Free Sample Chapters

Harness AI directly on your own Linux machines—no vendor lock-in, no cloud dependencies. Install GPU drivers and CUDA, run local LLMs with Ollama and llama.cpp, deploy containerized inference, and automate, secure, and scale AI workloads. A hands-on guide for developers, admins, and enthusiasts.

Reclaim AI on infrastructure you own. This hands-on guide shows you how to build, run, optimize, and secure large language models on your own Linux systems—from hardware selection and runtimes like llama.cpp, Ollama, and vLLM to prompt engineering, API serving, deployment, and scaling.

Master the Model Context Protocol from first principles to production. This hands-on guide teaches you to build MCP servers, resources, tools, and prompt templates, then secure, deploy, and monitor them—connecting AI applications to external tools and services reliably and at scale.

Run powerful AI models on your own hardware with Ollama. This hands-on guide covers installation on Linux, Windows, and macOS, pulling and running models, custom Modelfiles, the REST API, performance tuning, self-hosting, security, and real projects—for private, cost-free, local AI.

Master Retrieval-Augmented Generation from the fundamentals up. This guide breaks RAG into its core components—data sources, chunking, embeddings, vector databases, retrieval strategies, and LLM integration—then covers security, deployment, evaluation, and enterprise-grade systems.

Design, build, secure, and deploy production-grade AI APIs. This hands-on guide covers AI application architecture, API design, connecting models, prompt management, RAG and agent APIs, MCP integration, API and AI security, scaling, CI/CD, monitoring, and enterprise AI platforms.

Secure AI APIs, LLM services, and autonomous agents against threats traditional API security can't handle. This practical guide covers threat modeling, authentication and authorization, prompt injection, data leakage, rate limiting, agent and MCP security, monitoring, incident response, and DevSecOps.

Learn how to code smarter with AI. Master prompt engineering, AI pair programming workflows, debugging, test generation, system design, security, and performance optimization in the AI-augmented developer era.

Build production-ready full-stack systems with AI as your pair programmer. Master frontend, backend, APIs, databases, security, CI/CD, and observability.

Ship AI systems that survive production, not just demos. This engineering-first guide covers LLMs, model selection, prompt engineering, RAG and vector databases, agents and tool integration, local and cloud infrastructure, security, deployment, and monitoring—the full AI application lifecycle.

A comprehensive, practitioner's guide to defending enterprise AI—LLMs, ML systems, and autonomous agents. Covers prompt injection, jailbreaks, training pipeline and dataset security, RAG and embedding defense, agent authorization, infrastructure hardening, governance, and AI red teaming.

Secure AI systems, LLMs, and ML infrastructure against modern threats. This security-first guide covers prompt injection, data poisoning, model theft, and adversarial attacks—plus training data protection, infrastructure and API hardening, monitoring, incident response, and AI governance.

Become an AI-driven senior engineer. Master technical judgment, leadership, system thinking, and AI-augmented workflows to accelerate career growth.

Learn how to build real production-ready applications with AI pair programming. Master architecture, testing, DevOps, security, performance, scaling, and AI-assisted code reviews from prototype to deployment.

Using AI to Write Better Code, Debug Faster, and Boost Developer Productivity

Master systematic debugging, AI-assisted code analysis, safe refactoring, and technical debt reduction. Learn how to modernize legacy systems and solve complex software defects with structured AI workflows.

Learn machine learning from the ground up. Understand core concepts, data preparation, models, evaluation techniques, and real-world ML workflows-no advanced math required.

Master prompt engineering as a core developer skill. Learn structured prompt design, code generation control, debugging, testing, security, token optimization, and production-ready AI instruction patterns.

Master the principles of prompt engineering to design reliable, high-quality prompts for AI systems, reduce hallucinations, and build reusable prompt patterns.

Master secure coding for AI-assisted development. Protect applications, APIs, prompts, infrastructure, and supply chains with secure-by-design engineering practices.

Design scalable and reliable systems with AI as your architecture co-pilot. Master APIs, data models, caching, microservices, and real-world case studies.