Books

The 350-701 exam changed in August 2026. This guide was written for the version you are about to sit — all six domains, all 47 topics, in blueprint order.

As data breaches become more common, the cost per incident averages at about $2.2 million. This book will allow you to follow the most common and successful attacks a professional penetration tester uses to find gaps in security before the malicious actors do. Paul is sharing his secrets from over 10 years of experience as a security consultant.

Your AI agent is only as smart as what it remembers. Most developers treat knowledge representation as an afterthought -- a data structure problem, not an architecture problem. They flatten complexity into schemas that can't express relationships, deploy embeddings without grounding, and build agents that degrade under their own reasoning load. This book changes that. **TypeDB for Edge AI Agents** is a practitioner's guide to building knowledge systems that actually work at the scale and complexity your agents demand. TypeDB is built on type theory -- the same mathematics that powers formal verification and programming language safety. For agents, that means you can express constraints that prevent bugs in your knowledge layer, reason about what's possible and what's forbidden, and build memory systems that don't require constant hallucination detection. With the Rust rewrite in TypeDB 3.0, your agents can carry sophisticated knowledge graphs on-device -- no round-trip to a server, no latency, no external dependencies. This book covers everything from the PERA data model and OWL ontologies to promise graphs for multi-agent coordination, giving you the patterns to design knowledge systems that scale without becoming incoherent. Whether you're an ML engineer, AI architect, or backend developer building production agent systems, this book bridges the gap between type theory and working code. You'll learn to design knowledge graphs that don't decay, structure agent memory that doesn't degrade over time, and coordinate multi-agent systems that respect causality and distributed constraints. Stop building agents that confidently hallucinate -- start building agents that reason correctly, remember reliably, and coordinate with certainty.

A Collection of Explorations in Code and ThoughtNot your typical textbook. Not a structured guide. This is a deep dive into the tangled web of logic, algorithms, and modern AI—told in code, mathematics, and mild discussion.

Build financial systems that stay correct when requests repeat, events arrive late, and services fail. Learn practical patterns for money modeling, double-entry ledgers, idempotency, reconciliation, auditability, security, and invariant-driven testing—so every balance and transaction can be explained and trusted.

You’ll find inside:- An atlas of system topologies.- A compendium of architectural patterns.- One of the largest and most cohesive pattern languages.

AI engineering is becoming one of the most valuable and in-demand areas of software development, and Python, LangChain, and LangGraph are core skills for building the systems behind it. Learn how to turn basic Python knowledge into production-grade backend and agentic AI applications—and move toward an engineering niche centered on building and controlling AI rather than competing with it.

The book covers every topic in the latest Security+ exam syllabus (SY0-701), with 300 pages organized in a format that makes it easy to drill down on specific exam domains and concepts at-a-glance, making it an essential exam resource for anyone who aims to prepare for the exam without wasting time or money.

Modern cloud-native applications generate massive volumes of telemetry data, making observability increasingly expensive and difficult to scale. While OpenTelemetry has become the industry standard for distributed tracing, traditional tail-based sampling architectures often require stateful collector clusters with significant memory and processing overhead. Rethinking OpenTelemetry Tail-Based Sampling explores how Event-Driven Architecture (EDA) and event mesh technologies can be used to address these challenges. Through practical examples, architectural analysis, and implementation considerations, this book examines OpenTelemetry sampling strategies and presents an alternative approach that externalizes trace buffering and sampling coordination beyond the collector layer. Whether you are an observability engineer, platform architect, integration specialist, SRE, or cloud-native practitioner, this book provides practical guidance for designing scalable, resilient, and cost-effective distributed tracing platforms.

Ever wondered what really happens inside a terminal? Go beyond the command line and explore the systems that make modern terminals work. From TTY and PTY internals to Unicode, GPU-accelerated rendering and terminal protocols, this book takes you under the hood and brings it all together with a fully working terminal emulator built in Rust.

What if AI could help build software that is not just functional but mathematically proven to work? This book shows how to combine Claude’s coding capabilities with Lean 4’s formal verification to create more reliable software. A practical guide to turning ambitious ideas into verified systems, without losing sight of real-world constraints.

Explore the hidden weaknesses of large language models and the techniques used to push their safety boundaries. From foundational concepts to advanced exploitation strategies, this book offers a deep dive into the mechanics of AI jailbreaking, backed by practical demonstrations and research-driven insights into detection, mitigation and responsible AI security.

What if you could understand what people are really thinking, even when they never say a word? Discover the psychology behind human behavior, learn to recognize hidden emotions, spot deception and understand the intentions that shape every interaction. The Dark Art of Reading Minds reveals how to see beyond appearances and understand people on a deeper level.

Ready to move beyond AI prototypes? Learn how to build agents that reason, plan, use tools and tackle real-world tasks. From core concepts to production-ready architectures, this practical guide covers the code, design decisions and engineering challenges you need to build reliable, secure and scalable agentic systems.

An agent's answer can look right while its actions are wrong. Learn to evaluate tasks, traces, tool calls and changed state with reproducible harnesses, tested graders and regression gates.