Software Architecture

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 agents are moving from experiments into real production systems. This book shows experienced engineers how to design, deploy and operate reliable agent systems that can scale. From architecture and infrastructure to security, reliability and governance, it provides practical patterns and production-ready examples for building autonomous systems that work in the real world.

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.

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.

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.

An Advanced Engineering Reference for GPU Architecture, Low-Level Optimization, and High-Performance ComputingModern GPU programming requires more than exposing parallel work to thousands of threads. It demands an understanding of the relationship between hardware architecture, instruction execution, memory movement, synchronization, and the software systems that coordinate computation across multiple GPUs. Mastering NVIDIA CUDA: Expert GPU Programming is a 100-chapter technical reference for experienced developers, high-performance computing engineers, researchers, and AI systems architects.

Building an LLM demo is easy. Running one reliably at scale is not. Production LLMOps shows you how to design, deploy and operate real-world LLM systems, covering RAG, agents, fine-tuning, evaluation, CI/CD, observability, security and more, with practical code you can adapt for production.

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.