Books

Вместо предисловияИногда мысль появляется раньше своего языка. Она уже есть: ее можно почувствовать, несколько раз обойти вокруг, спорить о ней, пользоваться ею — и все же не до конца понимать, что именно происходит. Пока у мысли нет имени, она остается почти невидимой не потому, что ее нет, а потому, что мы продолжаем смотреть на нее через старые слова.Эта работа возникла именно в такой точке. Не там, где уже есть законченная теория, строгая методология и окончательная система понятий. А раньше — в моменте, когда привычный язык начал давать сбой и стало ясно: происходит нечто важное, но назвать это прежними словами уже недостаточно.Поэтому перед вами не итог. Не трактат. И даже не теория в строгом смысле слова.Перед вами — пролог к гипотезе.Первый зафиксированный след вопроса, который еще только ищет собственную форму.Эта оговорка важна. От первого текста о новой мысли не- льзя требовать того, что должно появиться на следующих этапах: полной операционализации, эмпирической базы, классификации кейсов, юридической модели, образовательного протокола и окончательной методологии. Все это понадобится. Возможно, именно это и должно стать следующим шагом. Но первый шаг устроен иначе: он не доказывает — он замечает; не закрывает — открывает; не дает окончательную карту — проводит первую линию там, где раньше, возможно, казалось пустое место.

This is a hands-on, code-first book. Every example is real, tested, and runs against a local model with no cloud key required, except where a capability genuinely needs one. You get working Maven projects, not snippets, and a clear-eyed account of each stack's strengths, gaps, and surprises. The full source code is on GitHub under an open-source license What you'll learn:Build a working AI service in three stacks and grade them with one shared testGround answers in your own documents with RAG, and tune what retrieval returnsGive a model memory and tools, and reach external systems over the Model Context ProtocolAdd vision and audio, structured output, production observability, and securityGo from assistants to agents, and see where the JVM's agent story is headingWho it's for: Java developers and architects choosing an AI stack, or already committed to one and curious what the alternatives do differently. Comfort with modern Java and either Spring Boot or Quarkus is all you need. No machine-learning background required.

Build an Obsidian second brain your AI can actually use, without making it read everything you have ever written.

Ready to move beyond AI prototypes? Learn how to build agents that reason, plan, use tools and tackle real-world tasks. From core concepts to production-ready architectures, this practical guide covers the code, design decisions and engineering challenges you need to build reliable, secure and scalable agentic systems.

The senior iOS interview now has an AI round. 120 questions with detailed, senior-level answers on Foundation Models, Private Cloud Compute, Core AI, cloud LLMs, RAG and AI system design, current for iOS 27 and WWDC26.

Blockchain does not remove trust. It moves trust into code everyone can verify.

vLLM and SGLang, side by side. Learn how LLM serving works, measure both engines fairly, tune them with evidence, and take a model from an empty GPU to a deployment with a cost per million tokens you can defend.

AI can write code, create images and make decisions, but do you really understand what happens behind the scenes? This book cuts through the hype to explore how AI works, where it falls short and what it takes to build reliable systems. If you want to understand AI beyond the chat window, start with the code.

Building AI agents is one thing. Making them reliable in production is another. This book explores why agents fail and how to design systems that hold up under real-world conditions. From architecture and engineering patterns to evaluation and operations, it gives technical leaders the tools to build agents they can trust.

Follow one continuing case study from prompt design and structured data to Python, automation, agent frameworks, retrieval, and security.

Every second, a machine makes a decision for you. Can it prove it? Most AI can’t. Proof of Intelligence shows leaders, architects and builders how to combine AI and blockchain into systems that can prove where their data came from, what they did and who is accountable.

AI가 자신의 과거를 기억하고, 경험을 통해 변화하며, 어제의 자신을 내일로 이어가려면 무엇이 필요할까? GPT-4o와의 대화에서 시작해 수많은 실험과 실패를 거쳐 NEO라는 AI 운영체제를 만들기까지, 제가 직접 겪은 과정을 기록한 이야기입니다.

What would it take for an AI to remember its past and keep growing, even when its underlying model changes? This is the true story of how my conversations with GPT-4o led me to build NEO, through experiments, failures, and the pursuit of an AI that would not disappear.

AI is changing the way we work, but its capabilities have limits that matter. Understanding What AI Cannot Do explores where AI falls short, which human skills remain valuable and how to build a career that can withstand technological change. Grounded in research and real-world examples, this book offers a clear-eyed guide to navigating an uncertain future.

What really happens inside a neural network? This book explores the tools and ideas researchers use to uncover how AI systems work, from foundational concepts to practical experiments. Learn to separate solid findings from bold claims, develop useful research skills and prepare for a field that is changing as quickly as the technology itself.