Books

"If you intend to use machine learning to solve business problems at scale, I'm delighted you got your hands on this book." —Cassie Kozyrkov, Chief Decision Scientist at Google "Foundational work about the reality of building machine learning models in production." —Karolis Urbonas, Head of Machine Learning and Science at Amazon

Build reliable LLM applications from first prompt to production agent. AI Engineering Visualized uses memorable doodles, practical Python, RAG, evals, tool calling and safety patterns to help you ship AI systems that work on Monday.

Stop copying code snippets into chat widgets. Master closed-loop autonomous AI coding agents, local offline LLMs (Ollama/Llama 3.1), event-driven lifecycle hooks, and Model Context Protocol (MCP) runtimes.

Bestselling book on building LLMs. A practical, project-driven manual for engineers who want to understand how modern language models are built — and where they fail — by writing every layer themselves. From a scalar autograd engine to RLHF to fused specialists, in 36 hands-on projects with deliberate sabotage experiments. Build it. Break it. Measure it.

Get more out of Claude Code with practical tips, proven workflows and clear guidance you can put to work right away. Whether you're just getting started or already using it daily, this guide helps you write better code, work smarter and avoid common pitfalls while making the most of what Claude Code has to offer.

What happens when AI starts making the decisions behind your database queries? This practical guide explores how modern optimizers work, then shows how machine learning and reinforcement learning can improve them. Learn to build, test and deploy AI-driven optimization systems that deliver real performance gains.

Build Claude into a reliable specialist for your domain. The Claude Skills Handbook takes you from the first idea through architecture, implementation, testing, security, deployment and monitoring, with practical patterns and working examples throughout. Learn how to build Skills that scale, stay maintainable and work in the real world.

Building fast LLM inference is about far more than turning the right knobs. This book takes you inside vLLM and the systems behind it, showing how memory, scheduling, GPUs and distributed serving shape real-world performance. Learn how to measure what matters, find bottlenecks and build inference infrastructure that scales without wasting money.

MCP can transform the way you work with Claude Code. This practical guide takes you from the basics to production, showing you how to connect tools, build integrations, automate workflows and ship reliable, secure solutions you can use right away.

AI is changing how people discover information, products and businesses. The AEO Playbook shows you how to get found, understood, cited and recommended by AI. From practical tactics to technical implementation, it gives you the frameworks and tools to stay visible as search evolves.

Generative AI can write code fast, but fast does not always mean safe. This practical guide shows software teams how to verify, test, secure and govern AI-assisted code across the full development lifecycle. Packed with real-world examples and practical workflows, it offers a clear path to adopting AI without lowering the bar for software quality.

AI coding agents can move fast, but speed without clear intent creates expensive mistakes. This practical guide shows how Behavior-Driven Development turns specifications into a reliable contract between you and autonomous coding systems, helping agents build the right thing, catch problems earlier and produce software you can trust.

Build Model Context Protocol servers that go beyond demos. This hands-on guide walks you from the fundamentals to production deployment with practical examples in Python, TypeScript, Go, Rust and Java. Learn the patterns, tools and real-world practices needed to build secure, scalable MCP servers that are ready for enterprise AI.

Build powerful AI agents that run entirely on your own hardware with Ollama. Learn practical patterns for RAG, multi-agent systems, security and deployment through clear explanations and production-ready code you can use right away. From first setup to reliable real-world applications, this book helps you build with confidence.

Great software is built through great reviews. This book shows you how to spot hidden bugs, improve design, strengthen security, and review AI-generated code with confidence. Packed with practical examples and proven techniques, it helps you write better software in a world where humans and AI build code together.