Software Engineering

AI writes code faster than you can review it. This book gives you the gates: automated guardrails that catch bugs, enforce standards, and verify behavior before bad code reaches production. Stop being the last line of defense. Build the gates that catch the slop first, so you design and verify instead of proofread.

This book is not just another introduction to Domain-Driven Design (DDD). It explores real-world design problems, challenges conventional approaches, introduces new ideas by providing practical guidelines on three levels of abstraction, which the author calls "Clean Backend Application", "Clean Aggregates" and "Clean Domain-Driven Code". The former is the overarching concept, whilst the other two are essential and obligatory building blocks. Together, they form a specialised, systematic approach to the professional software engineering of backend applications. The book is packed with detailed diagrams and code examples. Along the way, readers gain practical guidelines and design insights that help them confidently apply these concepts in their own software projects.

The chapter that grew into its own book: existential and universal restrictions, the correct VegetarianPizza pattern, and how OWL restrictions become EKA governance.

Most senior engineers don't fail AI systems interviews from lack of ability—they fail because they sound like builders when they need to sound like staff. This book gives you the answer flow, the system deep dives (RAG at billion-doc scale, agentic pipelines, token-cost tradeoffs), and the behavioral stories that earn L5/L6 offers at AI-first companies. Built for senior engineers targeting Staff AI Systems roles in the next 8 to 12 weeks.

Failure Driven Development Software Development Habits Haven't Caught Up to Cheap Compute. Design software the way electrical engineers design circuits — probes, test jigs, and a bowl of trusted parts — and field defects follow the same trajectory as hardware: toward zero.

You have been using AI as a faster keyboard.The engineers who will define the next decade are using it as a cognitive workforce they direct, constrain, and govern. The gap between those two practices is not a matter of better prompts. It is a matter of an entirely different mental model.This book is that mental model. Built from first principles. Illustrated through 28 chapters of real architectural decisions, real failures, and real production systems.From execution to orchestration. The complete practitioner guide.

Python Engineering Judgment is for engineers who already know Python and want to build the skill AI makes more valuable: the ability to reason about code, systems, tradeoffs, and failure modes. This deep, practical field guide teaches the Python object model, type system, decorators, generators, concurrency, asyncio, multiprocessing, API design, packaging, performance, debugging, security, observability, and reliability through the lens of real-world engineering judgment. Use it to prepare for senior, staff, principal, and tech-lead interviews, and to make better decisions in code reviews and production systems.

AI can write code faster than any human can review it. That changes the economics of engineering, but not what engineering is for. The bottleneck has moved from building to judging —and judgment cannot be prompt-engineered into a system designed for cheap proposals.

Stop being a 'Syntax Coder' and start becoming a 'Diagnostic Architect' by mastering the hidden mental models of software failure. This book teaches you how to own every logical branch of your system and eliminate bugs before they even happen. Stop guessing and start diagnosing.

A practical handbook for building deterministic, auditable AI pipelines with safety gates, capability profiles, audit trails, telemetry and production deployment patterns.

You don't need another "intro to LLMs" tutorial. You need patterns—proven architectural blueprints that tell you exactly when to use RAG vs. fine-tuning, how to build agents that don't spiral, and why your retrieval pipeline returns garbage. This book gives you 29 of them, with architecture diagrams and working Python code. Free. No fluff. No vendor lock-in.

Claude Code users routinely spend more on tokens than they expect, and cannot see where the money goes. Chapter 8 catalogues 48 diagnostic symptoms — what each one looks like, what it costs you, and what to change — with a fix where a fix exists, and an honest "unresolved" where none does yet. Chapter 7 puts before-and-after numbers against the changes, measured on the author's own unattended runs, and Chapter 9 collects the settings templates.

Learn how to build Kubernetes Operators from scratch using Kubebuilder, step by step. You will create real controllers, work with CRDs, and understand how reconciliation works in practice. By the end, you’ll know how to design, test, and run your custom operator.

跳过黑箱框架。用纯 Python 从零构建生产级 AI 编程智能体——云端或本地，用 pytest 测试，全部在一个文件中完成。

ブラックボックスのフレームワークは不要。純粋なPythonでプロダクションレベルのAIコーディングエージェントをゼロから構築。クラウドでもローカルでも、pytestでテスト済み、すべて1つのファイルに収まります。