Books

This book focuses on the enshittification of Windows 11 and what you can do to fight back by countering the bad behaviors in this platform through fixes, workarounds, and configuration changes.

The engineer's guide to RAG systems that survive a deploy.

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 35 hands-on projects with deliberate sabotage experiments. Build it. Break it. Measure it.

The demand for skilled data science practitioners in industry, academia, and government is rapidly growing. This book introduces concepts from probability, statistical inference, linear regression and machine learning and R programming skills. Throughout the book we demonstrate how these can help you tackle real-world data analysis challenges.

It's never been easier to build an AI agent — and never been harder to make one that actually works. This book takes you from language model foundations to production-ready multi-agent systems with the depth to predict failure before it happens, engineer graceful degradation over catastrophic failure, and take absolute architectural ownership. Get the paperback from amazon.

Residuality Theory is a new way to think about the design of software systems that explains why we experience design the way we do, why certain things seem to work only sporadically, and why certain architects get it right so often regardless of which tools they use. A new, scientific approach is defined that fuses Software Engineering, Complexity Science, and Philosophy to produce an entirely new way to think about how to design software. The result is a theoretical base that allows architecture to finally become its own discipline.

Pain-Free MBSE is a practical guide to applying SysML without the unnecessary pain. Too often, MBSE gets a bad reputation for being slow, rigid, or overly complex. This book changes that. Using the Lunar Lander as a running example, it exposes high-pain modeling practices and introduces simpler, more effective alternatives. You'll learn how to apply the Value-to-Pain Ratio (VPR) to streamline your process, improve collaboration, and build models that work in the real world. Whether you’re a systems engineer, software developer, or decision-maker, Pain-Free MBSE helps you model with confidence — and without the headaches.

A practical guide to designing scalable and maintainable frontends. Learn how to think beyond components, model data effectively, shape APIs around real access patterns, and apply modern techniques like pagination, caching, virtualization, and real-time updates. Perfect for engineers preparing for system design interviews or leveling up in complex frontend projects.

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

A clear, illustrated guide to large language models, covering key concepts and practical applications. Ideal for projects, interviews, or personal learning.

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.

Developer platforms promise to rewrite the laws of IT physics: they boost innovation through standards; they speed up development while assuring compliance; and they reduce cognitive load without restricting choice. Building such an in-house platform, or even deploying one, is far from easy, though. This book condenses a decade of building and rolling out platforms into practical advice for platform teams and users.5300+ happy readers! Also as Paperback edition

You’ll just need the Five Essential Elements of Learning Through five essential elements, you’ll gain the ability to learn anything deeply—no matter how complex the subject. These five pillars of effective learning will become lifelong tools, guiding you every time you set out to truly master new knowledge and retain it permanently. If you're ready to stop passively consuming information and start truly mastering it, this book is for you.

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

This 2025 edition of Docker Deep Dive is a true masterpiece. The best-selling Docker book is up-to-date with the hottest Docker technologies and the latest industry trends. This edition includes a brand-new chapter on Docker Model Runner for fast, private LLM-based apps. You won't find a better and more up-to-date book on Docker. Hand-crafted and kept up to date over the past 9 years by best-selling author Nigel Poulton.