Books

The most important impact of AI on software development isn't that it writes code faster — it's that it changes what you can delegate. Agentic Programming is a practical roadmap for climbing the AI Fluency Ladder: from prompting to agentic workflows, verified execution, and ultimately autonomous software development.

A clear, illustrated guide to large language models, covering key concepts and practical applications. Ideal for projects, interviews, or personal learning.

A best-selling book. The practitioner's guide to Claude Code in production. 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.

With the advent of LLMs and AI coding assistants, we are generating code at an ever-accelerating rate. How do we manage this influx of code without getting fatigued and overwhelmed? How do we ensure coding standards are maintained? What practices and tools can we put in place to amplify our efforts while not introducing more bugs?

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.

A hands-on guide to designing, building, testing, and deploying secure stdio and SSE MCP servers in Python and TypeScript (447 manuscript pages).

How to put AI agents in production without ending up in the news. A field guide to bounded AI autonomy, MCP security, and AgentSecOps.

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.

Master language models through mathematics, illustrations, and code―and build your own from scratch!

The definitive guide to agentic software engineering with Codex CLI, from prompting and AGENTS.md fundamentals to multi-agent orchestration, CI/CD integration, security hardening, and enterprise deployment across 28 hands-on chapters.

Build a complete LLM inference engine in C++ — from a blank project to a working Transformer that loads a real model and generates text. Forged one challenge at a time, with tests that prove every piece works before you move on.

The fastest practical path to understanding harness-driven development as a complete system. Through clear visual diagrams and a real repository mapped to the book, you will see how a harness guides AI agents, evaluates their work, detects drift, enforces constraints, supports repair, and keeps software evolution visible, verifiable, and under control.

A scenario-based study guide for the Claude Certified Architect Foundations (CCAR-F) exam. Learn to reason like an architect across all five domains, then prove it on two full practice exams.

Vector stores don't think — they search. They find fragments that sound like your query, then forget they ever looked. Every session starts from nothing. Every context window is a memory that dissolves at sunset.But the deeper problem isn't amnesia. It's that when agents do remember, they remember in someone else's house — on servers you don't control, in formats you can't inspect, under terms you didn't write.Memory Graph is a book about building something different: persistent, structured, queryable memory that lives inside your application — no external servers, no data leaving your process, no infrastructure you don't own. An embedded graph database that travels with your agent the way a nervous system travels with a body.You'll learn how to model not just facts, but relationships between facts. Causality. Temporal ordering. The layered structure of meaning that makes memory more than a search index. You'll build ontologies that enforce what can be known and how. You'll combine graph traversal with semantic search — so your agents find not just what's similar, but what's connected.The result is an agent that remembers the way you do: structurally, contextually, privately — with memory that belongs to you.

The CCAR-P exam tests judgment, not trivia. This scenario-based guide covers all seven domains, from solution design and RAG to governance and stakeholder communication, with 65 sketchnote figures, chapter quizzes, a distractor appendix that explains every wrong answer, and a full 63-question practice exam at the official weighting.