Books

Concurrency is where small mistakes become serious problems. This practical guide takes you from hardware and memory models to locks, atomics and lock-free algorithms, with a focus on building fast, reliable systems that hold up under real production workloads.

Stop choosing between an AI that's powerful and one you can certify. From Shannon's entropy to a reproducible, auditable Industry Language Model — built in C#, wrapped in Deterministic Islands, with every number independently verified.

Forget IShape — it's time to build software that actually does something. OrderFlow takes ten classic Gang of Four design patterns and weaves them into a single, fully functional food delivery platform built with C# and .NET 10. Clone the repository, step through the production-grade code, and finally master not just how to write patterns, but when to use them (and when to keep it simple).

When code gets cheap, understanding gets expensive. The Code Is Lying follows a team of engineers as coding agents move the bottleneck from writing software to reviewing, verifying, and trusting it. Every chapter ends with field notes on the habits and checks that keep the practice honest.

Quantum mechanics, the nucleus, and quantum computing — from what you already know as an engineer to a quantum computer you build in C#. Full rigour, honest limits, no hype. For the engineer who will model them.

Jellyfin makes self-hosting your media library look easy, but getting it right takes more than installing a server. This practical guide covers architecture, deployment, administration and optimization, helping you build a reliable setup that works smoothly for local and remote users alike.

Build a complete TypeScript MCP App with a portable text fallback, accessible React dashboard, server-validated action, and tests at every boundary. Includes the tested companion project.

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.

AI is changing fast, and so are the security risks that come with it. This practical guide shows security and technology leaders how to govern, secure and assure AI systems from design through deployment and beyond. Packed with proven frameworks, controls and real-world guidance, it turns complex AI security requirements into practical action.

Good software is not about following rules. It is about knowing when they apply. This book explores the judgment behind building software that lasts, from choosing simplicity over cleverness to balancing today’s needs with tomorrow’s costs. Practical, thoughtful and focused on decisions, not dogma.

What if one physical server could run several independent Linux kernels at the same time? Multikernel Linux explores the technologies behind it, from virtualization and bare-metal partitioning to CPU, memory, devices, networking and storage. It moves from core concepts to practical production setups, with a focus on what really works and why.

Why is your system slow, and how do you know what to fix? This practical guide takes you from CPU and memory behavior to databases, networks and distributed systems. Learn how to profile, benchmark and diagnose real performance problems with working examples across modern languages and tools.

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

A model that crashes is a good day. The dangerous failures return answers — plausible, fluent, and wrong. 100 failures. 63 diagnostic instruments. One rule: inspect what actually happened.

Cloud GPU rental costs more than compute time. It can also introduce recurring expenses, external dependencies, and additional questions about where your prompts, datasets, and model artifacts are processed. A local machine learning workstation gives you direct control over the hardware, software stack, and data workflows. The Local AI Stack is a technical engineering reference for building a Windows-based machine learning workstation with Linux environments, NVIDIA GPU acceleration, and virtualization. Across 140 pages, it follows the stack from firmware configuration and Windows virtualization features to CUDA-enabled containers, model inference, storage optimization, and system recovery.