Artificial Intelligence

The practitioner's guide to Claude Code in production: hooks, permissions, MCP, the Claude Agent SDK, evals and cost control for agents that run under real load. Thirty-one chapters covering the agent loop, tools, hooks, MCP, the Claude Agent SDK, permissions, multi-agent orchestration, evals, observability, and cost engineering. Includes a full walkthrough of Anthropic's financial services reference agents. Code from real production systems, not toy examples.

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.

What if AI could help you find flaws in software logic before they become costly bugs? This practical guide shows you how to use Claude Code alongside proven verification tools to test assumptions, uncover hidden issues and build confidence in your code. From first principles to production workflows, learn a smarter way to verify software with AI.

Learn how modern LLM inference engines work by building one from scratch in Rust. From transformers and tokenization to KV caching, quantization, batching, and GPU optimization, this book combines theory, hands-on code, and performance engineering to help you create fast, production-ready AI systems.

Your C# skills are worth more today than they were a year ago — if you know how to put a language model in the loop. This book shows you how, with the Microsoft Agent Framework: real tools, RAG, multi-agent orchestration, plus the hosting, observability, and safety that separate a demo from a system you ship. Nineteen chapters. 120 runnable projects. No Python detours. Just C# and .NET 10.

A practical guide to product engineering in an AI native era, where building shifts from manual construction to steering tools, editors, and agents. Product Engineering with AI covers platforms, agentic workflows, prompting, code quality, UX, and responsible practices for getting from prototype to production. 

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.

What does it take to run an LLM on hardware that barely has enough memory to breathe? This practical guide takes you from transformer math and model compression to inference runtimes, benchmarking and real-world deployment, with hands-on case studies for building fully offline LLM systems on tiny devices.

Bestselling book on building LLMs. A practical, project-driven manual for engineers who want to understand how modern language models are built — and where they fail — by writing every layer themselves. From a scalar autograd engine to RLHF to fused specialists, in 36 hands-on projects with deliberate sabotage experiments. Build it. Break it. Measure it.

Language models guess. Knowledge graphs know. Rusty Graph shows you how to build a local AI agent whose memory is a real knowledge graph — typed, validated, reasoned over, and queryable with SPARQL — all inside a single static Rust binary. No JVM. No Docker sidecar. No Python runtime bolted to the side. You will build Ares a research-assistant agent that observes papers, forms beliefs, makes promises to other agents, and tracks the provenance of every fact it holds. Chapter by chapter, Ares grows from an empty Cargo workspace into a full pipeline: > load → reason → validate → query → answer All of it in under a thousand lines of idiomatic Rust, using three crates that actually work today: `oxigraph`, `reasonable`, and `rudof_lib`. `grapfeo` You will learn how to- Model a domain as RDF triples and load them into an embedded store. - Write RDFS and OWL 2 RL axioms that infer trust, identity, and inverse relationships — automatically. - Guard your graph with SHACL shapes that reject bad data at the boundary, not in production. - Query everything with SPARQL, from simple lookups to federated queries across named graphs. - Wire the graph into a hybrid RAG pipeline so your LLM answers are grounded in facts, not vibes. Who it is forRust developers building agents, assistants, or any system where the answer "the model said so" is not good enough. You should be comfortable with Cargo and traits. You do **not** need any prior semantic-web background — every concept is introduced through Ares before any formal definition appears. Why Rust, why nowLocal agents are the next deployment target: a user's laptop, a Raspberry Pi, a WASM sandbox. Python cannot go there comfortably. Rust can. This book is the missing manual for the Rust side of the semantic web — the one that tells you exactly which crates work, where the ecosystem is thin, and how to ship anyway. Stop hoping your model tells the truth. Give it a graph that does.

Legacy modernization is an evidence problem before it is a code-generation problem. Use Claude Code to recover hidden behavior, build a verification harness, and move critical systems in controlled, reversible slices without handing production decisions to AI. Includes 26 practical chapters, 86 diagrams, and a runnable companion lab.

Learn how to build, run, and optimize llama.cpp from the ground up. This book covers everything from compiling the code and working with GGUF models to deploying fast, production-ready local LLM inference.

Discover how the Model Context Protocol is transforming the way AI applications connect to tools, data, and external systems. With clear explanations and practical guidance, this book shows you how to build, secure, and deploy production-ready MCP solutions, from your first server to enterprise-scale AI applications.

"If you intend to use machine learning to solve business problems at scale, I'm delighted you got your hands on this book." —Cassie Kozyrkov, Chief Decision Scientist at Google "Foundational work about the reality of building machine learning models in production." —Karolis Urbonas, Head of Machine Learning and Science at Amazon