Books

Build production-ready applications with TypeScript in the age of AI. Go beyond the basics to master type-safe development, modern full-stack architectures, LLMs, intelligent agents, tool calling and the Model Context Protocol. With practical, runnable examples throughout, this book shows you how to build, test, secure and deploy serious TypeScript AI systems.

My pitch was that rules written in CLAUDE.md get skipped, so you need enforcement. Then I measured it. In 22 trials with the ban written in, the banned command was never attempted. In 17 without it, 17 out of 17. This is the whole record: the instrument, every trial, the limits, and the harness so you can check me.

The second half of a from-scratch AI/ML course that refuses to skip the hard parts: LSTMs, real attention, a working transformer, LoRA fine-tuning derived and measured, and a genuine multi-tool, multi-agent system — every mechanism built by hand, then checked against PyTorch and real numbers.

Build a real, production-grade design system on the web platform — Prism DS, from a single custom element to an AI-agent-ready component library — with every example tested in a real browser before it made it into the book.

Building an LLM demo is easy. Running one reliably at scale is not. Production LLMOps shows you how to design, deploy and operate real-world LLM systems, covering RAG, agents, fine-tuning, evaluation, CI/CD, observability, security and more, with practical code you can adapt for production.

A from-scratch AI/ML course that treats you like an engineer, not a tourist — three modules covering language-as-numbers, the math foundations, and neural networks, each concept built in raw Python first, then PyTorch, so you always know what's really happening under the hood.

Build AI that keeps working when the cloud doesn't. Local-First AI Engineering shows you how to run capable, private AI systems on infrastructure you control. From hardware and inference to RAG, agents, security and production ops, you'll learn how to build local AI that is fast, reliable and truly yours.

Build AI systems that do more than demo well. This hands-on guide shows you how to engineer autonomous software on Linux that is secure, observable, reliable and ready for production. From agents and memory to threat modeling, OpenTelemetry and Kubernetes, you’ll build a real system while learning what it takes to run AI you can actually trust.

Move beyond AI demos and build agents that can actually run IT operations. This practical guide takes you from MCP fundamentals to production-ready monitoring, incident response, remediation, Kubernetes operations, security and multi-agent systems, with complete runnable code and a strong focus on safe autonomy.

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.

一本面向有一定 AI 基础的工程师、技术负责人与产品人的实战手册。它回答一个被大多数 AI 内容回避的问题：为什么大多数 AI 项目止步于 Demo，而少数能真正跑进生产？

Move Beyond Simple Prompts. Build Reliable, Autonomous AI Systems.Connecting an LLM to an API is easy; building an AI agent that works reliably in production is hard. Mastering AI Agents gives you the exact architectural frameworks, workflows, and engineering practices needed to design, deploy, and scale intelligent agents that solve real-world problems.Practical Engineering: Master memory systems, tool integration, multi-agent orchestration, and system security.Production-Ready Focus: Learn how to evaluate, monitor, and maintain agents so they remain controllable and safe.Evergreen Principles: Built on core software architecture principles that stay valuable even as frameworks and models evolve.Grab your copy today and start building the next generation of intelligent software!

Short Description of the BookSecuring Modern AI Applications is a practical guide to securing LLM-powered applications, RAG systems, AI agents, and modern AI development environments.The book explains AI-specific threats such as prompt injection, data leakage, RAG poisoning, insecure tool use, agent abuse, and AI supply-chain risks while also covering traditional application security, privacy, governance, secure development, monitoring, testing, and red teaming.Designed for developers, AI engineers, cybersecurity professionals, architects, and technology leaders, the book focuses on practical principles that remain useful as AI technologies continue to evolve.Build AI. Secure AI. Govern AI.

This book presents a modern, unified, and language-agnostic workflow for creating analytical pipelines that are truly reproducible, from environment to execution.