Books

Learn how to build intelligent, production-ready AI agents with the OpenAI Agents SDK. Through practical Python examples and step by step guidance, you'll master everything from the fundamentals to advanced multi-agent workflows, tools, and deployment.

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.

Move beyond the API. Dismantle the AI black box and build generative engines from scratch with pure Python and NumPy. Master the profound geometric principles and applied mathematics driving LLMs and Transformers. Transform from a mere consumer into an elite AI innovator by writing the core mathematical architecture yourself—no shortcuts, no frameworks, just pure engineering excellence.

Learn how to build autonomous, production-ready multi-agent AI systems in C# with .NET. Using Harness, Hermes Agents, and Loop Engineering, you'll create intelligent agents that reason, collaborate, and solve real-world problems through practical examples and hands-on projects.

Build smarter AI systems that go beyond the limits of large language models. Retrieval-Augmented Generation is a practical guide to designing, implementing, and scaling RAG applications with modern retrieval techniques, vector databases, and real-world deployment strategies.

A practical, systems-first guide to understanding LLMs, RAG, AI agents, GraphRAG, evaluation, fine-tuning, and modern AI applications — without needing to start with equations.

Build and operate production AI platforms on Kubernetes. Learn to manage NVIDIA GPUs, serve and optimize LLMs with vLLM, run training and batch workloads, and design secure, observable, multi-tenant infrastructure for AI at scale.

Local Intelligence shows you how to run large language models entirely on your Mac with Apple Silicon. Learn to use tools like Ollama, MLX, and llama.cpp, understand quantization, and build real local AI applications with open-source code.

Discover the mathematics powering the future of Artificial Intelligence and cybersecurity.Algebraic Foundations of AI: Groups, Rings, and Fields in Security & Cryptography reveals how abstract algebra forms the backbone of modern AI security, cryptographic systems, blockchain technologies, privacy-preserving machine learning, federated learning, and post-quantum cryptography.

⚡ THE BRUTAL TRUTH: 90% of job seekers use AI the same way — basic prompts, generic CVs, zero strategy. The other 10%? They're getting callbacks while you wait. This guide puts you in the 10%.

A hands-on guide to designing, building, testing, and deploying secure stdio and SSE MCP servers in Python and TypeScript (437 manuscript pages).

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.

AI-collaboration interviews are not about prompting better. They are about controlling the process. Learn a four-step framework to ask better questions, write a clear spec, guide AI-assisted building, and prove correctness with independent review and tests.

Unlock the full potential of Claude Fable 5 with a practical guide built for developers who are shipping real AI applications. From prompt engineering and agent orchestration to RAG, memory, safety, evaluation, and production deployment, this definitive reference delivers proven patterns, hands-on examples, and reusable templates to help you build reliable, scalable AI systems with confidence.