Books

Most ENCOR guides tell you what a protocol does. This one shows you the whole configuration, the output that proves it works, and the exact line that tells you when it doesn't.

An open, technical and introductory book for the Zig programming language, which is a new general-purpose and low-level programming language.

Ihre Use Cases haben einen neuen Leser: einen KI-Agenten, der sie direkt in Code und Tests umwandelt. Die Implementierung ist mittlerweile kostengünstig, doch die präzise Formulierung der Absicht stellt den Engpass dar. Dieser Leitfaden vermittelt Ihnen, wie Sie Use Cases verfassen, die sowohl für Stakeholder und Ingenieure als auch für die KI-Agenten, die sie umsetzen, funktionieren.

Unlock the full power of Ableton Live with Max for Live.This comprehensive guide teaches you how to create your own instruments, effects, generators, and interfaces, while diving into the rich Max environment. Learn to take full control of Live through the Live Object Model (LOM), and build your Live MIDI Tools to design your own creative workflow. A unique springboard to push your creative boundaries and transform Live into a fully customized sonic laboratory.(Version française disponible)

Code Is the Side Effect"Software engineers are not primarily code writers. We are clarity traders — and that hasn't changed."You've seen the demos. The AI builds a whole feature from a sentence. The agent writes tests, fixes the failing ones, opens the PR. It's remarkable.Then you come back three months later. The codebase is a tangle. Nobody knows why anything is the way it is. The agent that built it has no memory of what it decided or why. And every time you ask it to add something new, it breaks two things you didn't know were connected.This is the pattern that nobody talks about. AI coding tools make the easy parts of engineering dramatically easier. They leave the hard parts untouched — and they create new hard parts that didn't exist before.Ways of Working is the book for engineers who want to work with AI agents rather than be gradually replaced by them — who understand that the tools are genuinely powerful and genuinely limited, and want to build practices that get the most from each.What you will actually learnThe world model framework. Before an agent can build anything well, it needs to understand what it's building and why. This book teaches you to give agents what they need: a structured, queryable representation of your architecture, your component contracts, your behavior specifications, and your code patterns. No world model = no sustained agentic development.Intent documentation. The most expensive bug in agentic codebases is not a hallucination — it's a decision made without context. Why is this rule here? Why is this boundary where it is? Agents can't infer rationale from code. You have to write it down.Spec-Kit and formal specifications. GitHub's Spec-Kit brings machine-readable, traceable, CI-verified specifications to engineering teams. This book shows how to use it to turn requirements into agent inputs that are precise enough to generate correct implementations.Graph explainers. Tools like Graphify and Understand-Anything transform codebases and documents into queryable knowledge graphs — giving agents navigable context instead of flat text. This is the memory substrate that makes multi-agent systems reliable at scale.Agent architecture that holds. What makes an agent coherently itself? When do file-based agent systems break down and what replaces them? How does constraint-based coordination (borrowed from holocracy) solve the autonomy-coherence problem that has stumped AI researchers for decades?Claude Code, for real. A complete treatment of Claude Code's CLAUDE.md convention, permission model, hooks, and slash commands. Plus the oh-my-claudecode ecosystem: 15+ specialized agents, workflow orchestration patterns (autopilot, ralph, ultrawork), and the skills framework for team-specific automation.The AI-native organization. What genuine AI-native teams look like beneath the marketing. How to hire, structure, and lead them. What language-oriented programming and constrained natural language mean for the future of the human-code relationship.Who it's forEngineers who are past the "should I use AI?" question and into the "how do I use it without losing my engineering integrity?" question.Senior engineers. Engineering managers. Technical leaders. People who have noticed that the more they delegate to AI, the less certain they feel — and who want to understand why.From the AuthorI've been building production systems with AI agents for years. Not demos — systems that had to work reliably across months, maintain themselves as requirements changed, and produce outputs that engineers could understand and defend.That experience has made me skeptical in both directions.Skeptical of the "AI will do everything" vision — because I've watched too many AI-generated codebases collapse under the weight of accumulated misunderstanding.Equally skeptical of the "nothing fundamentally changed" position — because the engineers who treat AI coding tools as just faster autocomplete are making a category error they'll pay for in months of maintenance debt.Something genuinely new is happening. This book is my attempt to think about it clearly.

Your applications lives and runs in more places - MCP, desktop applications, browser-based application, data integration pipelines, gRPC, browsers, etc. - than ever. Can your security framework keep up? If you're using Spring Security, then it definitely can, and this book - by Spring Developer Advocate Josh Long - will show you how.

You don’t need massive compute or big-tech resources to build real AI. This hands-on guide shows you how to build and fine-tune your own small language model—from scratch—using accessible tools like Google Colab. Learn transformers step-by-step, train and align models on your own data, and deploy practical AI systems that run on consumer GPUs. If you’ve ever wondered “Can I build my own model?”—this book proves the answer is yes.

This book provides an introduction to the high-level concepts behind query engines and walks through all aspects of building a fully working SQL query engine in Kotlin.

This textbook accompanies a year-long Discrete Mathematics course for Computer Science and AI students, covering classical topics such as combinatorics, graph theory, probability, logic, and set theory. It emphasizes applications across computer science and complements the standard curriculum with advanced topics in each chapter.

Actionable Agile Metrics for Predictability is a comprehensive guide on how to use flow metrics and analytics to get the predictability your customers crave.

The deepest tutorial and explanation about EventStorming, straight from the inventor.

"Work in Progress" - Updated with the latest syntax, new CPAN modules and new file formats

Build a complete LLM inference engine in C++ — from a blankproject to a working Transformer that loads a real model andgenerates text. Forged one challenge at a time, with teststhat prove every piece works before you move on.

Skip the black-box frameworks. Build a production-grade AI coding agent from scratch in pure Python - cloud or local, tested with pytest, all in a single file.

A how-to field guide on building leak-free abstractions and algebraically designing real-world applications.