Books

A practical front-end debugging guide to the tools and techniques required to debug, optimize and monitor your web applications. This handbook helps you gain the fundamental and deep knowledge of a comprehensive toolset, from Chrome DevTools and framework extensions to integrated IDE debuggers.

What could a mighty billion-parameter reasoning machine learn from a camel trying to touch its ear with its tongue? From a cup of coffee? From deleting your entire codebase while you sleep? More than you’d think. And less than you’d hope. Today’s AI is brilliant structure without grounding—a hollow genius. We chase smarter models but ignore the architecture they need. This book is about building that missing layer: the trust chains and systems that turn raw intelligence into reliable autonomy. For builders ready to move beyond prompts.Watch agents solve unsolvable problems. Learn to think in trust chains. Start here.

Phase 0 - Orientation Learning How to Think About DesignBefore we talk about systems, we need to talk about how you think.Most engineers approach design the way they approach code: gather requirements, choose tools, apply patterns, and move forward. This works—until it doesn’t. At some point, the systems you build stop behaving like the code you wrote. Problems emerge that cannot be traced to a single function, service, or decision. When that happens, adding more knowledge does not help. Changing how you reason does.This phase exists to interrupt familiar instincts.Phase 0 is not about teaching you what to design. It is about exposing the assumptions you already carry—about correctness, scale, failure, and simplicity—and showing where they quietly break down. These chapters will feel less concrete than the ones that follow. That is intentional. You cannot reason well about systems until you are comfortable with uncertainty, trade-offs, and incomplete explanations.If you rush through this phase looking for answers, it will feel unsatisfying. If you sit with it, it will change how the rest of the book lands. The goal of Phase 0 is simple: by the time you reach Phase 1, you should no longer be asking, “What framework should I use?” You should be asking, “What kind of problem is this, really?”Chapter 1: What Is System Design (and What It Is Not) A System That Worked YesterdayThe system worked yesterday.The code hadn’t changed. Tests were green. No alerts fired overnight.And yet this morning, requests were timing out.Someone suggested adding more instances. Someone else blamed the database. A third person said, “It works on my machine.”All reasonable guesses. All wrong.By noon, a temporary fix was deployed. By evening, the incident was “resolved.” By next week, it happened again—somewhere else.Nothing was broken. And that was the problem. The Question Nobody AskedAfter incidents like this, teams usually ask familiar questions:Which service caused it?Was it a bug or a configuration issue?Do we need better monitoring?These are safe questions. They have owners. They lead to tickets. The dangerous question is the one nobody asks:What assumption did we design this system around—and when did it stop being true?That question does not map to a single component. It does not have a Jira ticket. It cannot be fixed with a patch.And yet, most system failures quietly begin there. When Code Stops Explaining BehaviorAs a developer, you are trained to reason locally.Input goes in. Logic executes. Output comes out.When something breaks, you look for the line of code responsible.Systems do not behave this way.In systems:Failures emerge from interactionsLatency appears without slow functionsLoad exposes assumptions you forgot you madeEvery component can behave “correctly” while the system misbehaves.This is not an edge case. This is the default state of non-trivial systems. The Moment Code Becomes a SystemThere is a moment—often unnoticed—when local reasoning stops working.The moment when:Understanding one service is no longer enoughFixing one issue creates another elsewhereBehaviour depends on timing, not logicConfidence drops even though competence hasn’tNothing announces this transition. No role change marks it. No diagram captures it cleanly.But once it happens, the rules change.System design begins after this moment. Most teams do not notice this moment. They only feel its consequences

You already know how to work hard. The question is whether you know when to stop.Most frameworks optimize effort. The Ishi Spiral governs it. It is a discipline of self-governance that sustains performance under pressure without drifting into burnout or grinding past the point where continuation is still justified. The Guide is the Companion to 'Ishi: the Discipline of Determined Intent'.

Want to write a technical book? Approach it the same way you build software!

Stop treating PostgreSQL like slightly broken MySQL and start using it the way it was designed.

A practical guide to product engineering in an AI native era, where building shifts from manual construction to steering tools, editors, and agents. Product Engineering with AI covers platforms, agentic workflows, prompting, code quality, UX, and responsible practices for getting from prototype to production. 

Here is the English translation:Learn to build discrete-event simulation models and agent-based simulation models using SimPy, a Python library.

Low level is a term that confuses people. People think something high level is better than low level. In simple terms, humans consider themselves superior to machines and therefore think themselves higher or more important because of their abstract though. A computer thinks only in terms of numbers. A computer may not understand "high level" abstractions such as love, religion, philosophy, etc, but that is not its job. A computer must add, subtract, multiply, and divide. These are the four arithmetic functions which many human struggle to do. I wrote this book because I think like a machine and I hope to help others think this way because it is the best way to learn programming and control your computer by writing Assembly Language programs or to go back to your favorite programming language with a greater understanding of why things work as they do.

Most companies are stuck in "pilot purgatory"—endless AI experiments that never scale. Teams spend weeks sharing clever prompts and one-off scripts, but nothing makes it into production. Budgets evaporate, engineers' enthusiasm curdles into cynicism, and executives start asking "we spent how much on this?" The problem isn't the AI tools—it's the approach. AI models are inherently non-deterministic (ask the same question twice, get different answers), while software engineering demands deterministic, reliable results. Without structure to bridge this gap, AI adoption fails spectacularly. This book introduces PIT™ (Plan Implement Test), a proven methodology that transforms unpredictable AI outputs into reliable engineering workflows. Built from thousands of hours of real development work, PIT™ channels AI's capabilities through structured planning, phased implementation, and comprehensive testing—delivering the consistency that production code demands.

Unlock true self-reliance with THE OFF-GRID MINDSET—your step-by-step guide to unshakable security in an uncertain world. Master resilient solar power, grow your food, purify water anywhere, and protect your family—no matter what the future brings. Stop surviving—start thriving with independence.

A manifesto against simulated empathy.A blueprint for ethical design.This is not a book about AI—it’s about what we mistake it for.

The first comprehensive guide to Taproot script engineering. Learn Bitcoin from the inside out — through real testnet transactions, stack execution, and Merkle trees. This book is evolving into an open, testnet-driven Taproot engineering lab.