Books

Assembly programming is actually a lot like Minecraft or Terraria because you start the game with nothing and have to slowly build your tools to make something useful. I started Assembly in 2024 and have already built a series of tools I personally use on both DOS and Linux operating systems. Through the course of this book, I will be slowly showing you how I can port everything in the Linux version of Assembly Arithmetic Algorithms to Windows.

You already know how to build software.You can design APIs, debug production incidents, reason about latency, operate databases, and ship features that people rely on. But AI engineering interviews ask a different set of questions:How do you design a RAG system that does not confidently invent answers? When should you use an agent—and when is a simple workflow safer? How do you evaluate an LLM feature when quality can decline without an error message? How do you control token costs, defend against prompt injection, and safely deploy a prompt change?AI Engineer – Interview Book bridges the gap between being a capable software engineer and being ready for an AI engineering interview.Learn how to reason about models, retrieval, agents, evaluations, cost, latency, safety, and production failure modes. Practice answers out loud. Learn the trade-offs behind the right answer—not just the vocabulary.Stop preparing for yesterday’s system-design interview. Start preparing to build AI systems that work in the real world.

Build real-time web apps that hold up beyond the demo. This hands-on guide takes you from WebSocket fundamentals and Socket.IO essentials to security, Redis scaling, monitoring and production deployment. Learn how the pieces work, why they matter and how to build fast, reliable systems ready for serious traffic.

Ever wondered what actually happens inside a Transformer? Follow “A-to-Z Transformer Walkthrough: “The capital of France is ___?”” through a complete miniature decoder-only Transformer—from embeddings, Q/K/V and multi-head attention to FFNs, logits, softmax, and finally “Paris.” Every major step is explained with real numbers and reproducible Python code.

Master Django by building the kind of web applications people actually use. Start with the fundamentals, then move into APIs, security, performance, frontend integration and production deployment. With practical projects and professional techniques throughout, this guide takes you from your first Django app to software built for the real world.

This book explains how Toyota uses DRBFM as a practical extension of FMEA during the design phase, where up to 80% of product quality is determined. Instead of repeating a generic FMEA, DRBFM zeroes in on design changes, asking: What could go wrong because of this change?

The secret lies in two simple patterns: Improvement Kata and Coaching Kata.In this concise and practical guide, Dr. Mohamed H. Soliman breaks down Toyota’s habit-building system for daily progress and leadership development.

One issue. One source. One proof question. A cleaner way to move from claim to evidence.

Mark the roles. Trace the authority. Separate the claim from the proof. Read the document as a map.

The correct chunk was retrieved. The answer was still wrong. Can you explain why in the next thirty seconds? Most candidates cannot. Cracking the RAG Interview trains you to be the one who can.

Three full passes. One focused half-pass. A repeatable way to find patterns without losing the evidence trail.

La IA no toma decisiones: traduce el criterio del profesional que conoce su organización. Primero es el criterio. Hasta después es la herramienta.

The eight volumes before this one each teach a single domain, and each is deliberatelyself-contained. The exams do not ask about domains — they ask about systems, and a system spansall of them at once. "A retrieval system is slow, expensive and occasionally wrong — which do you fix first?" needsretrieval, optimisation and operations together. Every one of the 150 questions here crosses atleast two domains, because that is the specific thing a single-domain book cannot teach you.

"The model is safe." "The filter is effective." "We red-teamed it and found nothing." "Wereviewed a sample and it looked fine." Each of those is a claim about evidence, and each is unfalsifiable as stated. Reviewing thirtyoutputs and finding no problems is consistent with a failure rate of one in eleven. Threehundred tests against a thousand possible failure modes leave seven hundred nobody looked at. This book supplies the missing arithmetic — and it was written without generating a singleharmful output to study.

A model that works in development and fails in production usually fails for reasons that havenothing to do with the model. It waits behind other requests. It loads slowly after a restart.It answers differently because somebody moved a decoding parameter by a tenth of a point. Aguardrail blocks a third of legitimate traffic and reports itself as working. None of those appear in a benchmark. They appear in an incident — and almost every one of themis invisible in an instantaneous measurement and obvious in a trend.