Books

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).

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.

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.

What if your software did not need the cloud to work? Local First Software Development shows you how to build fast, reliable apps that work offline and sync when needed. Learn practical patterns for data, security and synchronization while exploring how local-first systems can grow from simple personal tools into large collaborative applications.

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.

Mastering NVIDIA CUDA: Expert GPU Programming About the BookTo master NVIDIA CUDA at an expert level, you must discard the outdated notion of the GPU as a mere "parallel co-processor" and instead view it as a highly integrated, self-orchestrating compute fabric.

Go beyond writing code and learn what it takes to build software that lasts. Fundamentals of Software Engineering walks you through requirements, architecture, testing, deployment and production using practical trade-offs and a real-world case study. Follow TaskFlow from its first requirement to a system built to scale, evolve and survive the real world.

Platform engineering is changing fast, and AI is raising the stakes. This practical guide shows you how to build internal developer platforms that work in the real world, from cloud-native foundations to AI-powered workloads. Follow hands-on examples and a production-grade reference architecture to create platforms that are secure, scalable and built to evolve.

Running NetBSD in production is one thing. Running it under serious traffic is another. NetBSD at Scale shows how to design, secure, tune and operate infrastructure built for real workloads. From architecture and automation to high availability and disaster recovery, it is a practical guide for engineers who need systems that keep working.