Books

Discover the power of thinking in logic. This hands-on guide takes you from your first Prolog query to advanced techniques in constraints, grammars, AI search, meta-programming and production development. With clear explanations and executable SWI-Prolog examples, you’ll learn not just how Prolog works, but how to use it well.

Keyword search misses meaning. Vector search misses precision. This book shows you how to combine them into production systems that deliver both, with architecture patterns, model selection frameworks, evaluation methodology, and operational guidance grounded in primary research.

By the time you finish this book, you should be able to make your systems observable across microservices, AI workloads, security monitoring, and hybrid cloud infrastructure. This book will help you learn how to effectively instrument, generate, collect, and export telemetry data (metrics, logs, and traces) to analyze your software’s performance and behavior.

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. 

Most AI systems fail in production. They chain prompts together and call it "agentic". They collapse under real-world pressure. The gap isn't the technology – it's missing infrastructure. This book bridges that gap. Learn the architecture patterns and infrastructure to build autonomous AI systems that work at scale. Master what distinguishes real agents from chatbots with tools. Deploy with confidence. Stop building demos. Start shipping production agentic AI.

大規模言語モデル (LLM) の主要な概念から実践的な応用例まで、簡潔に図解されている学習ガイドです。プロジェクトでの利用、面接対策、個人的な学習にも最適です。

Build real AI products with TypeScript. Learn LLMs, RAG, Agents, MCP, and production AI engineering from a frontend developer's perspective.

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.

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.

Most enterprises can deliver fast, but few can adapt at scale.The Cybernetic Enterprise introduces a unifying operating model that embeds AI, feedback loops, and platform thinking into the DNA of your organization. Learn how to build a system that senses change, learns continuously, and transforms disruption into strategic advantage.

An introduction to Common Lisp and many useful example programs. Use LLMs, as well as classic symbolic AI techniques.

Modern Deep Learning models can be extremely large, often exceeding the memory capacity of a single GPU or CPU. In these cases, training must be distributed across multiple processors. This introduces the need for high-speed communication between GPUs—both within a single server and across multiple servers. Intra-node GPU communication typically relies on high-speed interconnects like NVLink, with Direct Memory Access operations enabling efficient data transfers between GPUs. Inter-node communication, however, depends on the backend network, either  InfiniBand or Ethernet-based. Synchronization of model parameters across GPUs places strict requirements on the network: high throughput, ultra-low latency, and zero packet loss. Achieving this in an Ethernet fabric is challenging but possible.   This is where datacenter networking meets Deep Learning. Understanding how GPUs communicate and what the network must deliver is essential for designing effective AI data center infrastructures.

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.

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.