Books

The practitioner's guide to Claude Code in production. Thirty chapters covering the agent loop, tools, hooks, MCP, permissions, evals, observability, and cost engineering for AI agents that actually scale.

The definitive guide to agentic software engineering with Codex CLI, from prompting fundamentals to multi-agent orchestration, CI/CD integration, and enterprise deployment across 32 hands-on chapters.

A clear, illustrated guide to large language models, covering key concepts and practical applications. Ideal for projects, interviews, or personal learning.

Master language models through mathematics, illustrations, and code―and build your own from scratch!

Everything you really need to know in Machine Learning in a hundred pages.

The essentials of making predictions using supervised regression and classification for tabular data. Tech stack: python, pandas, scikit-learn, CatBoost, LightGBM, XGBoost, TabPFN, TabICL

It's never been easier to build an AI agent—and never been harder to make one that actually works. This book takes you from language model foundations to production-ready multi-agent systems, with the depth to understand what you're building and why it fails.

Metagraphs for Agentic AI: Beyond Triples, Beyond HypergraphsFrom Knowledge Graphs to Knowledge ArchitecturesThe triple is not enough.Every AI engineer building agent memory hits the same wall. You model a meeting as a knowledge graph triple — and immediately lose the fact that five people were in the room, a decision was made, and that decision caused three downstream actions. You reify. You flatten. You create workarounds. And your "knowledge graph" becomes a tangle of auxiliary nodes that machines can traverse but no human can reason about.This book shows you the way out. What You'll Learn Metagraphs are graph structures where edges connect sets of nodes to sets of nodes — and where edges themselves can be referenced as first-class nodes. They are the missing data structure for AI agents that need to remember, reason, and coordinate like humans do.This book takes you on a complete journey:Hypergraphs first. You'll learn what they are, why they matter, and where they break down. You'll implement them three ways — in SQL, in LadybugDB (Cypher), and in TypeDB — so you understand the tradeoffs viscerally, not just theoretically.Then metagraphs. You'll see how metagraphs solve the fundamental hypergraph problem (edges that can't be nodes), explore RDF named graphs as a lightweight metagraph, and implement full metagraphs in the same three database paradigms with production-ready, commented code.Then the big ideas. Semantic Spacetime. Holonic systems. Human cognitive architecture mapped to graph structures. Multi-agent coordination. Promise Theory for autonomous AI networks. This is where metagraphs stop being a data structure and become an architecture for intelligence. Who This Book Is For You're a software engineer, AI researcher, or knowledge graph practitioner who builds real systems. You've used Neo4j or RDF stores. You've built RAG pipelines. You've felt the limits. You want to know what comes next.No PhD required. Every concept comes with working code in SQL, Cypher, TypeQL, SPARQL, and Python. What Makes This Book Different This isn't a theoretical monograph. It's the distillation of two and a half years of research, 130+ published articles, and hands-on implementation at the intersection of knowledge graphs and agentic AI.Every chapter bridges theory and practice. You'll read about Basu and Blanning's formal metagraph definition — and then build the schema in PostgreSQL. You'll learn Mark Burgess's Promise Theory — and then model a multi-agent coordination protocol as a six-layer promise graph. You'll understand why labeled property graphs are secretly metagraphs — and what that means for your Neo4j deployment today. 18 Chapters. Three Parts. One Argument. Part I — The Hypergraph Foundation (7 chapters): From the knowledge representation crisis through hypergraph theory to three complete database implementations.Part II — The Metagraph Solution (5 chapters): Metagraphs as the answer, RDF named graphs as a bridge, and three full metagraph implementations with detailed commentary.Part III — Theory Meets Practice (6 chapters): Semantic Spacetime, labeled property graphs, AI memory and human cognition, holonic systems, agent-to-agent interaction, and Promise Graphs for network-of-networks coordination. The Core Thesis If you want AI agents that reason like humans, you need knowledge structures that capture how humans actually organize knowledge — not as flat collections of facts, but as nested, hierarchical, context-rich, temporally-aware structures where relationships themselves carry meaning and can be the subject of further reasoning.Metagraphs are that structure. This book shows you why, and how to build with them.

AI can generate code faster than ever. But speed is no longer the hardest part of software development.The real challenge is building systems where generated code remains correct, controlled, and aligned with architectural intent. Becoming a Harness-Driven Developer introduces a new development model for the age of AI-assisted engineering - one where developers focus less on writing every line by hand and more on defining the rules, structure, and enforcement mechanisms within which AI code agents operate.Through a practical repository mapped directly to the book’s chapters, this book shows how to move from ad hoc coding to a harness-driven approach based on specification, architecture, invariants, controlled execution, and system evolution. This is a book about staying in control while software development changes.

Skip the black-box frameworks. Build a production-grade AI coding agent from scratch in pure Python - cloud or local, tested with pytest, all in a single file.

Master AI-powered infrastructure automation with this hands-on guide to building production-ready MCP servers and AI agents in Go. Transform from manual AWS operations to intelligent automation that understands your environment and makes smart decisions while keeping humans in control.

Vector stores don't think — they search. They find fragments that sound like your query, then forget they ever looked. Every session starts from nothing. Every context window is a memory that dissolves at sunset.But the deeper problem isn't amnesia. It's that when agents do remember, they remember in someone else's house — on servers you don't control, in formats you can't inspect, under terms you didn't write.Memory Graph is a book about building something different: persistent, structured, queryable memory that lives inside your application — no external servers, no data leaving your process, no infrastructure you don't own. An embedded graph database that travels with your agent the way a nervous system travels with a body.You'll learn how to model not just facts, but relationships between facts. Causality. Temporal ordering. The layered structure of meaning that makes memory more than a search index. You'll build ontologies that enforce what can be known and how. You'll combine graph traversal with semantic search — so your agents find not just what's similar, but what's connected.The result is an agent that remembers the way you do: structurally, contextually, privately — with memory that belongs to you.

By the time you finish this book, you should be able to make your systems observable across microservices, AI workloads, security monitoring, and hybrid cloud infrastructure. This book will help you learn how to effectively instrument, generate, collect, and export telemetry data (metrics, logs, and traces) to analyze your software’s performance and behavior.

Usar IA para programar es fácil. Usarla sin perder el control, no tanto. Spec-Driven Development es el método para convertir tu idea en una spec que la IA ejecuta con precisión — sin loops infinitos, sin código roto, sin empezar de cero.

Using AI to code is easy. Using it without losing control — not so much.By week three, your project stops moving. The agent forgets decisions it made ten days ago. A change in auth breaks the dashboard. You spend more time re-explaining context than writing features. The code works, but only you know why — and you're not even sure you remember all of it.That's vibe coding hitting its ceiling.Spec-Driven Development is the method that replaces the chaos with a spec the AI actually executes. Not a ceremonial document. A working artifact: PRD, issues, tests, code — all traceable, all connected, all in the right order.This book shows you:How to grill your own idea before writing a single promptHow to write a PRD the AI won't misinterpretThe 7 phases that turn an idea into working softwareHow to use GitHub SpecKit and openSpec (and when not to)How to work this way in a team without slowing downThe 5 anti-patterns that destroy every spec22,000 words. 13 chapters. 5 appendices with ready-to-copy templates.No theory dumps. No filler. Just the method.