Books

The first book that addresses engineering management specifically for the AI-native era. Classic EM books like The Manager's Path and An Elegant Puzzle predate agentic coding tools — this one starts where they left off. Every chapter produces a concrete artifact: a hiring rubric, a performance calibration guide, a budget template, a rollout plan. By the end, you have a complete management playbook for leading engineering teams where agents write most of the code.

Kubernetes exposes plenty of state, but operators still have to reconstruct operational meaning from scattered Pods, Services, EndpointSlices, Events, PVCs, owner references, and status fields. This ebook uses `kctx`, a small read-only Kubernetes context engine, to show how deterministic entities, relations, signals, graphs, namespace snapshots, CRD adapters, and stable JSON contracts can turn raw cluster data into reusable context for humans, tools, and AI agents. It is written for SREs, platform engineers, Kubernetes operators, infrastructure developers, and AI tooling builders who want better primitives than raw YAML and improvised troubleshooting pipelines.

How should an AI system make decisions when information is incomplete?How can machines quantify uncertainty instead of merely producing predictions?How can intelligent systems continuously update their beliefs as new evidence emerges?The answer lies in Bayesian Mathematics.In Bayesian Mathematics for AI Decision Making, Anshuman Mishra explores the powerful framework that enables modern AI systems to reason probabilistically, model uncertainty, and make rational decisions in complex environments.From Bayesian inference and probabilistic programming to uncertainty-aware deep learning, reinforcement learning, healthcare diagnostics, robotics, and financial forecasting, this book reveals how Bayesian thinking is shaping the next generation of Artificial Intelligence.Learn how uncertainty becomes knowledge—and how probability becomes intelligence.

A hands-on guide to designing, building, testing, and deploying secure stdio and SSE MCP servers in Python and TypeScript (447 manuscript pages).

A hands-on guide for engineers and architects to build, optimize, and deploy scalable production-grade AI systems and LLM infrastructure.

You've set up your agent and taught it your name. With this book you teach it your patterns, your triggers, and the version of yourself you're working towards. 24 chapters. All prompts, no code.

Every sentence an AI writes can carry a hidden signature, and the world's biggest models are already signing their output. AI Watermarking shows you exactly how it works, using a four-word vocabulary and arithmetic you can check with a pocket calculator. By the end you will know what a watermark can prove, what destroys it in seconds, and why short text can never be tested.

Security is no longer something you add after a system is built. It must be designed from the beginning.Security by Design explores how modern digital systems can be built to reduce risk, limit unauthorized access, protect sensitive data, detect threats, contain failures, and recover from attacks.From secure architecture and cybersecurity foundations to privacy, cloud security, DevSecOps, AI security, and digital resilience, this book provides a practical foundation for building systems that remain secure in an increasingly connected and AI-powered world.

Build MCP servers from scratch — from protocol to production deployment. Learn Tools, Resources, Prompts, HTTP transport, authentication, testing, and server composition. 14 chapters, one real project, complete TypeScript code.

AI made creation cheaper. It also made judgment more valuable.Taste: Turning Vibe into Assets in the AI Age shows why the next wave of winners will not simply be the people with the best credentials or the biggest teams, but the ones who can turn instinct, clarity, and initiative into real assets.

Short Description of the BookSecuring Modern AI Applications is a practical guide to securing LLM-powered applications, RAG systems, AI agents, and modern AI development environments.The book explains AI-specific threats such as prompt injection, data leakage, RAG poisoning, insecure tool use, agent abuse, and AI supply-chain risks while also covering traditional application security, privacy, governance, secure development, monitoring, testing, and red teaming.Designed for developers, AI engineers, cybersecurity professionals, architects, and technology leaders, the book focuses on practical principles that remain useful as AI technologies continue to evolve.Build AI. Secure AI. Govern AI.

An LLM is not an AI system.Systems Thinking for Agentic AI shows software engineers and architects how to design reliable AI applications with prompts, RAG, tools, memory, orchestration, guardrails, evaluation, observability, and runtime control.Move beyond chatbot demos and learn how to build production-ready agentic AI systems you can reason about, measure, debug, operate, and improve

AI can write code faster than you can read it—so why do projects still derail? The answer is the spec. This book teaches Spec-Driven Development hands-on, building a complete app one loop at a time with Spec Kit. Stop prompting and praying. Start shipping software AI actually gets right.

Build a "Second Brain" for your apps by mastering SwiftData, CloudKit synchronization, and persistent AI memory. Implement hardware-accelerated vector search and HNSW indexing for lightning-fast retrieval of semantic embeddings. Architect thread-safe, GDPR-compliant data layers using Swift 6 actors and advanced privacy-preserving strategies. Scale from local prototypes to production-grade distributed intelligence with definitive Apple ecosystem blueprints

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.