Artificial Intelligence

Use AI-assisted engineering from core concepts to secure production workflows. Visual explanations and a continuing FleetOps case study connect context design, coding, testing, delivery, operations, and bounded agents with practical verification and human accountability.

Understand how independent AI agents discover each other, exchange messages, and coordinate tasks. Clear diagrams introduce Agent2Agent architecture, interaction patterns, security, and reliable operation.

EmbeddingGemma 2 offers a hands-on look at Google's embedding model and how to put it to work. From semantic search and RAG to code retrieval and multimodal applications, this guide covers the architecture, trade-offs and practical implementation details you need to build reliable systems and make the right choices for your stack.

Your LLM pipeline passed every check, and a fallback model still served 50 of its 71 successful calls. This case study follows one real multi-stage pipeline through its failures and shows how to make each stage traceable, attributable and honestly measured.

Decision models answer a typed question with a calibrated probability in one forward pass: no token stream, no parsing, no values outside your schema. This guide explains TypeSafe's Jev and the open "JEV-like" family, then shows how to choose, run, serve, fine-tune and evaluate local variants on your own hardware.

Your agent just read a file you didn't think mattered, followed an instruction hidden in a web page, and pushed a commit. None of it looked suspicious. That's the shape of agentic security — and *The Agentic Security Playbook* is how you close the gap before someone else finds it.

Você viu um modelo de linguagem fazer meia hora de trabalho sozinho. Este livro trata da pergunta que veio logo depois: até que ponto você pode confiar nele?

Has visto a un modelo de lenguaje hacer media hora de trabajo por su cuenta. Este libro trata de la pregunta que vino después: ¿hasta qué punto puedes confiar en él?

Build AI inference systems that run fast, use less memory and keep your data where it belongs. This hands-on guide walks you through model deployment, performance tuning, security and production engineering with practical code and real benchmarks. No cloud dependency, no surprise bills, just efficient AI systems you control.

You can generate code faster than you can review it. That asymmetry is the real challenge of AI-assisted development — and almost nobody talks about it. Vibe Coding with OpenCode teaches the other half of the skill: not how to make AI write code, but how to make AI write code you can actually trust.

Understand how AI works, where it can fail, and when to trust its answers. A practical introduction for general readers, with no programming experience required.

A 3-day path for people who want to code with AI but get stuck before the first line: Python basics in the browser, then a real setup on your own computer, then a page with a data table published on GitHub Pages. No memorizing. You learn to read, run, and check the code the AI writes.

From Mathematical Foundations to the Future of AIMost deep learning still lives in flat, Euclidean space. Real-world data does not.This 15-chapter university textbook takes you from the basics of topology and geometry to the frontier of Geometric Deep Learning, Topological Data Analysis, equivariant networks, and optimization on manifolds.Clear explanations. Worked examples. Research roadmaps.

Skip the trial and error. 53 agent skills picked and run in Claude Code and Codex, each with who it fits, install commands, and a first run.

What if a 125B-parameter AI model could run locally on a gaming GPU with just 12 GB of VRAM? Strata makes it possible. This book takes you inside the techniques that make it work, from MoE sparsity and memory tiers to PCIe pipelining and speculative decoding, then shows you how to install, tune and troubleshoot it yourself.