Software Engineering

Go beyond a plausible patch: trace its impact, choose meaningful tests, and make the merge decision reviewable.

Turn useful results into lessons your AI workflow can test, retain and apply again.

Graphs are behind many of the problems software engineers face every day, from finding the shortest route to understanding complex networks. Practical Graph Theory shows you how to turn these problems into working solutions, with clear explanations, Python implementations and real-world examples that help you choose the right algorithm and understand why it works.

Building AI-powered software takes more than plugging into an LLM. This book explores the engineering principles and architectural patterns behind reliable, secure and scalable AI systems. Learn how to move from prototypes to production with practical techniques that stand the test of time, even as models and frameworks evolve.

Kod generowany przez AI kompiluje się, przechodzi testy i wygląda na gotowy. Błędy, które się liczą, kryją się w tym, czego brakuje. Praktyczna metoda przeglądu przed wdrożeniem.

Código gerado por IA compila, passa nos testes e parece pronto. Os defeitos que importam estão no que falta. Um método prático para revisar esse código antes do deploy.

Master practical system design with 62 parts covering distributed systems, scalability, databases, security, and production architecture. A comprehensive engineering handbook with real-world examples, diagrams, and decision frameworks.

Persistent memory, hooks, permissions and token cost: what worked when using Claude Code every day in a real small-business operation.

Stop relying on abstract marketing buzzwords. Master the real technical architecture, decision trees, and production-grade ABAP Cloud patterns needed to build upgrade-stable Clean Core extensions on SAP S/4HANA and SAP BTP.

Ready to move beyond AI prototypes? Learn how to build agents that reason, plan, use tools and tackle real-world tasks. From core concepts to production-ready architectures, this practical guide covers the code, design decisions and engineering challenges you need to build reliable, secure and scalable agentic systems.

Agents that plan, act and recover: the harness around the model. Tools, plans, checks, state, approvals and memory, drawn and built in Python.

Ready to build GIS applications that go beyond basic maps? This practical guide takes you from core spatial concepts to production-ready geospatial systems, with hands-on projects covering web mapping, spatial databases, real-time tracking and enterprise analytics. Learn the tools, techniques and engineering practices needed to turn geospatial ideas into reliable applications.

Vague prompts produce vague results. This book shows developers how to write clear, testable prompts for real coding tasks—explanations, functions, debugging, tests, and more—using a simple, repeatable method: state the goal, give the right context, set constraints, define the output, and verify the result.Written for beginners and working developers who want better AI-assisted work without treating model output as automatically correct.

A practical 30-day plan that turns complete beginners into confident, responsible users of AI coding assistants.Learn how to set clear goals, build a small project, debug with purpose, use Git, write better code, and present your work — all while using AI as a learning partner, not a crutch.

AI can speed up learning and coding — but only if you stay in control. This book shows new developers how to use GPT-6, Claude, and Cursor effectively: how to ask clear questions, supply useful context, review generated code, test edge cases, and avoid common mistakes such as invented APIs, insecure suggestions, and overconfident explanations. You will build practical habits for explanations, small features, tests, debugging, and documentation — while protecting private data and remaining responsible for the final result. Ideal for students, self-learners, and early-career developers who want to learn with AI, not instead of learning.