Books

The missing manual for making your web applications future-proof

STOP building fragile AI wrappers. START designing resilient AI systems. Lots of companies are trying to make their small AI experiments into big products, but they don't have a good plan. Engineers need a practical guide to build these new AI systems the right way - so they can handle scale, be reliable, and won't cost too much. This book is that guide. It explains how to design systems that use AI models. This book breaks down the architecture of real AI applications, like an AI-powered code editor or a smart learning app. It gives you a deep, practical look at the real-world challenges and solutions for building these systems. It discusses system design concepts for systems that use LLMs.

“Happily purchased. Handy to have these in one place. Thank you!” — Kent Beck “Excellent new thinking on Domain-Driven Design. It's full of real practical experience in getting the most value from domain modelling. Just like the Eric Evans' DDD book, this gives more insight each time you read it.” — Ian Russell

This series of test-driven small coding puzzles lets you code a database from scratch (no dependencies).We'll cover KV storage engines, LSM-Tree indexes, SQL, concurrent transactions, ACID, etc.

Modern cloud-native applications generate massive volumes of telemetry data, making observability increasingly expensive and difficult to scale. While OpenTelemetry has become the industry standard for distributed tracing, traditional tail-based sampling architectures often require stateful collector clusters with significant memory and processing overhead. Rethinking OpenTelemetry Tail-Based Sampling explores how Event-Driven Architecture (EDA) and event mesh technologies can be used to address these challenges. Through practical examples, architectural analysis, and implementation considerations, this book examines OpenTelemetry sampling strategies and presents an alternative approach that externalizes trace buffering and sampling coordination beyond the collector layer. Whether you are an observability engineer, platform architect, integration specialist, SRE, or cloud-native practitioner, this book provides practical guidance for designing scalable, resilient, and cost-effective distributed tracing platforms.

An approachable, well-written, practice-oriented, academism-free guide into programming with types. How to build useful real-world type-level programs with low complexity and low risks.

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.

An LLM is not an AI system.Systems Thinking for Agentic AI shows software engineers and architects how to design reliable AI applications with prompts, RAG, tools, memory, orchestration, guardrails, evaluation, observability, and runtime control.Move beyond chatbot demos and learn how to build production-ready agentic AI systems you can reason about, measure, debug, operate, and improve

Keyword search misses meaning. Vector search misses precision. This book shows you how to combine them into production systems that deliver both, with architecture patterns, model selection frameworks, evaluation methodology, and operational guidance grounded in primary research.

Identity protocols are easy to underestimate. This book goes deep into SAML 2.0, OAuth 2.0 and OpenID Connect, showing how they work in practice, where they break and how to build and audit secure systems. With protocol traces, working code and real attack analysis, it is a hands-on guide for engineers working with modern identity.

After 1,000+ interviews, I kept seeing the same gaps: engineers who use shared_ptr daily but can't explain the control block; developers who write multithreaded code without understanding the memory model they're relying on. This book is the resource I wished existed. Free preview includes Chapter 3: OOP & Polymorphism — the chapter that eliminates more candidates than any other.

Build real-world software by coding a Redis server from scratch.Network programming. The next level of programming is programming for multiple machines. Think HTTP servers, RPCs, databases, distributed systems.Data structures. Redis is the best example of applying data structures to real-world problems. Why stop at theoretical, textbook-level knowledge when you can learn from production software?Low-level C. C was, is, and will be widely used for systems programming and infrastructure software. It’s a gateway to many low-level projects.From scratch. A quote from Richard Feynman: “What I cannot create, I do not understand”. You should test your learning with real-world projects!

Most enterprises can deliver fast, but few can adapt at scale.The Cybernetic Enterprise introduces a unifying operating model that embeds AI, feedback loops, and platform thinking into the DNA of your organization. Learn how to build a system that senses change, learns continuously, and transforms disruption into strategic advantage.

A scenario-based study guide for the Claude Certified Architect Foundations (CCAR-F) exam. Learn to reason like an architect across all five domains, then prove it on two full practice exams.

As AI models become increasingly accessible, the real challenge lies in making them work reliably in production. Advanced Harness Design explores the engineering behind robust AI systems, from architecture and orchestration to security and operations. Discover the patterns, tools and practical techniques needed to build dependable AI systems that perform in the real world.