Software Engineering

Concurrency is where small mistakes become serious problems. This practical guide takes you from hardware and memory models to locks, atomics and lock-free algorithms, with a focus on building fast, reliable systems that hold up under real production workloads.

​A system-first engineering guide to building, deploying, operating, and securing production-grade Kubernetes clusters, cloud-native platforms, and AI inference workloads.

Rapid-development frameworks across Ruby on Rails, Node.js, PHP, Python, and .NET revolutionized how quickly we can ship software. But what happens when your business domain grows complex? Relying on Active Record and heavy ORM patterns tightly couples your database to your business logic. As your product matures, this coupling creates brittle, difficult-to-maintain codebases that resist change and slow down your team. The solution is the Domain Model Pattern (the foundation of Domain-Driven Design). It isolates business rules from infrastructure, creating a pure, scalable architecture. Historically, however, this pattern required a massive amount of effort and boilerplate, making it too slow for fast-paced development. That changes today. We are at an AI inflection point. Modern AI coding assistants excel at generating boilerplate and wiring infrastructure, effectively eliminating the traditional drawbacks of DDD. AI finally bridges the gap between the rapid speed of framework scaffolding and the architectural purity of a Domain Model. But to harness AI reliably, you cannot simply let it generate code unguided. You need a structured methodology to guide the machine. In this book, you will learn:How to build reliable, maintainable software using a strict, AI-assisted pipeline: Use Cases → Gherkin → Event Storming → Strategic & Tactical DDD.How to create implementation-agnostic class diagrams that serve as universal blueprints, allowing you to generate code in OOP languages (Java, C#, PHP, Python) or Functional languages (Elixir, Clojure, F#).How to future-proof your systems with a timeless methodology that prioritizes the longevity of business logic over the ephemeral nature of web frameworks.How to scale your architecture gracefully from a Majestic Monolith to Microservices without a complete rewrite. The methodology is language agnostics but in this edition all codes samples are in TypeScript. Whether you want to accelerate your development velocity, improve your system's resilience, or simply reclaim your craft by building beautiful, majestic systems, this book provides the blueprint.Discover how to combine the timeless principles of DDD with the power of AI.

It is three in the morning and something is wrong with your LLM application. This is the book you open. Not a tutorial — a reference manual, organised by symptom, for the moment something you built has already broken.

React Native got you far. But when performance, maintainability and long-term control start to matter, native development deserves another look. This book explores Swift, SwiftUI, Kotlin and Jetpack Compose, helping you understand when going native makes sense and how to make the move without losing what already works.

Build real-time data systems that can keep up with the demands of production. Streaming at Scale takes you under the hood of Apache Flink, covering state, event time, exactly-once processing, performance, Kubernetes and more. With runnable examples and practical lessons, it shows how to build systems that are fast, reliable and ready to scale.

Forget IShape — it's time to build software that actually does something. OrderFlow takes ten classic Gang of Four design patterns and weaves them into a single, fully functional food delivery platform built with C# and .NET 10. Clone the repository, step through the production-grade code, and finally master not just how to write patterns, but when to use them (and when to keep it simple).

AI can sound certain even when it is completely wrong. This book takes a practical approach to building RAG systems and AI agents that ground answers in evidence, verify what they generate and know when to stop. Learn how to make AI more reliable in the places where getting it wrong really matters.

Security is no longer something you add after a system is built. It must be designed from the beginning.Security by Design explores how modern digital systems can be built to reduce risk, limit unauthorized access, protect sensitive data, detect threats, contain failures, and recover from attacks.From secure architecture and cybersecurity foundations to privacy, cloud security, DevSecOps, AI security, and digital resilience, this book provides a practical foundation for building systems that remain secure in an increasingly connected and AI-powered world.

The Future of Software Engineering Has Arrived.AI can now write code, generate tests, analyze requirements, explain systems, assist with debugging, and automate increasingly complex engineering tasks.But building great software has never been just about writing code.How do you design the right architecture?How do you build systems that remain secure and reliable at scale?How do you integrate AI without sacrificing quality, privacy, or control?How do you use AI to make engineers more effective without replacing engineering judgment?Engineering Software Systems in the AI Era explores these questions and provides a practical framework for designing, building, testing, securing, deploying, operating, and evolving modern software systems.Inside, you will explore:Modern software architecture and system designCloud-native and scalable systemsData architecture and intelligent data systemsAI engineering and AI-native applicationsCybersecurity and privacy by designSoftware quality and reliabilityDevOps, DevSecOps, and continuous deliveryObservability and production engineeringAI-augmented software developmentAI agents and intelligent engineering workflowsPlatform engineeringTechnical debt and engineering strategyAI governance and risk managementTechnical leadership in the AI eraThe future of software engineeringThe central message is simple:AI may make software development faster. Engineering makes software better.Learn how to combine the power of artificial intelligence with the discipline of software engineering to build systems that are secure, scalable, reliable, intelligent, maintainable, and ready for the future.

Tu equipo no va lento porque escriba mal código: va lento porque escribe código de más. Una guía práctica para gestionar el coste basal del software y recuperar la capacidad del equipo, combinando Lean Software Development, Extreme Programming y mentalidad de producto.

Python is entering a new performance era. Explore free-threaded execution, Rust extensions and modern tools for building faster, more scalable Python systems. Learn how CPython is changing, when Rust makes sense and how to turn these ideas into production-ready software.

Git is much bigger than GitHub. This practical guide explores self-hosted platforms, decentralized approaches, secure operations and CI/CD beyond the usual defaults. Learn how to design, deploy and run independent Git infrastructure for real-world engineering, enterprise and regulated environments.

What if you could build software without manually writing every line of code? Artificial Intelligence is transforming the way software is designed, developed, tested, and deployed.But becoming an AI-powered software engineer isn't simply about asking an AI to write code.It's about learning how to think, design, build, test, verify, and improve software with AI as your engineering partner.The AI-Powered Software Engineer takes you on a practical journey from traditional programming to AI-assisted development and modern AI engineering.Learn how to use AI to:Design software. Generate and understand code. Debug complex problems. Build AI-powered applications. Automate workflows. Test and secure software. Deploy real products. Build AI-powered SaaS businesses.You don't need to become the fastest programmer in the room.You need to become the person who can turn an idea into a working, reliable, valuable system.Write less code. Think more. Build faster. Engineer smarter.The future of software engineering isn't humans versus AI.It's humans building with AI.

AI agents are moving from experiments into real production systems. This book shows experienced engineers how to design, deploy and operate reliable agent systems that can scale. From architecture and infrastructure to security, reliability and governance, it provides practical patterns and production-ready examples for building autonomous systems that work in the real world.