Books

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.

"Wired Protocols in Embedded Systems" is your essential guide to understanding how wired communication works in embedded systems. From UART and SPI to I²C and CAN, this book explains the core principles, advantages, and limitations of each protocol. Whether you're an embedded engineer, firmware developer, or Arduino enthusiast, you'll gain the knowledge needed to choose and implement the right protocol for your project.

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.

You don't need a graph database. You need graph thinking inside DuckDB. GraphDuck takes you from SQL adjacency lists to metagraphs, hypergraphs, and hybrid Graph RAG pipelines — all inside DuckDB. Learn to model knowledge graphs, build AI agent memory systems, run graph algorithms, and combine vector search with graph traversal in a single embedded database. Every concept comes with runnable code. No infrastructure required.

SYSMOD is an MBSE toolbox for pragmatic modeling of systems. It is well-suited to be used with SysML. The book provides a set of methods with roles and outputs. Concrete guidances and examples show how to apply the methods with SysML.

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.

C++11 is the first C++ standard that deals with concurrency. The story goes on with C++17, C++20, and will continue with C++23. I'll give you a detailed insight into the current and the upcoming concurrency in C++. This insight includes the theory and a lot of practice.

This book flattens Rust's incredibly steep learning curve. Each chapter introduces enough of Rust's syntax, data structures, and functions to incrementally build a fully functional, real-world Unix command-line interface (CLI) program, without overwhelming you with advanced language concepts such as lifetimes. Every new Rust programmer should read this book first to build the intuition and experience they'll need to become a confident Rust developer.

Data analysis is now part of practically every research project in the life sciences. In this book we use data and computer code to teach the necessary statistical concepts and programming skills to become a data analyst. Instead of showing theory first and then applying it to toy examples, we start with actual applications and describe the theory as it becomes necessary to solve specific challenges. The book includes links to computer code that readers can use to follow along as they program.

Usar IA para programar es fácil. Usarla sin perder el control, no tanto. Spec-Driven Development es el método para convertir tu idea en una spec que la IA ejecuta con precisión — sin loops infinitos, sin código roto, sin empezar de cero.

This book aims to be the comprehensive manual for type-level programming. It's about getting you from here to there---from a competent Haskell programmer to one who convinces the compiler to do their work for them.

Far from the madding crowd: vibecoding agents, big companies, and promises of a perfect programming language (that never arrives)... your thoughts spring from your fingers and, through the keyboard, code takes shape.If you enjoy programming, you feel it tickle your brain cells. At times, you notice that something just "clicks." It's real - the satisfaction of learning. Not to get a job. Not to make apps in 5 minutes. Not to get rich reusing prompts. But for the pleasure of understanding, of digging deeper, and of building elegantly.Zig is a young language, still evolving - and that doesn't really matter. All languages change sooner or later. Everything becomes obsolete eventually, except your mind and what you truly learn along the way. That journey begins today with the first step, and it will go as far as your time, your mind, and your imagination take you. That's the spirit of Zen of Zig. $ zig zen  * Communicate intent precisely. * Edge cases matter. * Favor reading code over writing code. * Only one obvious way to do things. * Runtime crashes are better than bugs. * Compile errors are better than runtime crashes. * Incremental improvements. * Avoid local maximums. * Reduce the amount one must remember. * Focus on code rather than style. * Resource allocation may fail; resource deallocation must succeed. * Memory is a resource. * Together we serve the users.

The book highlights the significance of software in systems engineering and uses AI as a subject matter expert. It presents a comprehensive example that covers SysML modeling, including requirements, use cases, logical/ physical architecture, and parametric simulation. It then continues into software, leveraging AI's code generation capabilities to produce software including microcontroller, UI, and DMBS code. It introduces a variety of personas and agents that can help engineers communicate with AI about systems and software engineering. The book also introduces SysML v2, focusing on the new language model and exploring AI's ability to generate models via code generation. Perhaps most importantly, it provides a straightforward roadmap for hardware/software co-design, accelerated at every step by AI. Whether you're a systems or software engineer, or just interested in how to use AI for engineering, AI Assisted MBSE with SysML will prove to be a valuable guide.

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 understand what you're building and why it fails.

Everything you really need to know in Machine Learning in a hundred pages.