Books

AI can sound certain even when it is completely wrong. This book takes a practical approach to building RAG systems and AI agents that ground answers in evidence, verify what they generate and know when to stop. Learn how to make AI more reliable in the places where getting it wrong really matters.

Build AI agents that reason when necessary, preserve what they learn, and stop paying the intelligence premium for work they already know how to perform. Smart AI Agents combines architecture, working implementations, public source code, and measured experiments to show how agents can learn once and execute many.

Los agentes de programación con IA pueden avanzar más rápido de lo que permite el flujo de trabajo de Git basado en staging y commits.Juju-chu! muestra cómo Jujutsu🐦‍⬛ te da commits automáticos, una forma confiable de deshacer operaciones y una manera más segura de reorganizar los cambios desordenados que genera la IA, todo con compatibilidad total con Git y GitHub. Para desarrolladores que ya usan Git y quieren un flujo de trabajo con IA más tranquilo.

AI agents are moving from experiments into real production systems. This book shows experienced engineers how to design, deploy and operate reliable agent systems that can scale. From architecture and infrastructure to security, reliability and governance, it provides practical patterns and production-ready examples for building autonomous systems that work in the real world.

Static code analysis is more than running a linter and fixing warnings. This book shows how to build practical analysis pipelines with Claude Code and deterministic tools, combining AI-driven insights with reliable checks to improve code quality and security across projects of any size.

Unlock the full potential of Claude Fable 5.1 with practical prompting techniques built for real-world work. Learn how to get better results from coding, research and complex workflows, avoid common mistakes and build reliable AI systems with proven strategies and ready-to-use prompts.

What if your browser could run AI without sending your data to a server? AI in the Browser shows you how to build fast, private and fully local AI applications using WebGPU, WebAssembly and on-device LLMs. From GPU compute to a complete local chat interface, you’ll build everything yourself with practical code.

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.

What if AI could help you find flaws in software logic before they become costly bugs? This practical guide shows you how to use Claude Code alongside proven verification tools to test assumptions, uncover hidden issues and build confidence in your code. From first principles to production workflows, learn a smarter way to verify software with AI.

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.

My pitch was that rules written in CLAUDE.md get skipped, so you need enforcement. Then I measured it. In 22 trials with the ban written in, the banned command was never attempted. In 17 without it, 17 out of 17. This is the whole record: the instrument, every trial, the limits, and the harness so you can check me.

The second half of a from-scratch AI/ML course that refuses to skip the hard parts: LSTMs, real attention, a working transformer, LoRA fine-tuning derived and measured, and a genuine multi-tool, multi-agent system — every mechanism built by hand, then checked against PyTorch and real numbers.

Build a real, production-grade design system on the web platform — Prism DS, from a single custom element to an AI-agent-ready component library — with every example tested in a real browser before it made it into the book.

Building an LLM demo is easy. Running one reliably at scale is not. Production LLMOps shows you how to design, deploy and operate real-world LLM systems, covering RAG, agents, fine-tuning, evaluation, CI/CD, observability, security and more, with practical code you can adapt for production.

A from-scratch AI/ML course that treats you like an engineer, not a tourist — three modules covering language-as-numbers, the math foundations, and neural networks, each concept built in raw Python first, then PyTorch, so you always know what's really happening under the hood.