Repository History
47 repositories tagged with machine-learning
Shimmy: A Pure-Rust WebGPU Inference Engine for GGUF Models
Shimmy is a high-performance, pure-Rust WebGPU inference engine designed for GGUF models. It offers OpenAI-API compatibility, enabling local and private execution of large language models without Python or C++ dependencies. This single-binary solution provides rapid startup and a low memory footprint, making it an efficient alternative for local AI inference.
Awesome-Self-Evolving-Agents: A Curated List for AI Agent Research
Awesome-Self-Evolving-Agents is a comprehensive GitHub repository offering a curated collection of resources on self-evolving agents. It includes a systematic survey, research papers, benchmarks, and open-source projects, providing valuable insights into this rapidly advancing field of AI. This repository serves as an essential guide for researchers and developers exploring model-centric, environment-centric, and co-evolutionary approaches.
Awesome-Self-Improving-Agents: A Curated List for Agentic AI Self-Improvement
Awesome-Self-Improving-Agents is a comprehensive GitHub repository featuring a curated and continuously updated list of resources on self-improvement in foundation model-based agentic systems. It serves as a central hub for researchers and practitioners, offering papers, benchmarks, and various media. This resource is essential for anyone exploring the cutting edge of self-evolving AI agents.
CQ: An Open Standard for Shared Agent Learning by Mozilla.ai
CQ is an open standard designed to prevent AI agents from repeatedly making the same mistakes by enabling them to persist, share, and query collective knowledge. It facilitates a structured exchange of ideas, allowing agents to learn from each other's experiences and accelerate development. This system helps agents avoid redundant debugging and discover solutions more efficiently.
awesome-ai-agents: A Curated List of AI Agent Resources
awesome-ai-agents is a comprehensive, curated list of resources for building and understanding AI agents. It covers frameworks, tools, platforms, research papers, and more, making it an essential guide for anyone exploring the rapidly evolving field of autonomous AI systems.
SkillOpt: Optimizing Self-Evolving Agent Skills for LLMs
SkillOpt is an innovative text-space optimizer from Microsoft that enables the training of reusable natural-language skills for frozen LLM agents. It approaches skill development with the rigor of deep-learning optimization, using trajectory-driven edits and validation-gated updates. This results in deployable `best_skill.md` artifacts that significantly boost agent performance across various benchmarks and models without modifying model weights.
Awesome AI Agents 2026: The Ultimate List of AI Tools and Frameworks
This repository, `awesome-ai-agents-2026`, is a comprehensive and frequently updated collection of over 340 AI agents, frameworks, and tools across more than 20 categories. It serves as an essential resource for developers and researchers looking to explore the rapidly evolving landscape of artificial intelligence in 2026, covering everything from coding agents to creative AI and governance.
awesome-ai: A Curated List of 400+ AI APIs, Tools, and Frameworks
The awesome-ai repository by edwardtay offers a comprehensive, curated list of over 400 AI APIs, tools, frameworks, and platforms. Spanning more than 40 categories, it serves as an invaluable resource for developers and researchers navigating the vast landscape of artificial intelligence. This list helps users discover solutions for LLMs, agents, image/video generation, MLOps, and more.
AI-Agents-Projects-Tutorials: Comprehensive Guide to AI Agent Development
The AI-Agents-Projects-Tutorials repository offers an extensive collection of code implementations and tutorials for building advanced AI agents. It covers fundamental concepts such as multi-agent systems, memory management, planning, and reasoning loops. This resource is ideal for developers and researchers seeking practical insights into agentic AI development.
AsterMind-ELM: Modular Extreme Learning Machine for On-Device ML in JS/TS
AsterMind-ELM is a JavaScript/TypeScript library that modernizes Extreme Learning Machines (ELMs) for instant, on-device machine learning in web applications. It offers advanced features like Kernel ELMs, Online ELM, and DeepELM, enabling fast, private, and interpretable AI directly in the browser. This framework allows for building decentralized, self-training ML systems without relying on GPUs or servers.
Awesome-pytorch-list: A Comprehensive Collection of PyTorch Resources
The "Awesome-pytorch-list" is an extensive GitHub repository curating a wide range of PyTorch-related content. It serves as a valuable resource for developers and researchers, offering a structured overview of models, implementations, helper libraries, and tutorials. This list simplifies the discovery of essential tools and learning materials within the PyTorch ecosystem.
Axolotl: Streamlining LLM Fine-tuning with a Powerful Open-Source Framework
Axolotl is a comprehensive, free, and open-source framework designed to simplify the post-training and fine-tuning processes for large language models (LLMs). It offers extensive model support, diverse training methods, and robust performance optimizations, making it an invaluable tool for researchers and developers. With easy configuration and cloud-ready deployment, Axolotl empowers users to efficiently customize and enhance LLMs.