Books

AI doesn’t fail because it isn’t smart. It fails because it isn’t usable. This book defines the foundations of designing AI agents people trust, understand, and rely on.

Master the art of AI interaction with 22 proven prompting techniques, real code examples, and production-tested strategies. From the creator of GitHub's most-starred prompt engineering repository (7,100+ stars).

Stop guessing which machine learning algorithm to use. This book provides clear mathematical explanations, decision frameworks, and real-world examples to help you select, implement, and evaluate models correctly from data to deployment.

Architecture. Teams. Business. Aligned. Finally, a guide that connects the dots. From Team Topologies to Flexible Project Management, learn how to build systems that align with your business goals instead of fighting against them. Stop estimating and start engineering your path to confidence.

Digitale Transformation war das Hype-Thema vor KI. Es gibt allerdings einen nicht geringen Anteil an Fach- und Führungskräften, deren Wissen darüber weiter ausbaufähig ist. Dieses Buch ist eine Einführung für alle diejenigen, die keine "digitalen Pioniere" sind, sondern wissen wollen, was aus dem Thema Digitalisierung auf sie zukommen kann und aus dem Thema KI zusätzlich auf sie zukommen wird.

Bridge the gap between high-level C# and raw hardware execution. Eliminate GC pauses with zero-allocation Span<T> and SIMD vectorization. Learn to build blazing-fast tokenizers and local LLM inference engines. Stop waiting for the runtime—start engineering for extreme AI performance.

Break free from expensive cloud APIs to build private, offline-capable AI systems. Harness ONNX Runtime and LlamaSharp to run LLMs and Vision models natively in C#. Master hardware acceleration and local RAG pipelines for lightning-fast, zero-latency performance. Stop paying per token—start engineering high-performance intelligence directly on the Edge.

Stop scripting and start engineering the next generation of cloud-native AI. Master C# and Kubernetes to build resilient, distributed agent swarms at scale. Bridge the gap between experimental logic and high-performance, production-grade systems. Architect the future of intelligence—from event-driven scaling to secure service meshes.

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.