Books

Not another AI coding tutorial. A DX engineering book on using Claude Code to reduce developer friction - backed by METR, DORA, and Faros AI data. Processes first, then tools. 80% deterministic, 20% AI.

Understanding the most common mistakes in machine learning will allow you not only to avoid them, but to build better machine learning systems and less prone to errors. After reading this book, you will be ready to build more robust and trustworthy machine learning models.

"Data Science Project: An Inductive Learning Approach" provides a comprehensive methodology for data science project development, emphasizing software engineering principles essential for reliable solutions. Dr. Filipe Verri, a senior data science project manager, guides readers through the origins, scope, and key concepts of data science. This book covers machine learning, data handling, and rigorous validation techniques, all essential for preparing readers to tackle complex, real-world projects.

Essential Python libraries and frameworks that every aspiring data scientist, ML engineer, and Python developer should know.

A fun dive into AI programming with Python.

All examples in Hy. The Hy language (Lisp that compiles to Python) allows Lisp programmers access to the rich Python ecosystem for Large Language Models, deep learning, artificial intelligence, and general data wrangling. Applications: LangChain, Knowledge Graphs, NLP, Deep Learning.

Stay informed about the technology that matters.IT & AI Digest brings together concise analysis and insights on artificial intelligence, software, cybersecurity, cloud computing, robotics, digital business, and emerging technologies.No endless scrolling. No unnecessary noise.Just important ideas, explained clearly.Read less. Understand more.

What if agentic AI was less about hype and more about work you can actually inspect?OpenClaw and Hermes for Agentic AI is a practical field guide to building with local agents, memory, tools, sessions, cron jobs, and ACP bridges. It shows how to turn fuzzy ideas into reliable workflows with clear boundaries, real artifacts, and human review where it matters most.If you want a grounded look at how agent systems really operate, this book starts there.

Move beyond chatbots. Learn how to build autonomous AI agents that execute real business workflows 24/7 and scale operations at a fraction of traditional costs.

AI is reshaping industries and products. This book helps Product Managers understand how to use AI tools to plan, design, and deliver great products. It explains the AI stack and strategic frameworks for integrating AI features into products; how to use GenAI to perform customer discovery, market research, and prototypings; and how the Product Development Life Cycle and the Model Development Life Cycle intersect when creating AI-driven products.

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.

Running LLMs with your own data allows you to quickly build personalized applications.

Zefs Guide to Deep Learning is a short guide to the most important concepts in deep learning, the technique at the center of the current artificial intelligence revolution. It will give you a strong understanding of the core ideas and most important methods and applications. All in around only 150 pages!

An introduction to Common Lisp and many useful example programs. Use LLMs, as well as classic symbolic AI techniques.

From KNN to LLMs: build real AI applications in TypeScript — no GPU required. Implement machine learning from scratch, train neural networks with TensorFlow.js, integrate Google Gemini and OpenAI APIs, run local models with Ollama, build a chess engine with alpha-beta search, and query the world's knowledge graphs with SPARQL — all from the command line.