Books

Is it safe to ditch the GUI and IDE? Or maybe you could get an instant optimization to your workflow right now, with great potential to pay off down the road as you utilize new features. Learn to navigate the world to the terminal using the tool depended on daily by thousands of system administrators and programmers.

Build real-world software by coding a Redis server from scratch.Network programming. The next level of programming is programming for multiple machines. Think HTTP servers, RPCs, databases, distributed systems.Data structures. Redis is the best example of applying data structures to real-world problems. Why stop at theoretical, textbook-level knowledge when you can learn from production software?Low-level C. C was, is, and will be widely used for systems programming and infrastructure software. It’s a gateway to many low-level projects.From scratch. A quote from Richard Feynman: “What I cannot create, I do not understand”. You should test your learning with real-world projects!

Core ML is pretty easy to use — except when it doesn’t do what you want. The Core ML Survival Guide is packed with tips and tricks for solving the most common Core ML problems. Updated for iOS 14 and macOS 11.

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.

What if you could communicate about the work your team does, as fast as the work itself happens? What if you could make busy people pay attention to the progress your team is making, without overloading them with too much detail? What if organisations could communicate more like humans do? This book has the answers.

You've set up your agent and taught it your name. With this book you teach it your patterns, your triggers, and the version of yourself you're working towards. 24 chapters. All prompts, no code.

Master machine learning interpretability with this comprehensive guide to SHAP – your tool to communicating model insights and building trust in all your machine learning applications.

The missing manual for making your web applications future-proof

A companion book for implementing Microsoft Dynamics 365 Business Central. Targeted end-users, super-users and administrators, this book covers many of the challenges you're faced when implementing a cloud-based ERP system. From setting up the system and creating the first company to user customizations and integration. Updated to 2025 Wave 2 v27.

Master Domain-Driven Design Tactical patterns: Entities, Value Objects, Services, Domain Events, Aggregates, Factories, Repositories and Application Services; with real examples in PHP. Explore the advantages of Hexagonal Architecture and understand Strategic design with Bounded Contexts and their integration through REST and message queues.

In this book, you'll uncover strategies that industry professionals use to build scalable, performant, and maintainable React applications, all without becoming overwhelmed by complexity.

Learn to build a production-grade web application with Spring Boot and Thymeleaf with this book.

The Ultimate Guide from Beginner to Pro: 300+ Examples, Practical Exercises, and Best Practices for Mastering Advanced TypeScript

Od Hasanaginice, preko Kuduza, do Zaplakale stare majke – autor je povezao sve kroz prizmu savremenih problema koji se vuku stoljećima. Pogodio je u samo srce problema: neprerađeni modeli odnosa, pogrešno razumijevanje muževnosti i ženstvenosti, transgeneracijske traume, kao i sistemska šutnja.

Python is a rich and powerful language, but many data scientists merely scratch the surface, and often feel uncertain about what lies beneath.  This book will go deep into the heart of Python, to truly understand its components, and how we can stitch them together to build better scientific workflows and machine learning systems.