Software Architecture

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.

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.

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.

You know how to build frontend applications. But can you explain how you would design one when the requirements are vague, the constraints keep changing, and you only have 45 minutes?Frontend System Design Interviews gives you a practical framework for structuring that conversation, making trade-offs, and going deep on the problems that actually change the architecture.

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.

What does it take to build distributed systems that keep working when things go wrong? Engineering Distributed Systems on the BEAM goes beyond syntax to show how Erlang/OTP turns concurrency, failure and recovery into practical design tools. Build a real system from the ground up, then learn how to scale, deploy and operate it with confidence.

What really happens when you open a website, send a message or connect to a server across the world? This book builds the Internet from first principles, revealing how bits become packets, packets cross networks and protocols make it all work. Learn the machinery behind the Internet by understanding why each piece exists and how it fits together.