Artificial Intelligence

StackPilot shows how to use graph-grounded AI to plan and explain infrastructure changes while keeping authorization, execution, and accountability under deterministic and human control. It is a practical guide for building safer AI-assisted infrastructure operations—from typed intent and evidence retrieval to approval manifests, observation, and earned production authority.

Every news feed is full of AI buzzwords: Transformers, tokens, embeddings, context windows, hallucinations, objective functions. Yet most explanations are either dense academic textbooks or empty marketing fluff. ☕ Coffee Break AI is your practical guide to AI. Written in plain English with warm real-world analogies. It breaks down the core mechanisms of AI into 40 bite-sized chapters.

AI-generated code can look flawless while hiding invented APIs, security gaps and subtle logic errors. This practical guide shows you how to build production-ready analyzers that catch what tests miss, from taint tracking and symbolic execution to custom rules for CI/CD and IDEs.

Building an impressive AI agent demo is easy. Running one in production—reliably, at scale, with governance—is where most enterprises get stuck. Agentic Orchestration shows how BPMN-based process orchestration governs AI agents at scale, drawing on real deployments across banking, telecom, and healthcare. Reasoning without orchestration is expensive chaos. This book shows the way out.

16 章手写一个 CLI Agent。从 Hello World 到 Skill 插件系统，只用 Python 标准库 + OpenAI SDK。每章附真实项目踩坑笔记——来自 git 提交历史的 bug、设计失误和凌晨调试的教训。读完你会理解 Agent 的底层运作方式，而非只是会用某个框架。

Software changed its front door: we don't open apps anymore; we talk to an assistant. Learn to build for that world —a real AI-native app with MCP, OAuth, and LLMs— using AI as your copilot. From zero to production.

Your AI has not forgotten you. It simply cannot show you what it knows, let you correct it, take it with you, or act on it. This book fixes all four: plain files you own, an assistant with hands on them, rules it will not cross, and work that lands before you wake up. No code, no terminal.

El software cambió de puerta de entrada: ya no abrimos apps, le hablamos a un asistente. Aprende a construir para ese mundo —una app AI-native real con MCP, OAuth y LLMs— usando la IA como copiloto. De cero a producción.

The future of AI depends on how we measure it. This book cuts through the hype to show what benchmarks really tell us, where they fall short and why better evaluation leads to better models. Practical, clear and grounded in real-world experience, it is an essential guide for anyone building, studying or deploying modern AI.

Build serious AI systems on NVIDIA DGX Spark with a practical guide that goes far beyond setup. Learn to deploy LLMs, create RAG pipelines, orchestrate AI agents, and optimize performance for real production workloads. Whether you are experimenting or scaling enterprise AI, this book shows you how to get there.

A £4.1M PRA compliance failure. A fictional bank called Avon & Wessex. Sixteen chapters and 23 systems showing exactly how to build production-grade, auditable AI for credit, market, operational and liquidity risk, model governance, and regulatory reporting — the practitioner's guide UK banking has been missing.

What if TransE, ComplEx, RotatE and the rest of the knowledge graph “model zoo” were different views of one geometric operator? Learn the mathematics, code and practical design principles behind structured memory for trustworthy AI.

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.

Top-k is not relevance, retrieved text is not evidence and an LLM judging another LLM is not verification. Beyond “Chunk and Pray” shows how to build RAG that answers through a verified knowledge graph, preserves exact numbers, cites its sources and abstains when it cannot prove the answer.