Books

Reverse engineering gets a powerful upgrade with Claude Code. Learn how to investigate, understand and reconstruct software you’re authorized to analyze, while keeping evidence, accuracy and reproducibility at the center. From legacy systems to modern codebases, this book turns AI into a practical partner for serious software analysis.

Master LTX 2.5 from the first prompt to the final shot. Learn to create cinematic video with synchronized audio, precise camera movement and consistent characters while avoiding the mistakes that waste time and generations. Built on official documentation and verified behavior, this is a practical guide to getting production-ready results from LTX 2.5.

Move beyond individual prompts and engineer the full information environment an AI model receives. Learn to design, test, measure, and govern context for reliable AI workflows.

Static code analysis is more than running a linter and fixing warnings. This book shows how to build practical analysis pipelines with Claude Code and deterministic tools, combining AI-driven insights with reliable checks to improve code quality and security across projects of any size.

A vendor-neutral, mechanism-level field guide to operating and extending agentic coding harnesses (538 manuscript pages).

Unlock the full potential of Claude Fable 5.1 with practical prompting techniques built for real-world work. Learn how to get better results from coding, research and complex workflows, avoid common mistakes and build reliable AI systems with proven strategies and ready-to-use prompts.

What happens when AI agents stop working alone and start working as a team?Artificial Intelligence is entering a new phase in which intelligent systems can do more than respond to individual instructions. Multiple specialized agents can collaborate, divide complex tasks, communicate with one another, use tools, evaluate results, and coordinate their actions toward a shared objective.Multi-Agent AI Systems: The Complete Handbook for Building Intelligent, Scalable, and Autonomous Agent Teams provides a practical roadmap for understanding this emerging paradigm.The book begins with the fundamentals of multi-agent systems and explains why collaboration between specialized agents can be valuable for complex workflows. Readers will learn about hierarchical, peer-to-peer, and hybrid architectures, along with roles such as manager, planner, worker, critic, and supervisor agents.It then moves into modern frameworks and technologies, including CrewAI, AutoGen, LangGraph, MetaGPT, LLMs, vector databases, and agent memory systems. Practical chapters explain how to design agent teams, decompose tasks, establish communication protocols, manage shared memory, coordinate workflows, integrate tools and APIs, and recover from failures.Readers will also explore advanced concepts such as dynamic replanning, parallel execution, swarm intelligence, agent debates, self-organizing systems, human-in-the-loop workflows, and multimodal agents.The book goes beyond experimentation and addresses the challenges of deploying multi-agent systems in real environments. Cloud deployment, Docker, Kubernetes, monitoring, logging, scaling, evaluation, benchmarking, testing, and cost optimization are included.Real-world applications demonstrate how agent teams can support software development, research, customer service, content creation, and business operations.Equally important, the book examines AI safety, privacy, security, transparency, governance, alignment, and responsible AI development.Whether you are a student discovering agentic AI, a developer building your first agent team, a researcher exploring collaborative intelligence, or a professional preparing for the next generation of AI applications, this book provides a foundation for moving from individual AI agents toward coordinated intelligent systems.Understand the architecture. Design the team. Build the agents. Coordinate the intelligence.

The first thing many people hit with an AI coding assistant is a plateau. A correction made on Monday is gone by Thursday. This book is about the operating model above the prompt, where truth lives, how a correction becomes a standing rule that holds, and which judgments stay in human hands.

Build real-world web applications faster with Claude Code by your side. This hands-on guide takes you from web development fundamentals to production-ready full-stack engineering with React, Next.js, TypeScript and PostgreSQL. Learn practical AI-assisted workflows while building, testing, securing and deploying applications that are ready for real users.

Learn C, Python, and assembly — not just the syntax, but the thinking behind them. This book helps you understand computers, write real code, and see where technology meets the real world.

A practical, code first guide to building reliable, safe, and cost effective LLM systems in 2026. Learn how modern models really read your prompts, how to orchestrate tool using agents, and how to harden your systems against prompt injection, all with runnable examples across the major AI APIs.

Unlock the full potential of Qwen 3.8 with a practical guide built for real results. Learn the techniques behind stronger prompts, avoid common mistakes, and create reliable outputs for coding, research, business, creative projects and AI agents with clear examples you can use right away.

A working engineer's guide to building and maintaining production software with autonomous terminal coding agents (410 manuscript pages).

Discover how to unlock the full potential of Claude Opus 5 with a practical guide built on Anthropic's official documentation and proven best practices. From writing your first effective prompt to designing sophisticated agentic workflows and production-ready systems this book gives you the techniques, patterns and insights needed to achieve more accurate, reliable and consistent results.

An agent can score well on average and still fail exactly where it matters. Beyond “Ship and Pray” shows how to replace benchmark averages with designed experiments, geometric ground truth and failure attribution—so teams can discover when an agent breaks, identify the responsible component and test whether it fails safely under tool faults.