Artificial Intelligence

Using AI to code is easy. Using it without losing control — not so much.By week three, your project stops moving. The agent forgets decisions it made ten days ago. A change in auth breaks the dashboard. You spend more time re-explaining context than writing features. The code works, but only you know why — and you're not even sure you remember all of it.That's vibe coding hitting its ceiling.Spec-Driven Development is the method that replaces the chaos with a spec the AI actually executes. Not a ceremonial document. A working artifact: PRD, issues, tests, code — all traceable, all connected, all in the right order.This book shows you:How to grill your own idea before writing a single promptHow to write a PRD the AI won't misinterpretThe 7 phases that turn an idea into working softwareHow to use GitHub SpecKit and openSpec (and when not to)How to work this way in a team without slowing downThe 5 anti-patterns that destroy every spec22,000 words. 13 chapters. 5 appendices with ready-to-copy templates.No theory dumps. No filler. Just the method.

A beginner-friendly introduction to machine learning with Python, that is based on the PyCaret and Streamlit libraries. Readers will delve into the fascinating world of artificial intelligence, by easily training and deploying their ML models!

Dive into NLP, deep learning, knowledge representation, and semantic web technologies. All of my Leanpub books, including this book, can be read for FREE on my web site: https://markwatson.com/

Practical, safe AI for people who actually run networks.

Discover why current AI memory systems fail and how to build agents that truly remember. This groundbreaking exploration reveals semantic spacetime, causal graphs, and event-driven architectures that transform static retrieval into dynamic understanding. From temporal reasoning with Allen's algebra to hypergraphs and metagraphs, learn to create AI that doesn't just store facts but understands causality, context, and meaning. The future of AI agents depends on memory systems that mirror human cognition—forgetting strategically, reasoning causally, and adapting contextually. Essential reading for developers building the next generation of intelligent agents that genuinely comprehend human experience and decision-making patterns.

Hate calling yourself a "non-technical" founder or professional? Never again. The ultimate guide to thinking digitally and leading software companies.

Stop building chatbots and start architecting autonomous digital workers that act, plan, and collaborate. Master multi-agent orchestration with CrewAI and build self-healing, cyclic workflows using LangGraph. Move beyond simple prompts to implement the OODA loop, browser automation, and production-grade security. Transform LLMs into reasoning engines capable of managing entire software agencies without intervention.

Unlock the power of AI with "LLM Essentials: A Busy Professional's Guide to Large Language Models." This concise guide demystifies the world of LLMs, offering practical insights for business leaders and innovators. Discover how to leverage these powerful models to transform customer service, streamline content creation, and uncover hidden insights in your data. From ethical considerations to cutting-edge techniques like multimodal LLMs, this book covers it all. With step-by-step guides and real-world examples, you'll learn to implement LLMs in your business, even with limited resources. Don't let the AI revolution pass you by. "LLM Essentials" is your roadmap to harnessing the future of language technology. Get ready to lead your organization into a new era of AI-driven success.

How to develop an Artificial Intelligence application? This book includes Agility foundations, engineering practices, real examples, and suggestions for implementing them in your company. Foreword by Jeff Sutherland, co-author of Scrum and the Agile Manifesto.

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.

AI governance changes when AI stops merely producing answers and begins taking action. Runtime AI Governance provides the architecture, controls, evidence and assurance methods practitioners need to govern agentic AI while consequential actions are still observable, interruptible and accountable.

This book familiarizes readers with the world of LLM and agentic AI, and helps them quickly gain a working-level knowledge of building useful agentic AI solutions for process industry operations. With no prerequisites required, practical demo applications, and a hands-on approach adopted throughout, this book makes advanced AI technologies accessible to process engineers and data scientists alike. It aims to help process data scientists and engineers take their first confident steps into Agentic AI world, understand the full picture, and build a strong enough foundation to keep learning and building on their own. Also available here.

Most agent books teach prompts and frameworks. Agent Engineering teaches the judgment and engineering discipline required to build AI agents that are reliable, secure, testable, and ready for production. Follow a practical Claude-based project from first agent to production-grade autonomous system—and receive every future update to this early-access edition.

This book presents an architecture-first approach to designing trustworthy GenAI applications. Using Digital Forensics and Incident Response (DFIR) as a continuous case study, you will progressively build an AI-assisted investigation system. If you want to move beyond building AI applications that simply work, and start architecting AI systems that professionals can trust, this book is for you.