AI Fullstack Expert Bundle

Note: This book contains a total of 159 pages. The auto-generated table of contents page count shown in the sample PDF is incorrect.

This is a comprehensive guide to Claude Code, Anthropic's agentic coding system that has fundamentally changed how software gets built. Whether you are a developer new to AI-assisted programming or an experienced engineer looking to master multi-agent workflows, this book takes you from first installation through enterprise deployment. It covers Claude Code's architecture, prompt engineering techniques, real-world case studies from Stripe and Wiz, CI/CD automation, security best practices, and the future of agentic coding. Every chapter includes practical examples, hands-on projects, and actionable guidance grounded in research on hundreds of thousands of real Claude Code sessions.

Note: This book contains a total of 186 pages. The auto-generated table of contents page count shown in the sample PDF is incorrect.

Retrieval-Augmented Generation has become the dominant architecture for production AI applications, and vector databases form its critical retrieval layer. This book takes you from the fundamentals of embeddings and similarity search through to advanced distributed deployments of Qdrant, the high-performance open-source vector database written in Rust. You will learn every facet of Qdrant's feature set: collections, points, payloads, HNSW indexing, quantization, hybrid search with dense and sparse vectors, metadata filtering, faceting, sharding, replication, security, monitoring, and scaling. Through detailed code examples in Python, JavaScript, Go, Java, C#, and Rust, you will build complete RAG pipelines integrated with LangChain, LlamaIndex, Haystack, and a wide range of embedding providers. This is not a tutorial collection; it is a comprehensive reference that explains the why behind every feature, the trade-offs between every configuration choice, and the production engineering practices that separate experimental prototypes from systems serving millions of queries per day.

Note: This book contains a total of 109 pages. The auto-generated table of contents page count shown in the sample PDF is incorrect.

This book is a comprehensive guide to PydanticAI, the Python agent framework that brings the rigor and developer experience of the Pydantic ecosystem to building production-grade generative AI applications. Whether you are a Python developer new to LLMs or an experienced AI practitioner looking for a framework that takes type safety seriously, this book will take you from your first agent to complex multi-agent systems with durable execution. Every chapter includes working code examples, real-world patterns, and practical exercises.

Note: This book contains a total of 118 pages. The auto-generated table of contents page count shown in the sample PDF is incorrect.

This book is a comprehensive, up-to-date reference for modern Python development in 2026. It covers the tools that define the contemporary Python workflow: package management with uv, pip, Poetry, and PDM; code quality with Ruff, Black, and mypy/Pyright; testing with pytest and Hypothesis; performance optimization with Cython, Numba, and PyO3; build systems, documentation, CI/CD pipelines, security scanning, and more. Whether you are starting a new project or migrating an existing codebase, this book gives you the knowledge to make informed tool choices backed by benchmarks, real-world data, and current best practices.

Note: This book contains a total of 168 pages. The auto-generated table of contents page count shown in the sample PDF is incorrect.

Large language models are not just text generators. They are instruction-processing engines that blur the boundary between data and commands. Every time an LLM ingests untrusted text, it faces a fundamental architectural challenge: determining what it should execute versus what it is merely being told about.

This book bridges the gap between prompt engineering and cybersecurity, giving developers, security professionals, and technology leaders the knowledge and practical tools needed to build AI systems that are both effective and resilient. From understanding how LLMs interpret instructions at a mechanistic level, to examining real-world attack case studies, to implementing enterprise-grade defense architectures, the book provides a comprehensive guide to developing trustworthy, injection-resistant AI systems for production environments.