Large language models

Game producers already have most of what AI collaboration requires. They have been managing context, decomposing work, and evaluating output for years. This book is the map from where they are to where the work is going.

An agent can score well on average and still fail exactly where it matters. Beyond “Ship and Pray” shows how to replace benchmark averages with designed experiments, geometric ground truth and failure attribution—so teams can discover when an agent breaks, identify the responsible component and test whether it fails safely under tool faults.

Mathematical Foundations of AI and Data Science: Discrete Structures, Graphs, Logic, and Combinatorics in Practice transforms abstract mathematical concepts into practical tools for computational problem-solving.Explore logic, set theory, relations, functions, combinatorics, discrete probability, graph algorithms, trees, algebraic structures, Boolean systems, recurrence relations, optimization.

AI agents don’t fail because they forget everything. They fail because they remember badly. This book shows you how to engineer memory that stays accurate, efficient and useful over time. From SQLite and PostgreSQL to vector indexes and multi-agent systems, you’ll learn what it takes to build agents that can run for days without losing the plot.

Build MCP servers that are ready for real production. This hands on guide shows you how to design, secure, deploy and operate stateless Model Context Protocol servers that scale with confidence. Packed with practical examples and proven patterns, it gives you the skills to build reliable AI infrastructure from the ground up.

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.

Unlock the full potential of Moonshot AI's groundbreaking Kimi K3 with practical prompting techniques, real-world examples and production-ready strategies. Whether you are building AI applications, conducting research or deploying autonomous agents, this guide helps you get better results from one of the world's most capable open models.

Envoy Proxy is the foundation of modern cloud-native networking, powering service meshes, API gateways and AI infrastructure. This practical guide shows you how to design, deploy and operate Envoy in production with expert guidance, real-world configurations and advanced patterns for scalable, resilient systems.

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.

Build intelligent AI applications with LangChain using practical, production-ready Python examples. From intelligent agents and retrieval-augmented generation to scalable deployment and observability, this book equips you with the skills and architectural understanding needed to create reliable, real-world LLM-powered systems.

Build AI agents that do more than generate text. This book shows you how to use the Claude Agent SDK to create autonomous systems that use tools, manage context, coordinate multiple agents, and solve complex tasks. With practical examples in Python and TypeScript, you will learn how to build secure, scalable agents ready for production.

Bridge the gap between high-level C# and raw hardware execution. Eliminate GC pauses with zero-allocation Span<T> and SIMD vectorization. Learn to build blazing-fast tokenizers and local LLM inference engines. Stop waiting for the runtime—start engineering for extreme AI performance.

Top-k is not relevance, retrieved text is not evidence and an LLM judging another LLM is not verification. Beyond “Chunk and Pray” shows how to build RAG that answers through a verified knowledge graph, preserves exact numbers, cites its sources and abstains when it cannot prove the answer.

Learn how large language models work instead of relying on black-box APIs. Building Large Language Models from Scratch takes you through training a Transformer model in PyTorch, from raw text to a working inference API, covering tokenization, attention, distributed training, and alignment along the way.