Books

Learn SysML v2 with the ultimate guide for all skill levels in MBSE. Authored by insiders, it's your key to unlocking the full potential of system modeling and a passport to mastering your MBSE.

Not just for mathematicians and detectives! Learn the basics of logic and its many applications, including advanced testing, constraint solving, function composition, and code proofs. Over 50 exercises and 20 sample programs are included. No prior math background required!

Чому проєкт-цукерочка через рік перетворюється на непідтримуване пекло? Тому що вас ніколи не вчили базі. Ця книга — єдина система координат, яка нарешті збере ваші розрізнені знання про ООП, GRASP, SOLID та GoF в одну цілісну картину.

The book covers every topic in the latest CISSP exam syllabus, 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.

With more than 1200 microcontrollers, STM32 is probably the most complete ARM Cortex-M platform on the market. This book aims to be the most complete guide around introducing the reader to this exciting MCU portfolio from ST Microelectronics and its official CubeHAL and STM32CubeIDE development environment.

A complete foundation for Statistics, also serving as a foundation for Data Science. Leanpub revenue supports OpenIntro (US-based nonprofit) so we can provide free desk copies to teachers interested in using OpenIntro Statistics in the classroom and expand the project to support free textbooks in other subjects. More resources: openintro.org.

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

A best-selling book. The practitioner's guide to Claude Code in production. Thirty-one chapters covering the agent loop, tools, hooks, MCP, the Claude Agent SDK, permissions, multi-agent orchestration, evals, observability, and cost engineering. Includes a full walkthrough of Anthropic's financial services reference agents. Code from real production systems, not toy examples.

Master language models through mathematics, illustrations, and code―and build your own from scratch!

Monday morning. You typed "add multi-tenancy to the billing service" into Claude Code. Eight hours later: forty-three files touched. Half the tests yellow. You cannot explain to your tech lead what was decided, by whom, or against which constraint.The code looks fine. The intent is gone.The Clarity Forge is the antidote — a small, opinionated pipeline that forces every fuzzy idea through explicit spec, structured interrogation, and tailored artifacts before a single line of production code is written.Six stages. Six copy-paste prompts. One iron rule:The spec is the durable artifact. The code is the side effect.Pairs the frontier-grade Architect (Opus, GPT-5, Sonnet 4.6+) with a local Contractor (Gemma, Qwen). Pairs OpenSpec's directory convention with the Grill skills that surface ambiguity before it metastasises into code.A weekend read. A Monday-morning toolkit. Worked example included.Stop vibe-coding. Start clarity-trading.

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.

AI is an amplifier. It magnifies whatever engineering discipline — or lack of it — already exists, which means the bottleneck was never AI's capability; it is the collaboration space you design around it. This book is a six-level progression from early AI panic to sovereign engineering: the discipline of designing the environment in which human and artificial intelligence produce work worth keeping. It moves past prompting into the practices that compound — verification, habitat engineering, specification-first development, and the platform discipline that scales the practice across teams. The habitat is yours to design. Let's build it well.

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.

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.

The definitive guide to agentic software engineering with Codex CLI, from prompting fundamentals to multi-agent orchestration, CI/CD integration, and enterprise deployment across 32 hands-on chapters.