Artificial Intelligence

Learn how to build intelligent, production-ready AI agents with the OpenAI Agents SDK. Through practical Python examples and step by step guidance, you'll master everything from the fundamentals to advanced multi-agent workflows, tools, and deployment.

Build intelligent AI applications with LangChain using practical, production-ready Python examples. From intelligent agents and retrieval-augmented generation to scalable deployment and observability, this book equips you with the skills and architectural understanding needed to create reliable, real-world LLM-powered systems.

A practical people's guide to building AI agent businesses that deliver completed work, not just dashboards. Learn how to find high value workflows, build trusted agents, design control rooms, price outcomes, and move from copilot to controlled autopilot.

Learn how to build autonomous, production-ready multi-agent AI systems in C# with .NET. Using Harness, Hermes Agents, and Loop Engineering, you'll create intelligent agents that reason, collaborate, and solve real-world problems through practical examples and hands-on projects.

You don't have to be a programmer to learn from this book. This book asks nothing of you but your willingness to tell Claude what and how you think. I've written this for the professional who's been thinking, maybe quietly, that their real value wasn't in the busywork. You can automate anything that's between you and them.

Build smarter AI systems that go beyond the limits of large language models. Retrieval-Augmented Generation is a practical guide to designing, implementing, and scaling RAG applications with modern retrieval techniques, vector databases, and real-world deployment strategies.

A practical, systems-first guide to understanding LLMs, RAG, AI agents, GraphRAG, evaluation, fine-tuning, and modern AI applications — without needing to start with equations.

Build and operate production AI platforms on Kubernetes. Learn to manage NVIDIA GPUs, serve and optimize LLMs with vLLM, run training and batch workloads, and design secure, observable, multi-tenant infrastructure for AI at scale.

The Codex Playbook helps enterprise engineering teams turn Codex from a helpful assistant into a reliable part of the software delivery system, with practical patterns for context engineering, repositories, GitHub workflows, MCP, governance, and AI-assisted development.

Local Intelligence shows you how to run large language models entirely on your Mac with Apple Silicon. Learn to use tools like Ollama, MLX, and llama.cpp, understand quantization, and build real local AI applications with open-source code.

AI的边界，就是你的方向。从大模型的底层命门，推导出你不可替代的理由。

You shipped the demo. The model worked. Now production is coming, and the demo is not a system. An agentic system is a distributed-systems engineering problem — the reliability lives in the shell, not the model.