Large language models

Software development is changing fast, and Claude Code is at the center of that shift. Learn how to work effectively with AI agents to write code, automate workflows, and build larger projects with confidence. From setup and prompt design to real-world engineering practices, this book provides a practical guide to modern software development in 2026.

It's never been easier to build an AI agent — and never been harder to make one that actually works. This book takes you from language model foundations to production-ready multi-agent systems with the depth to predict failure before it happens, engineer graceful degradation over catastrophic failure, and take absolute architectural ownership. Get the paperback from amazon.

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.

Build AI agents that reason when necessary, preserve what they learn, and stop paying the intelligence premium for work they already know how to perform. Smart AI Agents combines architecture, working implementations, public source code, and measured experiments to show how agents can learn once and execute many.

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.

Serious AI research no longer needs a data center. This is the field log of training, fine-tuning, serving, and shipping real models on a single petascale desktop, with the code to reproduce every result. For engineers building AI on NVIDIA hardware who want depth, not hype.

Building fast LLM inference is about far more than turning the right knobs. This book takes you inside vLLM and the systems behind it, showing how memory, scheduling, GPUs and distributed serving shape real-world performance. Learn how to measure what matters, find bottlenecks and build inference infrastructure that scales without wasting money.

An LLM is not an AI system.Systems Thinking for Agentic AI shows software engineers and architects how to design reliable AI applications with prompts, RAG, tools, memory, orchestration, guardrails, evaluation, observability, and runtime control.Move beyond chatbot demos and learn how to build production-ready agentic AI systems you can reason about, measure, debug, operate, and improve

Somewhere there is a server with two GPUs for which no operational process exists. This book turns that into an operable platform — hands-on, in 23 labs: vLLM, KServe, LiteLLM, the NVIDIA GPU Operator, Keycloak, OpenBao, ArgoCD, pgvector. At the end your developers write code with local and external models through a governed gateway.

Agentic Cybersecurity: Engineering Autonomous AI Defense Cybersecurity is moving beyond traditional automation toward intelligent systems capable of continuously analyzing telemetry, investigating threats, using security tools, and executing controlled defensive actions. Agentic Cybersecurity: Engineering Autonomous AI Defense provides a practical technical overview of how to design, build, secure, and operate autonomous AI-driven cybersecurity systems.

AI agents are only as good as the loops behind them. This book shows you how to build systems that plan, act, evaluate and improve with every step. Learn the patterns, frameworks and engineering practices behind reliable, self-correcting agents that solve real problems in production.

ソフトウェア開発は急速に変化しており、Claude Codeはその変革の中心にあります。本書『Mastering Claude Code』は、AIエージェントと効果的に連携してコードを記述し、ワークフローを自動化し、より大規模なプロジェクトに自信を持って取り組むための方法を開発者に提示します。セットアップやプロンプト設計から実践的なエンジニアリング手法に至るまでを網羅した本書は、2026年における現代的なソフトウェア開発のあり方を理解したいすべての人にとって、実用的な指針となる一冊です。

Learn Claude Code by building real projects. This hands-on companion turns the Claude Code Masterclass workshop into a practical self-paced guide for planning, coding, testing, reviewing, refactoring, and shipping software with AI.

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.