#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

#go #databases #genai #llms #mcp

The MCP Toolbox for Databases helps developers connect AI agents to databases more easily and securely. It simplifies the process by handling complex tasks like connection pooling and authentication, allowing you to integrate databases with AI agents using minimal code. This toolbox supports the Model Context Protocol (MCP), which standardizes how AI interacts with external tools. By using MCP Toolbox, you can automate database tasks, query databases using natural language, and generate context-aware code, all of which save time and improve development efficiency.

https://github.com/googleapis/genai-toolbox