Books

What makes a software engineer truly effective? Beyond writing code, effectiveness comes from ethics, sustainable practices, collaboration, and the courage to adapt. The Effective Software Engineer guides you from clean coding habits to organizational empowerment, bridging the gap between developers and leaders.

The definitive guide to agentic software engineering with Codex CLI, from prompting and AGENTS.md fundamentals to multi-agent orchestration, CI/CD integration, security hardening, and enterprise deployment across 28 hands-on chapters.

This book is the ideal study guide for the official TCCI certification by the TYPO3 Association. The revised and updated 7th edition for TYPO3 v13 LTS contains more than 350 example questions and detailed explanations of which answers are correct and why. Simply the perfect choice for every integrator to prepare for the test and master the exam! 🏆

“Happily purchased. Handy to have these in one place. Thank you!” — Kent Beck “Excellent new thinking on Domain-Driven Design. It's full of real practical experience in getting the most value from domain modelling. Just like the Eric Evans' DDD book, this gives more insight each time you read it.” — Ian Russell

Prepare for senior Go interviews, or for the jump from mid-level to senior, with a focus on AI platform engineering: LLM gateways, RAG, vector search, Kubernetes, observability, concurrency ownership, and production failure modes. Includes interview questions, senior-level answer rubrics, executable Go examples, and a production-oriented RAG service capstone you can run, test, break, and explain.

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.

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

The book is also available in paperback for $25. Paperback royalties go to OpenIntro (US-based nonprofit), and the optional Leanpub PDF contributions go to authors to fund their time on this book.

Build real-world software by coding a Redis server from scratch.Network programming. The next level of programming is programming for multiple machines. Think HTTP servers, RPCs, databases, distributed systems.Data structures. Redis is the best example of applying data structures to real-world problems. Why stop at theoretical, textbook-level knowledge when you can learn from production software?Low-level C. C was, is, and will be widely used for systems programming and infrastructure software. It’s a gateway to many low-level projects.From scratch. A quote from Richard Feynman: “What I cannot create, I do not understand”. You should test your learning with real-world projects!

After 1,000+ interviews, I kept seeing the same gaps: engineers who use shared_ptr daily but can't explain the control block; developers who write multithreaded code without understanding the memory model they're relying on. This book is the resource I wished existed. Free preview includes Chapter 3: OOP & Polymorphism — the chapter that eliminates more candidates than any other.

AI can generate code faster than ever. But speed is no longer the hardest part of software development.The real challenge is building systems where generated code remains correct, controlled, and aligned with architectural intent. Becoming a Harness-Driven Developer introduces a new development model for the age of AI-assisted engineering - one where developers focus less on writing every line by hand and more on defining the rules, structure, and enforcement mechanisms within which AI code agents operate.Through a practical repository mapped directly to the book’s chapters, this book shows how to move from ad hoc coding to a harness-driven approach based on specification, architecture, invariants, controlled execution, and system evolution. This is a book about staying in control while software development changes.

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

Most delivery problems aren't caused by the code, the team, or the work management process. They start upstream: with how requirements mature (or don't). Ready introduces Requirements Maturation Flow (RMF), a system for gating, clarifying, and aligning work — before it starts and without changing your framework. If your Sprints end in carryover, rework, or delivering the wrong thing, RMF is the structural fix you've been missing.

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.

Learn databases from the bottom up by coding your own, in small steps, and with simple Go code (language agnostic).Atomicity & durability. A DB is more than files!Persist data with fsync.Crash recovery.KV store based on B-tree.Disk-based data structures.Space management with a free list.Relational DB on top of KV.Learn how tables and indexes are related to B-trees.SQL-like query language; parser & interpreter.Concurrent transactions with copy-on-write data structures.