Debugging

A local model returns the expected answer. How do we know which layers ran on the GPU? In Local AI on RTX Spark Laptops, we investigate questions like this through 251 hands-on WSL2 exercises, following the evidence from model-loader logs down to individual CUDA kernels.

AI can write code, create images and make decisions, but do you really understand what happens behind the scenes? This book cuts through the hype to explore how AI works, where it falls short and what it takes to build reliable systems. If you want to understand AI beyond the chat window, start with the code.

What can you learn from hardware that comes with no documentation, missing schematics or a protocol nobody has explained? This book explores practical methods for uncovering how electronic systems work, combining hands-on investigation, software analysis and AI to turn unknown hardware into something you can understand, analyze and rebuild.

You have built an app with AI assistance. Now learn how to check what it actually does. Develop a repeatable method to test, fix and verify what you build.

When systems fail, theory meets reality. This book explores how to design, build and operate software that holds up under pressure, from unexpected outages to subtle failures buried deep in the architecture. Learn the principles and practical techniques that help engineers make better decisions, reduce risk and build systems that keep working when things go wrong.

A practical guide for IT support staff to identify, test and resolve technical problems without guessing their way into the next incident.

We'll be working on a platform made up of seventy-nine recipes together. It starts off as a simple task, printing a line, but by the last chapter it covers extraction, warehousing, containers, machine learning and incident response. You can't just throw away examples in your work, and you shouldn't be doing that in your examples either. You don't need to be an Airflow expert to get started. What you're really learning here isn't a tool. It's all about making sure work is repeatable, observable and safe to rerun.

A brilliant engineer with no evidence is reduced to guessing, while an ordinary engineer with good evidence looks like a genius. Everything in this book is an attempt to get you to think differently. You don't need a huge team to do this. You don't need to get anyone's approval for a budget, and you don't have to rewrite any services. What you need is a system that tells you the truth about itself, and you need it before the incident rather than during it.As a team, we'll build that system together on one small storefront, and we'll do it the way real teams build, which is to say not perfectly but in order.

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.

Some bugs refuse to die. They hide for years, disappear when you look for them and surface only when the conditions are just wrong. Debugging the Impossible shows you how to hunt them down systematically, uncover what is really happening and fix the root cause for good, using practical techniques, runnable code and lessons from real-world failures.