Software Architecture

AI agents are only as good as the loops behind them. This book shows you how to build systems that plan, act, evaluate and improve with every step. Learn the patterns, frameworks and engineering practices behind reliable, self-correcting agents that solve real problems in production.

The CCAR-P exam tests judgment, not trivia. This scenario-based guide covers all seven domains, from solution design and RAG to governance and stakeholder communication, with 62 sketchnote figures, chapter quizzes, a distractor appendix that explains every wrong answer, and a full 63-question practice exam at the official weighting.

Learn the basic principles that govern how distributed systems work and how you can design your systems for increased performance, availability and scalability. Understand the basic algorithms and protocols used to solve the most common problems in the space of distributed systems.

A practical guide to CPU affinity, NUMA, and thread placement for Java engineers who need nanosecond-precise, deterministic latency. Every example compiles against the open-source faster-hft-affinity library and is enforced by its test suite.

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

본 책은 단순히 “코틀린을 어떻게 사용하는가”를 넘어, “코틀린이 실제로 어떻게 동작하는가”까지 안내합니다. 내부 구현을 들여다보고, 바이트코드와 컴파일러의 동작을 명확히 탐구하며, 언어를 형성하는 핵심 내부 구조를 파헤칩니다. 기초 문법부터 코루틴, 컴파일러에 이르기까지 더 깊이 이해하고, 더 자신 있게 코틀린 코드를 작성하고 싶은 분들께는 좋은 지침서가 될 것입니다. 특히, 우리가 일상에서 친숙하게 사용하던 모든 언어적인 형태가 왜 지금과 같은 형태로 설계되었는지를 탐구하며 사고의 깊이를 더할 수 있습니다. Hardcover | Paperback | ISBN: 979-8243872744 [Course] Practical Kotlin Deep Dive Course

Happy-path architecture is a myth. When downstream dependencies degrade, naive retries trigger thundering herds, thread pools exhaust in seconds, and dual-writes silently corrupt financial balances. Surviving Distributed Systems is an unvarnished, production-tested field guide for senior backend engineers. Packed with architectural diagrams and modern Java 21 code, this book teaches you how to design, isolate, and harden high-concurrency systems so they survive catastrophic cascading failures.

Lean Enterprise Architecture (LEA) redefines EA as a lean, value-driven discipline that bridges strategy, business, and technology. Instead of static blueprints, it shows you how to build adaptive architectures, enable transformation, and create measurable impact. Whether you are an enterprise architect, IT strategist, or business leader, LEA equips you to lead with clarity and influence.

Residuality Theory is a new way to think about the design of software systems that explains why we experience design the way we do, why certain things seem to work only sporadically, and why certain architects get it right so often regardless of which tools they use. A new, scientific approach is defined that fuses Software Engineering, Complexity Science, and Philosophy to produce an entirely new way to think about how to design software. The result is a theoretical base that allows architecture to finally become its own discipline.

This book introduces LASR – the Lightweight Approach for Software Reviews in a practical and in-depth way. It provides detailed insights into how the method works, real-world examples, guidance on using support materials, and practical tips for your own reviews.

The most important impact of AI on software development isn't that it writes code faster — it's that it changes what you can delegate. Agentic Programming is a practical roadmap for climbing the AI Fluency Ladder: from prompting to agentic workflows, verified execution, and ultimately autonomous software development.

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.

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.

The engineer's guide to RAG systems that survive a deploy.

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.