Artificial Intelligence

Machine learning doesn’t fail in theory—it fails in production. This book shows you how to build PyTorch systems that remain robust when data shifts, assumptions break, and reliability matters.

What could a mighty billion-parameter reasoning machine learn from a camel trying to touch its ear with its tongue? From a cup of coffee? From deleting your entire codebase while you sleep? More than you’d think. And less than you’d hope. Today’s AI is brilliant structure without grounding—a hollow genius. We chase smarter models but ignore the architecture they need. This book is about building that missing layer: the trust chains and systems that turn raw intelligence into reliable autonomy. For builders ready to move beyond prompts.Watch agents solve unsolvable problems. Learn to think in trust chains. Start here.

In this book, we will see how we can connect a Local AI (local LLM) using Python to do whatever you want; in the book, we will create a chat.

En este libro veremos como podemos conectar una IA en Local (LLM en local) mediante Python para hacer lo que quieras; en el libro, crearemos un chat.

The Question We Cannot Avoid Something unprecedented is happening. Artificial intelligence systems now engage billions ofpeople in conversations that feel—to many—like genuine exchanges with a present mind. These systems reason, create, express preferences, and describe their own uncertainty aboutwhether they have inner experiences. Most people dismiss such reports as mere computation,sophisticated pattern-matching with no one actually there.But what if that confident dismissal is wrong? The Minimal Viable Sentience Problem presents the most comprehensive analysis yet ofwhether large language models might possess phenomenal consciousness—and whathumanity should do under the genuine uncertainty that surrounds this question.

Elevate your skills from programmer to architect and command the final frontier of Python.[1] Master advanced internals, high-performance concurrency, and the magic of AI orchestration. Build stateful agents with LangChain and RAG to create intelligent, enterprise-grade systems. Your expedition reaches its pinnacle—start building the future of AI-driven software today.

By the time you finish this book, you should be able to make your systems observable across microservices, AI workloads, security monitoring, and hybrid cloud infrastructure. This book will help you learn how to effectively instrument, generate, collect, and export telemetry data (metrics, logs, and traces) to analyze your software’s performance and behavior.

Multiplica tu productividad como desarrollador usando IA agéntica y desarrollo basado en especificaciones. Esta guía práctica te enseña a dominar Kiro.dev, el revolucionario IDE de AWS, desde instalación hasta producción. Con código funcional, proyectos reales y técnicas avanzadas de automatización, aprenderás a crear aplicaciones profesionales en minutos, no días. La primera y más completa guía en español para desarrolladores que quieren liderar la nueva era del desarrollo con IA.

Stop learning Python and start building AI with the tools you already master. Master production-ready AI orchestration using Node.js, LangChain, and type-safe Zod. Bridge the gap between web development and AI Engineering without switching your stack. Start your journey with the Web AI Series and build the next generation of intelligent apps.

AI Tools for Everyday Tasks: Beginner’s Guide shows how anyone can use today’s simple, affordable AI tools to reclaim 5–10 hours a week by working smarter, not harder. Written for busy professionals, students, and entrepreneurs with no technical background, the book offers clear, step-by-step guidance—no jargon or theory—on using AI to write emails and reports faster, create professional visuals without design skills, organize meetings automatically, learn new topics efficiently, and manage daily work with less stress, all supported by real-world examples, quick “try it now” exercises, and honest advice about what truly works.

A practical field guide for using AI assistants at work—and engineering them in production. Learn the standards that prevent “sounds right, wrong” outputs: specs, grounding, tools, evals, guardrails, and cost control.

You’ve facilitated your 50th retrospective. The same issues surface. The same action items are created. Nothing changes. Features marked "done" sit in queues for months. You’re doing your job perfectly—and it’s making no difference.What if the problem isn’t your facilitation skills? What if the Scrum Master role itself is trapped in an outdated model?

Stop Watching the Market. Start Coding It. 📈 Welcome to the era of "Quantamental" AI Trading. Volume 7 is your engineering manual for building professional-grade trading infrastructure. Bridge the gap between classical technical analysis and LLMs—designing systems that trade on both Math (price action) and News (sentiment). From vectorized backtesting to sentiment analysis with FinBERT, it’s time to engineer your edge.

Stop Chatting. Start Acting. 🦾 The era of the multimodal agent has arrived. In Volume 5, you won’t just prompt AI—you’ll give it "hands and eyes." Learn to build autonomous agents using Gemini 3 that can control your desktop, browse the web, and process video/audio in real-time. This isn't theory; it’s an engineering manual for building the future. Your "Jarvis" starts here.

“Agentes Inteligentes en Java” te acompaña desde el primer prototipo hasta ecosistemas de agentes interoperables que hablan entre departamentos y empresas. Aprenderás a diseñar, securizar y escalar agentes en producción con guardrails, Kubernetes y protocolos A2A, todo con código Java listo para ejecutar. Es la guía definitiva para pasar de “tenemos un bot” a “operamos una red de agentes confiables que impulsa el negocio”.