Books

A clear, illustrated guide to large language models, covering key concepts and practical applications. Ideal for projects, interviews, or personal learning.

Skip the black-box frameworks. Build a production-grade AI coding agent from scratch in pure Python - cloud or local, tested with pytest, all in a single file.

Vector stores don't think — they search. They find fragments that sound like your query, then forget they ever looked. Every session starts from nothing. Every context window is a memory that dissolves at sunset.But the deeper problem isn't amnesia. It's that when agents do remember, they remember in someone else's house — on servers you don't control, in formats you can't inspect, under terms you didn't write.Memory Graph is a book about building something different: persistent, structured, queryable memory that lives inside your application — no external servers, no data leaving your process, no infrastructure you don't own. An embedded graph database that travels with your agent the way a nervous system travels with a body.You'll learn how to model not just facts, but relationships between facts. Causality. Temporal ordering. The layered structure of meaning that makes memory more than a search index. You'll build ontologies that enforce what can be known and how. You'll combine graph traversal with semantic search — so your agents find not just what's similar, but what's connected.The result is an agent that remembers the way you do: structurally, contextually, privately — with memory that belongs to 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

Everything you really need to know in Machine Learning in a hundred pages.

Master language models through mathematics, illustrations, and code―and build your own from scratch!

You've set up your agent and taught it your name. With this book you teach it your patterns, your triggers, and the version of yourself you're working towards. 24 chapters. All prompts, no code.

Unlock the power of AI in your applications with this groundbreaking book on AI-driven application architecture. Discover practical patterns and principles for building intelligent, adaptive, and user-centric software systems that harness the potential of large language models and AI components.

It's never been easier to build an AI agent—and never been harder to make one that actually works. This book takes you from language model foundations to production-ready multi-agent systems, with the depth to understand what you're building and why it fails.

The practical guide to AI-first teamwork. Includes access to the 'CollabAI AI companion' that helps you run your first session immediately. Most teams have fast individuals—but a slow system. AI can change that. CollabAI is the manual for teams who want to stop waiting and start flowing. It moves beyond "chatting with a bot" to a new collaborative rhythm where humans and AI build, test, and decide together in real time. Inside, you’ll discover:The Framework: How to run CollabAI sessions that compress weeks of work into hours.The Science: Why "System 2 Rituals" and psychological safety are the hard requirements for speed.The Scale: How to apply Joe Justice’s principles (Justice Boards & DSM) to run entire organizations without managers.The Future: How to transition safely to Agentic AI workflows using the Agion Pattern.Start optimizing the flow.

How to represent knowledge for LLMs and build memory for agents, I discovered Mark's work on semantic spacetimes. It's more of a theoretical framework from someone who came from physics. But actually, jumping to knowledge representation and reasoning, and trying to answer the question of how to build dynamic and complex systems—semantic spacetimes and promise theories are crucial for the future of agentic systems, in my belief.The semantic spacetime approach gives us answers on how to organize better memory and how to have better knowledge representation that could be understood quite well by LLMs. Vector embeddings actually create a lot of challenges—some spaces and some relations in vector embeddings simply don't exist. We all have this problem where "love my wife " and "hate my wife" while actually quite distant in practice, and also time and dynamics matter

本書は、生成AIという新しい不思議な世界について、テンポが速く、実践的で、視覚的なガイドです。Henrikの同名のバイラル動画を拡張したような内容となっています。 印刷版：ペーパーバックとハードカバーはAmazonでご購入いただけます。配送時間と費用を抑えるため、お住まいの国のAmazonサイト（例：スウェーデンの場合はAmazon.se）をご利用ください。

Master AI-powered infrastructure automation with this hands-on guide to building production-ready MCP servers and AI agents in Go. Transform from manual AWS operations to intelligent automation that understands your environment and makes smart decisions while keeping humans in control.

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.

Build AI agents that truly remember, reason, and act—entirely on user devices. Move beyond prompt engineering to create autonomous systems with graph-based memory using SQLite and LibSQL. Learn to implement hypergraphs, metagraphs, and vector search for privacy-first AI that scales to millions of entities. From personal knowledge graphs to production mobile apps, master the three pillars of agent autonomy: tools, memory, and reasoning. Real code, working examples, battle-tested in production. The future of AI is local, private, and in your hands.