Books

Научитесь думать как ИИ через работающую LLM, которую вы построите сами. Проверенное в бою системное мышление из Cray Research, применённое к современному ИИ. Совершайте то, что другие считают невозможным.

Master high-performance C# to build the next generation of AI-powered applications. Go beyond basic async/await to architect non-blocking, massively concurrent AI pipelines. Implement real-time LLM streaming with IAsyncEnumerable<T> and manage backpressure gracefully. Leverage Channels and parallel processing to build a resilient RAG ingestion engine.

Digital Transformation in the Era of AI reframes transformation as a continuous capability, not a one-time program. Drawing on real enterprise experience, this book shows how organisations can evolve their architectures, operating models, and leadership practices to harness AI responsibly and at scale, without breaking what already works.

What if Python could take care of the repetitive work for you?This book shows you how to use Python to automate real tasks—from handling files and simple workflows to reducing everyday manual effort. Through clear explanations and practical projects, you’ll learn how to turn small scripts into useful tools you can apply immediately.If you’re ready to move beyond examples and start building automation that actually saves time, this book is for you.

Ditch slow, expensive AI APIs and bring the power of Large Language Models directly to your users' browsers. This guide teaches JavaScript and TypeScript developers how to build private, offline-capable, and blazing-fast AI applications. Master the local-first AI stack with Transformers.js, WebGPU, and Ollama to slash costs and own your data. Become a leader in the new era of serverless AI and deliver an instantaneous user experience

AI engines are booming, and the more we work with agentic systems, the more we see that we need something to make them work at the enterprise level. We're quite active in exploring ideas around context graphs, decision traces, and supporting explainability—giving agents the ability to make more aware and company-aligned decisions.But this makes sense not only for enterprises, but for users and individuals building personal agents as well. Unfortunately, we have zero-to-none inclination on how to actually build a context graph.I'll try to explain how to build something like a context graph—but go beyond it. I deeply believe that to make this work, we need specific agentic memory and a set of cognitive processes that truly help agents use this memory and learn from experience and data.That's why this is the Book: Beyond Context Graphs—with a focus on real-life enterprise tasks and how to make agents make better decisions and, let's say, hallucinate less.

Multiple-choice test book, 200 professional quality questions.

Unlock the power of Cyclical AI and move beyond linear chains with LangGraph.js. This deep-dive guide teaches TypeScript developers to build autonomous agents that can reason, loop, self-correct, and collaborate. Master the architecture of stateful, multi-agent systems, from advanced state management to production deployment. Stop building scripts and start engineering the intelligent, resilient systems of the future.

Are you a JavaScript developer ready to build the next generation of intelligent web applications? This book series is your hands-on guide to creating production-grade Retrieval-Augmented Generation (RAG) applications using TypeScript, Node.js, and Next.js. Move beyond abstract theory and master the entire AI pipeline, from your first LLM API call to deploying scalable, trustworthy systems. Stop just using AI tools—start building them and become a full-stack AI developer.

The "It Works on My Machine" Excuse Ends Today. Understand Docker. Ship with Confidence. Own Production. This book is written for front-end developers who are tired of using Docker without truly understanding it. Instead of abstract theory or backend-centric explanations, it teaches Docker in a clear, practical way—aligned with how front-end engineers think and how real front-end applications are built and shipped.You’ll work with a real GitHub repository, follow real production workflows, and learn how to build, run, and deploy a React.js application using Docker—ending with a full deployment to Amazon EC2. Every step is explained with purpose, so you understand not just what to do, but why it works.By the end of the book, Docker will no longer feel like a black box. You’ll know how to create reliable builds, debug problems with confidence, and take ownership of your production environment as a front-end developer.If you want to stop guessing, stop copying commands, and finally ship front-end applications with confidence—this book is for you.

LLMs und Agentic AI sind derzeit Hype. Richtig angewendet können sie extreme positive Effekte bringen. Dieses Buch zeigt, vor welchen Herausforderungen man in großen Unternehmen und speziell auch in regulierten Umfeldern bei der Einführung stehen wird. Das Buch ist eine leicht lesbare Einführung für IT-nahe Führungskräfte und Enterprise Architekten und auch nützlich für IT-Profis allgemein, die in das Thema einsteigen möchten und absehbare Projektfehler vermeiden möchten

Stop Reading 3,000-Page Manuals. Start Seeing the Architecture.

Machine learning doesn’t fail in theory—it fails in production. This book shows you how to build PyTorch systems that remain robust when data shifts, assumptions break, and reliability matters.

What could a mighty billion-parameter reasoning machine learn from a camel trying to touch its ear with its tongue? From a cup of coffee? From deleting your entire codebase while you sleep? More than you’d think. And less than you’d hope. Today’s AI is brilliant structure without grounding—a hollow genius. We chase smarter models but ignore the architecture they need. This book is about building that missing layer: the trust chains and systems that turn raw intelligence into reliable autonomy. For builders ready to move beyond prompts.Watch agents solve unsolvable problems. Learn to think in trust chains. Start here.

In this book, we will see how we can connect a Local AI (local LLM) using Python to do whatever you want; in the book, we will create a chat.