Books

Software development is changing fast, and Claude Code is at the center of that shift. Learn how to work effectively with AI agents to write code, automate workflows, and build larger projects with confidence. From setup and prompt design to real-world engineering practices, this book provides a practical guide to modern software development in 2026.

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

This textbook is designed for use in introductory Software Engineering courses. Additionally, it can be used by junior developers intending to consolidate their knowledge in the field. It has also a web version available at https://softengbook.org.

Includes Editions 1, 2, 3, and 4. Editions 1-3 are listed under "Extras" after checking out, including for $0 purchases. AHSS provides a thorough intro to statistics and supports students preparing for the AP Statistics exam. B&W paperbacks (color) are sold for $25 ($40). Paperback royalties are shared between OpenIntro and the authors. Leanpub contributions go to authors to fund their time.

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.

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.

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

A Functional Programming book from beginner to advanced without skipping a single step along the way. In my 40 years of programming, I've felt that programming books always let me down, especially Functional Programming books. So, I wrote the book I wish I had 5 years ago. Functional Programming will never be easy, but it can be easier.

Architektur-Reviews ermöglichen Dir Schwächen und Potenziale von Softwarelösungen herauszuarbeiten, Entscheidungen abzusichern und Verbesserungsmaßnahmen zu bewerten. Dieses Buch bringt Dir leichtgewichtige Reviews näher, die Du nach der Lektüre alleine oder in einem kleinen Team direkt durchführen kannst!

AI for Data Visualisation shows how large language models (LLMs) can be used to explore, transform and visualise your data. It shows how to use simple prompts to analyse and transform data and to create charts, maps and simple dashboards.

A practical guide to fine-tuning Large Language Models (LLMs), offering both a high-level overview and detailed instructions on how to train these models for specific tasks.Get the paperback version here. Get the Kindle version here.

The book covers every topic in the latest CCSP exam syllabus, with more than 400 pages organized in a format that makes it easy to drill down on specific exam domains and concepts at-a-glance, making it an essential exam resource for anyone who aims to prepare for the exam without wasting time or money.

Your organization does all the right things. They practice chaos engineering, GameDays, and load testing. They conduct incident reviews and operational readiness reviews. Yet the same types of incidents keep recurring. This book examines why resilience practices so often fail to build resilience, revealing the organizational dynamics that systematically transform learning mechanisms into compliance theater and what you can do to navigate them consciously.

This book teaches you how to make machine learning models more interpretable.

Start with no experience and build the skills to write modern C++ with confidence. From your first program to advanced C++26 features and professional software design, this guide explains every step with clear examples and practical insights. Learn it once, keep it as a reference for years.