Artificial Intelligence

Build reliable, production-ready search systems with embeddings and neural reranking. Learn how to turn semantic meaning into relevant results, refine retrieved content and deliver better context for AI-generated answers. With hands-on Python examples and practical architectures, this book takes you from core concepts to real-world RAG systems.

Turn a small language model into an AI agent that runs on your own hardware. This practical guide takes you from choosing a base model to fine-tuning, tool use, evaluation and deployment. With reproducible code and real configurations throughout, you'll learn how to build specialized local agents that are capable, efficient and truly yours.

Designing AI is only half the journey. Learn how to deploy, secure, govern, monitor, and scale Enterprise AI systems for production with practical architectures and proven operational strategies.

Build AI agents that do real work with the Google Agent Development Kit (ADK), Python, and Google Cloud. Through complete, runnable examples, you'll learn how to build, orchestrate, and deploy production-ready multi-agent systems with practical skills you can apply from day one.

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.

A practical guide to operating a fleet of AI coding agents through routing, memory, skills, MCP, guardrails, and a persistent control plane (286 manuscript pages).

What if your AI agents could learn from their mistakes instead of repeating them? Discover how to build systems that remember past experiences, adapt to new challenges and improve with every task. From memory architecture to practical implementation, this book shows you how to create agents that get better over time.

What really makes an AI agent work? It is not just the model, but the system around it. This book takes you inside the architecture, algorithms and engineering behind AI agents, showing you how to build a complete agent harness from scratch in Python and turn it into a working system you can debug, deploy and extend.

Ready to put Hermes Agent to work? This practical guide takes you from the basics to real-world automation, covering setup, architecture and workflows for research, writing, development and operations. Learn how Hermes works, how to configure it for reliable results and how to make it a useful part of your daily work.

What can you learn from hardware that comes with no documentation, missing schematics or a protocol nobody has explained? This book explores practical methods for uncovering how electronic systems work, combining hands-on investigation, software analysis and AI to turn unknown hardware into something you can understand, analyze and rebuild.

What really happens inside a neural network? This book explores the tools and ideas researchers use to uncover how AI systems work, from foundational concepts to practical experiments. Learn to separate solid findings from bold claims, develop useful research skills and prepare for a field that is changing as quickly as the technology itself.

Build a real LLM inference engine from the ground up in portable C. Learn how transformers work, load SafeTensors weights, implement KV caching and optimize inference on the CPU. By the end, you will run TinyLlama locally and generate text with a complete working runtime.

AI is changing the security landscape faster than most organizations can adapt. This practical guide explores how CISOs can lead through that change, strengthen digital trust and resilience, govern AI risks and protect the business without slowing the innovation it needs.

Take a practical journey through SGLang, from its core architecture to real-world deployment and optimization. Built for engineers who need to run LLM inference reliably at scale, this handbook breaks down the tools, techniques and operational know-how needed to build and maintain production-ready SGLang systems.