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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 #14996 · 07/25/2025, 12:00 PM

#ocaml#c#go#java#javascript#python#r2c#ruby#sast#semgrep#static_analysis#static_code_analysis#typescript Semgrep is a fast, open-source tool that scans your code to find bugs and security issues in over 30 programming languages. It works locally on your computer or in your build system, so your code stays private. Semgrep’s rules are easy to write and understand, helping you catch problems early in development, whether in your IDE, pre-commit checks, or CI/CD pipelines. For stronger security, the Semgrep AppSec Platform offers advanced analysis, AI-powered triage, and detailed fix guidance, reducing false alarms and helping developers fix issues quickly without slowing down. This improves code quality and security efficiently. https://github.com/semgrep/semgrep