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Source channel @githubtrending · Post #14926 · Jul 8

#jupyter_notebook#artificial_intelligence#book#large_language_models#llm#llms#oreilly#oreilly_books You can learn how to use Large Language Models (LLMs) effectively through the book *Hands-On Large Language Models* by Jay Alammar and Maarten Grootendorst. This book uses nearly 300 custom illustrations to explain key concepts and practical tools for working with LLMs, including tokenization, transformers, prompt engineering, fine-tuning, and advanced text generation. It also provides runnable code examples in Google Colab, making it easy to practice and apply what you learn. This resource helps you understand and build your own LLM applications confidently, saving you time and effort in mastering complex AI technology. It’s highly recommended for anyone wanting hands-on experience with LLMs. https://github.com/HandsOnLLM/Hands-On-Large-Language-Models

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@githubtrending · Post #15487 · 02/12/2026, 12:30 PM

#typescript#ai#augmentation#humans#productivity PAI is open-source Personal AI Infrastructure that builds on tools like Claude Code to create your custom AI assistant. It learns your goals, preferences, and history from files like MISSION.md and GOALS.md, uses modular packs for skills like research or security, and improves via feedback in an Observe-Think-Plan-Execute-Learn loop. Install easily with git clone and a wizard for a full system in minutes. This magnifies your abilities, activates your potential, saves time on repetitive tasks, and makes elite AI accessible to everyone—not just experts—helping you achieve more with less effort. https://github.com/danielmiessler/Personal_AI_Infrastructure