May 30, 2026
Learn to build a high-performance LLM inference engine from scratch using C++ and CUDA, covering everything from safetensors to PagedAttention.
Explore MiniMind-O, a tiny 0.1B parameter Omni model capable of listening, seeing, and speaking, designed for full-stack transparency and local training.
Stop treating LLMs like black boxes. This comprehensive guide walks you through building a modern, LLaMA-style language model from scratch with fully annotated code.
Discover 40 battle-tested OpenClaw AI agent use cases that automate your work and life. From Chinese ecosystem integrations like Feishu, DingTalk, and WeCom bots to global scenarios for content creation, DevOps self-healing servers, and multi-agent teams. Beginner-friendly with copy-paste prompts, setup guides, and difficulty ratings. Transform OpenClaw into your 24/7 AI employee today!
Discover 'Teaching Boyfriend LLM' - the ultimate GitHub repository with 70+ Chinese PDF lecture notes covering LLM fundamentals, fine-tuning, RLHF, RAG, Agents, inference optimization, and cutting-edge models like DeepSeek R1, Qwen3, Llama3. Perfect for developers, students, and AI engineers seeking a systematic path from zero to expert. Organized by topic with clear difficulty ratings and learning progression.
Looking for practical ways to harness OpenClaw? Our Awesome OpenClaw Use Cases repository is an all‑in‑one resource packed with real‑world automation recipes, productivity boosters, and AI‑driven workflows. From social media digests to multi‑agent content factories, the list covers every niche. Learn how to contribute, navigate the categories, and understand the security considerations that come with these skills. Whether you’re a developer, a business owner, or an automation enthusiast, this guide will help you unlock OpenClaw’s full potential.
Discover the Android AI Sample Catalog – a free, open‑source collection of cutting‑edge AI demos that showcase Gemini, Imagen, and on‑device GenAI models. Learn how to set up Firebase, run the app in Android Studio, and explore a variety of sample functionalities from image generation to video summarization. Whether you’re a developer looking to prototype quickly or a hobbyist curious about generative AI, this guide walks you through getting started, sample highlights, and best practices for contributing and extending the catalog.
Discover Andrej Karpathy’s four‑principle guide to coding with Claude LLM. Learn how to think before you code, avoid over‑engineering, make surgical changes, and execute goal‑driven loops. The article explains each principle in depth, provides practical install instructions, and shows how to add the rules to your own projects for cleaner, more reliable code. Ideal for developers wanting to harness LLMs while keeping maintainability and clarity.
Dive into 'Best-of-ML-Python,' a meticulously ranked collection of over 900 awesome open-source machine learning Python libraries. Updated weekly, this list is an invaluable resource for developers, researchers, and data scientists looking for high-quality tools across various ML domains, including frameworks, data visualization, NLP, image processing, and more. Discover top-tier projects like TensorFlow, PyTorch, scikit-learn, and Hugging Face's Transformers, each evaluated by a unique project-quality score. Whether you're building, learning, or optimizing, this curated resource helps you pinpoint the most impactful libraries for your machine learning endeavors. Contributions are also welcome to keep the list current and comprehensive.
Explore a remarkable GitHub repository housing a comprehensive collection of 'leaked' system prompts from various large language model (LLM) services, including OpenAI, Anthropic, Google, and more. This open-source project offers a unique opportunity to understand the underlying instructions that guide leading AI models, providing insights into their operational methodologies and potential biases. Discover how these prompts shape AI behavior and contribute to the broader conversation around AI transparency and development. Perfect for developers, researchers, and AI enthusiasts.
Dive into the AI Engineering Hub, a comprehensive GitHub repository offering in-depth tutorials and real-world applications for Large Language Models (LLMs), Retrieval Augmented Generation (RAGs), and AI agents. Whether you're a beginner or an experienced practitioner, this hub provides invaluable resources to implement, adapt, and scale AI projects. Explore practical examples, contribute to a thriving community, and stay ahead in the rapidly advancing field of AI engineering. From multimodal RAGs to agentic workflows, discover code and insights to enhance your AI development skills.
Dive into the definitive open-source Prompt Engineering Guide by DAIR.AI, offering a wealth of resources from introductory concepts to advanced techniques for optimizing large language models (LLMs). This guide provides papers, lectures, notebooks, and practical examples for anyone from researchers to developers looking to deeply understand and effectively utilize LLMs. Discover methods like Chain-of-Thought, RAG, and more to enhance your AI applications. Join millions of learners and elevate your LLM proficiency with this continuously updated, community-driven resource.