Software Engineering

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.

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.

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.

Static code analysis is more than running a linter and fixing warnings. This book shows how to build practical analysis pipelines with Claude Code and deterministic tools, combining AI-driven insights with reliable checks to improve code quality and security across projects of any size.

Good software is not about following rules. It is about knowing when they apply. This book explores the judgment behind building software that lasts, from choosing simplicity over cleverness to balancing today’s needs with tomorrow’s costs. Practical, thoughtful and focused on decisions, not dogma.

From the Linux basics to advanced development workflows, this practical guide shows you how to turn Linux into a powerful software development environment. Learn the command line, work with popular programming languages, use containers and CI/CD, debug and profile applications and build skills you can use every day.

What if AI could help you find flaws in software logic before they become costly bugs? This practical guide shows you how to use Claude Code alongside proven verification tools to test assumptions, uncover hidden issues and build confidence in your code. From first principles to production workflows, learn a smarter way to verify software with AI.

Abandona el ensayo y error en la IA generativa. Un marco práctico y metodológico basado en el falsacionismo de Karl Popper para auditar, estresar y dominar la interacción con modelos de lenguaje.

Build real machine learning applications with TensorFlow.js, from your first model to production-ready systems. Learn how to train and deploy models in the browser and Node.js, tackle real-world challenges and explore modern techniques with practical JavaScript and TypeScript examples you can run and adapt.