Books

A short, hands-on guide to reverse engineering with AI agents and REA 6.4.0: drive Ghidra from Claude Code, Codex or Cursor, check every claim against evidence, and work through native, Electron, Android, .NET and web targets you are allowed to analyse. Every output shown is a real lab run.

A Comprehensive Engineering Guide to Low-Precision AI, Quantized Inference, GPU Kernels, and Production DeploymentQuantization is one of the key techniques for making large language models more efficient. By representing weights, activations, and other model data at reduced precision, engineers can lower memory requirements, improve hardware utilization, and potentially increase inference throughput. Achieving these benefits, however, requires careful trade-offs between numerical accuracy, computational cost, memory bandwidth, and hardware support. Mastering LLM Quantization: Architecture and Code is a 44-chapter engineering reference covering the mathematics, algorithms, implementation techniques, GPU kernels, evaluation methods, and deployment systems behind quantized language models. It connects theoretical foundations with practical approaches to building and optimizing low-precision inference pipelines.

Build, debug and maintain websites with Claude Code through 7 practical workflows. Includes a downloadable practice project, reference code and 30 automated tests to help you verify what the AI produces.

Curious about agentic AI but not sure where to start? This beginner-friendly guide takes you from the basics to building your own AI agents, even if you have no coding experience. Learn how agents work, choose the right tools and turn your ideas into practical, reliable solutions for real-world problems.

Kill an AI agent mid-refund and the retry bills every model call again and refunds twice. This book crashes one agent on purpose and recovers it on DBOS, Temporal, Restate and Prefect, with the real output of every run.

Use AI-assisted engineering from core concepts to secure production workflows. Visual explanations and a continuing FleetOps case study connect context design, coding, testing, delivery, operations, and bounded agents with practical verification and human accountability.

Understand how independent AI agents discover each other, exchange messages, and coordinate tasks. Clear diagrams introduce Agent2Agent architecture, interaction patterns, security, and reliable operation.

EmbeddingGemma 2 offers a hands-on look at Google's embedding model and how to put it to work. From semantic search and RAG to code retrieval and multimodal applications, this guide covers the architecture, trade-offs and practical implementation details you need to build reliable systems and make the right choices for your stack.

Your LLM pipeline passed every check, and a fallback model still served 50 of its 71 successful calls. This case study follows one real multi-stage pipeline through its failures and shows how to make each stage traceable, attributable and honestly measured.

Decision models answer a typed question with a calibrated probability in one forward pass: no token stream, no parsing, no values outside your schema. This guide explains TypeSafe's Jev and the open "JEV-like" family, then shows how to choose, run, serve, fine-tune and evaluate local variants on your own hardware.

An agent's answer can look right while its actions are wrong. Learn to evaluate tasks, traces, tool calls and changed state with reproducible harnesses, tested graders and regression gates.

Your agent just read a file you didn't think mattered, followed an instruction hidden in a web page, and pushed a commit. None of it looked suspicious. That's the shape of agentic security — and *The Agentic Security Playbook* is how you close the gap before someone else finds it.

Você viu um modelo de linguagem fazer meia hora de trabalho sozinho. Este livro trata da pergunta que veio logo depois: até que ponto você pode confiar nele?

Has visto a un modelo de lenguaje hacer media hora de trabajo por su cuenta. Este libro trata de la pregunta que vino después: ¿hasta qué punto puedes confiar en él?

Build AI inference systems that run fast, use less memory and keep your data where it belongs. This hands-on guide walks you through model deployment, performance tuning, security and production engineering with practical code and real benchmarks. No cloud dependency, no surprise bills, just efficient AI systems you control.