SQL, execute as a script, deploy as an app, and version with git. Stored as pure Python. All in a modern, AI-native editor.
Best Practices on Recommendation Systems
End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.
Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.
This is the homepage of a new book entitled "Mathematical Foundations of Reinforcement Learning."
Sample code and notebooks for Generative AI on Google Cloud, with Gemini Enterprise Agent Platform
A reference containing Styles and Keywords that you can use with MidJourney AI. There are also pages showing resolution comparison, image weights, and much more!
Tutorials, assignments, and competitions for MIT Deep Learning related courses.
:octocat:GitHub最全的前端资源汇总仓库（包括前端学习、开发资源、求职面试等）
Best Practices, code samples, and documentation for Computer Vision.
[NeurIPS 2024 Best Paper Award][GPT beats diffusion🔥] [scaling laws in visual generation📈] Official impl. of "Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction". An *ultra-simple, user-friendly yet state-of-the-art* codebase for autoregressive image generation!
Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.
22 prompt engineering techniques with hands-on Jupyter Notebook tutorials, from fundamental concepts to advanced strategies for leveraging LLMs.
Growth Engineering: The Definitive Guide。全栈增长工程师指南
Become a cracked AI/ML researcher/engineer with this unconventional textbook covering maths, computing, and ML with intuition.
OpenMMLab Multimodal Advanced, Generative, and Intelligent Creation Toolbox. Unlock the magic 🪄: Generative-AI (AIGC), easy-to-use APIs, awsome model zoo, diffusion models, for text-to-image generation, image/video restoration/enhancement, etc.
Gemma 4 26B-A4B inference in ~2 GB of RAM on any M-series MacBook