Agentic Engineering

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.

Build production-ready applications with TypeScript in the age of AI. Go beyond the basics to master type-safe development, modern full-stack architectures, LLMs, intelligent agents, tool calling and the Model Context Protocol. With practical, runnable examples throughout, this book shows you how to build, test, secure and deploy serious TypeScript AI systems.

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.

Installing safety hooks for Claude Code takes about thirty seconds. Proving they actually refuse anything is the step almost everyone skips — and an unverified guard looks exactly like a working one. Free: the pre-flight checklist, which event can refuse a tool call and which only watches, how to make a guard fire on purpose, what a broken hook does, and a catalogue of all 916 example hooks in cc-safe-setup.

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.

Build AI that keeps working when the cloud doesn't. Local-First AI Engineering shows you how to run capable, private AI systems on infrastructure you control. From hardware and inference to RAG, agents, security and production ops, you'll learn how to build local AI that is fast, reliable and truly yours.

Reverse engineering gets a powerful upgrade with Claude Code. Learn how to investigate, understand and reconstruct software you’re authorized to analyze, while keeping evidence, accuracy and reproducibility at the center. From legacy systems to modern codebases, this book turns AI into a practical partner for serious software analysis.

Move beyond AI demos and build agents that can actually run IT operations. This practical guide takes you from MCP fundamentals to production-ready monitoring, incident response, remediation, Kubernetes operations, security and multi-agent systems, with complete runnable code and a strong focus on safe autonomy.