Books

Most blockchain books teach the theory. This one walks you through building a complete Bitcoin-style blockchain in Rust, end to end — cryptography, consensus, networking, persistence, REST API, desktop wallets, and Kubernetes deployment.

Bad data breaks good code. You’ve written Python that works perfectly in testing, only to watch it fail in production because of a malformed API request, a messy CSV, or a missing config value. That’s the hidden cost of Python’s flexibility: without runtime validation, you’re always one bad input away from a crash. Enter Pydantic. This book takes you from the foundations of data validation to real-world applications in APIs, data pipelines, configurations, and machine learning workflows. Along the way, you’ll explore practical techniques, advanced features, and alternatives like Marshmallow, attrs, and dataclasses, so you’ll always know which tool is right for the job. If you’re a Python developer, data engineer, or FastAPI user, this is your roadmap to writing safer, cleaner, and more reliable code.

Learn databases from the bottom up by coding your own, in small steps, and with simple Go code (language agnostic).Atomicity & durability. A DB is more than files!Persist data with fsync.Crash recovery.KV store based on B-tree.Disk-based data structures.Space management with a free list.Relational DB on top of KV.Learn how tables and indexes are related to B-trees.SQL-like query language; parser & interpreter.Concurrent transactions with copy-on-write data structures.

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.

45% of AI-generated code has vulnerabilities. This book covers prompt injection, data leakage, secure coding with AI tools, MCP server hardening, agent sandboxing, supply chain security, and OWASP LLM compliance. 14 chapters with attack demos and defense code.

"Kubernetes is powerful—but when things break, do you know how to fix them?From CrashLoopBackOff to networking black holes, Kubernetes failures can cripple your applications. "Kubernetes Troubleshooting Guide" is your go-to resource for diagnosing and resolving issues fast. Whether you’re a DevOps engineer, SRE, or developer, this book arms you with practical debugging techniques, real-world scenarios, and proactive strategies to keep your clusters healthy.Stop guessing. Start fixing. Master Kubernetes troubleshooting—before your next outage."

Master Object-Oriented Programming in C# and take control of your software design. This comprehensive guide focuses on deep conceptual understanding—vital for guiding development effectively, especially with AI assistance. Explore the core OOP pillars, unravel the SOLID principles, implement key Design Patterns, and see how Dependency Injection shapes modern .NET applications. Learn to leverage recent C# features for better encapsulation and immutability, building efficient, testable, and truly maintainable applications through practical examples and unique AI collaboration prompts.

Master PostgreSQL administration with a practical guide to deploying, securing, optimizing, and operating production databases. Covering PostgreSQL 17 and 18, this book combines real-world examples, hands-on labs, and proven best practices for reliable, high-performance systems.

We will work in pure Rust, with no C++ shim or Python wrapper. We will build one small program that grows to cover the whole book. It begins as a loop that any of us could have written on a bad day. After gaining a kernel, it learns to stop waiting, grows streams and pinned memory, and ends as a pipeline fed by parallel producers through bounded channels. The device is busy almost every millisecond.

Build, Train, Optimize, Secure, and Deploy Large Language Models Under Full Infrastructure ControlLarge language models are increasingly treated as hosted services, but organizations seeking genuine control over their AI capabilities must understand and engineer the infrastructure behind them. Data ownership, compute resources, model weights, training pipelines, inference runtimes, security boundaries, and operational telemetry all contribute to the independence and reliability of an AI system.

A book for Tech Leads, from Tech Leads. Discover how more than 35 Tech Leads find the delicate balance between the technical and non-technical worlds. Discover the challenges a Tech Lead faces and how to overcome them. You may be surprised by the lessons they have to share.

This book teaches string and data structure algorithms the way engineers actually reason through them: one character, one pointer, one decision at a time. Every pattern inside is shown three ways — a plain-language explanation of what it does and why, a real working C# implementation you can compile and run yourself, and a full pictorial walkthrough that traces the index, the window, the stack, and the trie through every step.

Strings - Beginner → Intermediate → Advanced - C++ Edition brings both volumes together: 102 string algorithms taught through plain-language explanations, C++ implementations, and step-by-step visual walkthroughs that trace the... Printed in full color.

Your backend will run on Linux sooner or later!

Build a complete, production-grade microservices platform with Spring Boot 4 and Java 21, one chapter at a time.