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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 #15595 · 04/01/2026, 11:30 AM

#go#distribution_spec#helm#kubernetes#oci#oci_distribution#opencontainers#zot Zot is a lightweight, production-ready OCI-native container registry for storing images, Helm charts, SBOMs, and other artifacts without vendor lock-in. It offers built-in authentication (OIDC, LDAP), storage options (S3, Azure), scanning, caching to cut Docker Hub limits/latency, and ARM/edge support as a single binary. You benefit by easily self-hosting a secure, scalable alternative to Docker Hub, saving costs, boosting speed, and enabling secret-less workflows on any device. https://github.com/project-zot/zot