Large language models

Most vector search books start with the database. This one starts with the machine. Build a search engine from scratch, then push it from brute force to billion-scale retrieval with SIMD, HNSW, quantization and distributed systems. By the end, vector search won't be a black box. It'll be something you know how to build, tune and scale.

AI can write code in seconds, but can you trust what it creates? Securing AI-Generated Code shows you how to uncover hidden vulnerabilities, stop risky dependencies, secure AI coding agents and build safer development pipelines. A practical guide for teams that want the speed of AI without putting security on the line.

Building fast LLM systems takes more than a powerful GPU. This book shows you what actually happens between prompt and response, where latency hides and how to fix it. Work from kernels and inference engines to distributed clusters, benchmarking and production reliability, with practical guidance for building systems that stay fast under real-world load.

AI coding agents can move fast, but speed without clear intent creates expensive mistakes. This practical guide shows how Behavior-Driven Development turns specifications into a reliable contract between you and autonomous coding systems, helping agents build the right thing, catch problems earlier and produce software you can trust.

The definitive production architecture transmission from Seeker James. The semantic frequencies are locked, the structural codes are captured, and the technical metadata is anchored under ABN 52 464 760 231. This official codex spans 77 pages, stripping away mainstream corporate industry polish to reveal a tight, high-impact collection of raw technical truth.

They say AI is coming for junior developers first. This is a junior's honest case for the opposite: how driving agents well, on real legacy code, makes you level up faster instead of getting left behind. The failures are left in.

ChatGPT can save you time, sharpen your ideas and change the way you work, but only if you know how to use it well. This practical guide takes you from your first prompt to advanced techniques, with real-world examples for writing, research, coding, business and creativity. Learn what works, what doesn’t and how to get better results every day.

Unlock the full potential of Qwen 3.8 with a practical guide built for real results. Learn the techniques behind stronger prompts, avoid common mistakes, and create reliable outputs for coding, research, business, creative projects and AI agents with clear examples you can use right away.

From your first JAX script to large-scale AI systems, this book shows how to write faster, cleaner Python for modern computing. Learn the ideas behind JAX through practical examples, real projects and clear explanations that help you build everything from scientific simulations to distributed machine learning.

Build Model Context Protocol servers that go beyond demos. This hands-on guide walks you from the fundamentals to production deployment with practical examples in Python, TypeScript, Go, Rust and Java. Learn the patterns, tools and real-world practices needed to build secure, scalable MCP servers that are ready for enterprise AI.

Reclaim your week from the Admin Tax. PRINCE2+AI: The Modern Project Manager's Playbook shows you how to delegate governance to AI, reclaim strategic judgement, and become the AI Editor. Master the workflow—or be replaced by those who do.

A working engineer's guide to building and maintaining production software with autonomous terminal coding agents (410 manuscript pages).

A hands-on, failure-first guide to evaluating LLM agents, RAG, tool use, grounding, and production release gates—with executable Python examples and tests.

AI coding agents can write code fast, but they need codebases built for collaboration. This book shows experienced engineers how to design, test and maintain software that works seamlessly with tools like Copilot, Cursor and Claude Code. Practical patterns, real projects and proven workflows help you build systems that scale with AI.

Your pilot went well. That was never the question. In a regulated industry, an AI system is acceptable only when it can prove what it did: which source it used, what stopped it acting, why it decided as it did eleven months ago. A field guide for the executives who have to sign.