Books

You don't need a graph database. You need graph thinking inside DuckDB. GraphDuck takes you from SQL adjacency lists to metagraphs, hypergraphs, and hybrid Graph RAG pipelines — all inside DuckDB. Learn to model knowledge graphs, build AI agent memory systems, run graph algorithms, and combine vector search with graph traversal in a single embedded database. Every concept comes with runnable code. No infrastructure required.

A ship physician who practices 1,000 miles from shore and trains medical AI systems explains what every clinician needs to know about the technology that is quietly reshaping their practice.

For those who want to build controlled, reproducible AI systems entirely within their own infrastructure, this book is the most practical and implementation-focused trainer. Instead of relying on external APIs or cloud-hosted intelligence services, this book clearly demonstrates how Apache Spark can orchestrate data preparation, model training, batch inference, reporting, and LLM acceleration in a disciplined and transparent way.

Have you ever been curious about how your phone unlocks when it sees your face, how a camera can track people and objects in a video, how humans see depth, or how computers can differentiate dogs from cats? This book will start from the basics of image manipulation and build up to cover all of these topics, and more!

A language model predicts tokens. An agent acts—and must be held accountable. Learn to replace “prompt and pray” with governed tools, geometric memory, independent verification and auditable runtime controls.

Discover the power of thinking in logic. This hands-on guide takes you from your first Prolog query to advanced techniques in constraints, grammars, AI search, meta-programming and production development. With clear explanations and executable SWI-Prolog examples, you’ll learn not just how Prolog works, but how to use it well.

The definitive guide to Windsurf — the AI IDE with both Cascade (interactive agent) and Devin (autonomous agent). Covers Supercomplete, Flows, .windsurfrules, large codebases, testing, Git, and the honest comparison with Cursor and Claude Code. 14 chapters.

This is a fast-paced, practical, and visual guide to the strange new world of Generative AI. It is like an extended version of Henrik's viral video with the same name. Course version: The book is also available as a course on Leanpub. If your company wants to pay for you to take an AI course, now you can :) Print version: Paperback & Hardcover are available on Amazon. Use your own country's amazon site (ex: Amazon.se for Sweden) to minimize shipping time and cost.

Anyone can run INT4 and read off the accuracy drop. This book explains why that number is what it is — building every quantization method from scratch, breaking it on purpose, and measuring the result. Quantization, from the bits up.

Code Is the Side Effect"Software engineers are not primarily code writers. We are clarity traders — and that hasn't changed."You've seen the demos. The AI builds a whole feature from a sentence. The agent writes tests, fixes the failing ones, opens the PR. It's remarkable.Then you come back three months later. The codebase is a tangle. Nobody knows why anything is the way it is. The agent that built it has no memory of what it decided or why. And every time you ask it to add something new, it breaks two things you didn't know were connected.This is the pattern that nobody talks about. AI coding tools make the easy parts of engineering dramatically easier. They leave the hard parts untouched — and they create new hard parts that didn't exist before.Ways of Working is the book for engineers who want to work with AI agents rather than be gradually replaced by them — who understand that the tools are genuinely powerful and genuinely limited, and want to build practices that get the most from each.What you will actually learnThe world model framework. Before an agent can build anything well, it needs to understand what it's building and why. This book teaches you to give agents what they need: a structured, queryable representation of your architecture, your component contracts, your behavior specifications, and your code patterns. No world model = no sustained agentic development.Intent documentation. The most expensive bug in agentic codebases is not a hallucination — it's a decision made without context. Why is this rule here? Why is this boundary where it is? Agents can't infer rationale from code. You have to write it down.Spec-Kit and formal specifications. GitHub's Spec-Kit brings machine-readable, traceable, CI-verified specifications to engineering teams. This book shows how to use it to turn requirements into agent inputs that are precise enough to generate correct implementations.Graph explainers. Tools like Graphify and Understand-Anything transform codebases and documents into queryable knowledge graphs — giving agents navigable context instead of flat text. This is the memory substrate that makes multi-agent systems reliable at scale.Agent architecture that holds. What makes an agent coherently itself? When do file-based agent systems break down and what replaces them? How does constraint-based coordination (borrowed from holocracy) solve the autonomy-coherence problem that has stumped AI researchers for decades?Claude Code, for real. A complete treatment of Claude Code's CLAUDE.md convention, permission model, hooks, and slash commands. Plus the oh-my-claudecode ecosystem: 15+ specialized agents, workflow orchestration patterns (autopilot, ralph, ultrawork), and the skills framework for team-specific automation.The AI-native organization. What genuine AI-native teams look like beneath the marketing. How to hire, structure, and lead them. What language-oriented programming and constrained natural language mean for the future of the human-code relationship.Who it's forEngineers who are past the "should I use AI?" question and into the "how do I use it without losing my engineering integrity?" question.Senior engineers. Engineering managers. Technical leaders. People who have noticed that the more they delegate to AI, the less certain they feel — and who want to understand why.From the AuthorI've been building production systems with AI agents for years. Not demos — systems that had to work reliably across months, maintain themselves as requirements changed, and produce outputs that engineers could understand and defend.That experience has made me skeptical in both directions.Skeptical of the "AI will do everything" vision — because I've watched too many AI-generated codebases collapse under the weight of accumulated misunderstanding.Equally skeptical of the "nothing fundamentally changed" position — because the engineers who treat AI coding tools as just faster autocomplete are making a category error they'll pay for in months of maintenance debt.Something genuinely new is happening. This book is my attempt to think about it clearly.

Stop building static interfaces for dynamic AI and master the art of reactive, intelligent design.Leverage Swift 6 concurrency and @Observable to handle real-time token streaming and async outputs.From SwiftData persistence to animated visualizations, learn to architect production-ready Apple apps.The ultimate guide to building fluid, professional user experiences powered by modern AI models.

Network traffic can look like noise until you know what to look for. This practical guide shows you how to investigate unfamiliar protocols, analyze packet captures and build reliable workflows with Claude Code. Learn to reverse engineer traffic, test your assumptions and turn raw network data into findings you can trust.

AI governance is moving from principle to practice. This hands-on guide shows you how to build, implement and certify an ISO/IEC 42001 AI management system with confidence. From clause-by-clause guidance to practical templates, integration strategies and real-world scenarios, it turns a complex standard into a clear path from foundation to certification.

The loop writes code and clears context. The commit log records MAX_RETRIES = 3, but the reason it's three vanished on Friday night. The artifact survived; the decision evaporated.The Loop That Remembers inverts the premise: memory isn't the sixth piece, it's what the loop exists to produce. Two graphs, a promoted lattice, bounded context, and an evaluator checking claims against edges.

Modern Deep Learning models can be extremely large, often exceeding the memory capacity of a single GPU or CPU. In these cases, training must be distributed across multiple processors. This introduces the need for high-speed communication between GPUs—both within a single server and across multiple servers. Intra-node GPU communication typically relies on high-speed interconnects like NVLink, with Direct Memory Access operations enabling efficient data transfers between GPUs. Inter-node communication, however, depends on the backend network, either  InfiniBand or Ethernet-based. Synchronization of model parameters across GPUs places strict requirements on the network: high throughput, ultra-low latency, and zero packet loss. Achieving this in an Ethernet fabric is challenging but possible.   This is where datacenter networking meets Deep Learning. Understanding how GPUs communicate and what the network must deliver is essential for designing effective AI data center infrastructures.