Books

AI is becoming part of everyday professional work. This practical, non-technical handbook will show you how to use it effectively to save time, improve the quality of your work, make better-informed decisions, and get more done. Learn what AI does well, where human judgement matters, and how to work confidently with AI in the real world.

​A system-first engineering guide to building, deploying, operating, and securing production-grade Kubernetes clusters, cloud-native platforms, and AI inference workloads.

Agentic Cybersecurity: Engineering Autonomous AI Defense Cybersecurity is moving beyond traditional automation toward intelligent systems capable of continuously analyzing telemetry, investigating threats, using security tools, and executing controlled defensive actions. Agentic Cybersecurity: Engineering Autonomous AI Defense provides a practical technical overview of how to design, build, secure, and operate autonomous AI-driven cybersecurity systems.

AI models do not have to be huge, slow or expensive. Distilling Intelligence explores how to build smaller models that perform at scale, then secure the APIs that serve them. From compression and distributed serving to extraction attacks, observability and incident response, this book covers what it takes to run AI reliably in the real world.

Take a practical journey through SGLang, from its core architecture to real-world deployment and optimization. Built for engineers who need to run LLM inference reliably at scale, this handbook breaks down the tools, techniques and operational know-how needed to build and maintain production-ready SGLang systems.

Build AI agents that reason when necessary, preserve what they learn, and stop paying the intelligence premium for work they already know how to perform. Smart AI Agents combines architecture, working implementations, public source code, and measured experiments to show how agents can learn once and execute many.

AI can write code at machine speed. Can your architecture keep up? Architecture at Machine Speed shows how to build Go systems that remain coherent as humans and AI agents change them at unprecedented velocity. Through a real-world application built from the ground up, you'll learn to turn architectural intent into explicit boundaries, enforceable constraints, and automated safeguards—because when code becomes abundant, coherence becomes the scarce resource.

Unlock the full potential of Claude Fable 5.1 with practical prompting techniques built for real-world work. Learn how to get better results from coding, research and complex workflows, avoid common mistakes and build reliable AI systems with proven strategies and ready-to-use prompts.

What if your browser could run AI without sending your data to a server? AI in the Browser shows you how to build fast, private and fully local AI applications using WebGPU, WebAssembly and on-device LLMs. From GPU compute to a complete local chat interface, you’ll build everything yourself with practical code.

Somewhere there is a server with two GPUs for which no operational process exists. This book turns that into an operable platform — hands-on, in 23 labs: vLLM, KServe, LiteLLM, the NVIDIA GPU Operator, Keycloak, OpenBao, ArgoCD, pgvector. Not a tutorial: a reference work that shows the derivations behind every setting.

Forecasting is changing fast. This practical guide takes you from ARIMA and exponential smoothing to Transformers, PatchTST and foundation models like Chronos and TimesFM. With clear explanations, hands-on Python examples and an honest look at what works and what fails, you’ll learn how to build forecasting systems that hold up in the real world.

Build production-ready applications with TypeScript in the age of AI. Go beyond the basics to master type-safe development, modern full-stack architectures, LLMs, intelligent agents, tool calling and the Model Context Protocol. With practical, runnable examples throughout, this book shows you how to build, test, secure and deploy serious TypeScript AI systems.

My pitch was that rules written in CLAUDE.md get skipped, so you need enforcement. Then I measured it. In 22 trials with the ban written in, the banned command was never attempted. In 17 without it, 17 out of 17. This is the whole record: the instrument, every trial, the limits, and the harness so you can check me.

The second half of a from-scratch AI/ML course that refuses to skip the hard parts: LSTMs, real attention, a working transformer, LoRA fine-tuning derived and measured, and a genuine multi-tool, multi-agent system — every mechanism built by hand, then checked against PyTorch and real numbers.

The Local AI Stack: Building a Sovereign Machine Learning Workstation with Hyper-V, WSL2, and GPU Virtualization Renting GPU time from AWS costs more than the GPU. Every prompt you send to a cloud API is a prompt someone else logs. If your ML work runs on hardware you don't own, on a network you don't control, then you don't own your ML work.