Books

Using AI to code is easy. Using it without losing control — not so much.By week three, your project stops moving. The agent forgets decisions it made ten days ago. A change in auth breaks the dashboard. You spend more time re-explaining context than writing features. The code works, but only you know why — and you're not even sure you remember all of it.That's vibe coding hitting its ceiling.Spec-Driven Development is the method that replaces the chaos with a spec the AI actually executes. Not a ceremonial document. A working artifact: PRD, issues, tests, code — all traceable, all connected, all in the right order.This book shows you:How to grill your own idea before writing a single promptHow to write a PRD the AI won't misinterpretThe 7 phases that turn an idea into working softwareHow to use GitHub SpecKit and openSpec (and when not to)How to work this way in a team without slowing downThe 5 anti-patterns that destroy every spec22,000 words. 13 chapters. 5 appendices with ready-to-copy templates.No theory dumps. No filler. Just the method.

Aprende los conceptos básicos del Machine Learning y avanza poco a poco con teoría y divertidos ejercicios prácticos en Python a niveles intermedios y avanzados hasta llegar al Deep Learning.Tu camino para convertirte en un Científico de Datos comienza aquí

UPDATE: Please don't pay for this!! It's free. Please just take it for free. I'd much rather give it away. Even when it's done, it'll still be free. I'm 100% NOT trying to make money on this. Tell your friends, and be sure to read it. Your applications lives and runs in more places - MCP, desktop applications, browser-based application, data integration pipelines, gRPC, browsers, etc. - than ever. Can your security framework keep up? If you're using Spring Security, then it definitely can, and this book - by Spring Developer Advocate Josh Long - will show you how.

Realistic ExpectationsThis book does not claim that AI will automatically guarantee success or eliminate all challenges. Instead, it presents AI as a support system that can enhance productivity when used correctly.Success depends on:Consistent effortProper implementationContinuous learningAdaptability to change A Long-Term Learning ResourceRather than a one-time read, this book is designed to be a practical reference guide. You can revisit different sections as your business grows and your needs evolve.  Final ThoughtArtificial Intelligence is not just for large corporations anymore. With the right approach, small businesses can also benefit from its capabilities.This book provides a structured, ethical, and practical pathway to understanding and applying AI—helping you take informed steps toward improving your business operations. 

What You Will LearnBy the end of this book, you will be able to:1.     Understand the core concepts of unsupervised learning and how it differs from supervised learning.2.     Preprocess and prepare datasets for clustering, including scaling, normalization, and handling outliers.3.     Implement popular clustering algorithms in Python, tuning parameters for optimal results.4.     Evaluate clustering performance using both internal and external metrics.5.     Apply clustering techniques to real-world problems such as customer segmentation, anomaly detection, and image grouping.6.     Work with high-dimensional data and understand techniques to reduce dimensionality while preserving patterns.7.     Use advanced clustering techniques to solve complex data grouping problems in large datasets.8.     Develop ethical awareness of privacy, bias, and fairness in AI applications. Benefits After Studying This Book For Students ·        Gain strong theoretical foundations in machine learning without supervision.·        Prepare for academic exams, assignments, and competitive exams like UGC NET, GATE, and data science interviews.·        Build portfolio-worthy projects to showcase in internships or job applications. For Job Seekers and Professionals ·        Learn industry-relevant clustering algorithms used in AI, marketing, healthcare, and cybersecurity.·        Enhance data analysis and problem-solving skills to stand out in interviews for roles such as Data Scientist, Machine Learning Engineer, or Business Analyst.·        Understand how to integrate clustering techniques into business solutions for better decision-making. For Researchers and Innovators ·        Explore cutting-edge clustering methods and hybrid models for high-dimensional and big data scenarios.·        Gain insights into current trends and future research opportunities in unsupervised learning.·        Leverage clustering techniques for research publications, AI prototypes, and academic projects.

Prompting is a design problem, not a syntax problem. Prompt Patterns introduces durable patterns for reliable work with large language models.

The practitioner's guide to Claude Code in production: hooks, permissions, MCP, the Claude Agent SDK, evals and cost control for agents that run under real load. Thirty-one chapters covering the agent loop, tools, hooks, MCP, the Claude Agent SDK, permissions, multi-agent orchestration, evals, observability, and cost engineering. Includes a full walkthrough of Anthropic's financial services reference agents. Code from real production systems, not toy examples.

Agentic AI is everywhere, but what does it actually mean? This book cuts through the hype and explains how AI agents work, how they are built and where they can go wrong. Starting from the basics, it gives you a clear, practical understanding of the technology and what it takes to trust an agent in the real world.

AI agents are only as good as the loops behind them. This book shows you how to build systems that plan, act, evaluate and improve with every step. Learn the patterns, frameworks and engineering practices behind reliable, self-correcting agents that solve real problems in production.

Build AI agents that do more than generate text. This book shows you how to use the Claude Agent SDK to create autonomous systems that use tools, manage context, coordinate multiple agents, and solve complex tasks. With practical examples in Python and TypeScript, you will learn how to build secure, scalable agents ready for production.

FOCUS Architecture presents an opinionated architectural approach for creating simple, organized, and maintainable systems. Instead of multiplying layers and abstractions, the book demonstrates how to structure applications around four components—View, Orchestrator, Use Case, and Repository—thereby reducing the cost of change and simplifying the work for both developers and AI tools.

Tired of memory bottlenecks? It’s time to build AI with the speed and safety of Rust. Learn to bypass standard abstractions and achieve raw performance at the hardware level. Detailed architectural diagrams and expert exercises for modern high-speed AI stacks. Stop compromising on performance—start building the future of AI engineering today.

"The AI confirmed X for me" used as proof of X. Outputs that sound brilliant but don't hold up to a severe re-reading. A three-page "AI policy" that nobody reads. Sound familiar? Thinking with LLMs, the Right Way is the system of critical thinking applied to LLMs: the Thinking-With Triangle (Intent / Adversary / Editor), the four meta-decisions of governance, the Socratic and adversarial practices for investigating and verifying. Not prompt engineering: the method for not letting yourself be mirrored.

Most companies are stuck in "pilot purgatory"—endless AI experiments that never scale. Teams spend weeks sharing clever prompts and one-off scripts, but nothing makes it into production. Budgets evaporate, engineers' enthusiasm curdles into cynicism, and executives start asking "we spent how much on this?" The problem isn't the AI tools—it's the approach. AI models are inherently non-deterministic (ask the same question twice, get different answers), while software engineering demands deterministic, reliable results. Without structure to bridge this gap, AI adoption fails spectacularly. This book introduces PIT™ (Plan Implement Test), a proven methodology that transforms unpredictable AI outputs into reliable engineering workflows. Built from thousands of hours of real development work, PIT™ channels AI's capabilities through structured planning, phased implementation, and comprehensive testing—delivering the consistency that production code demands.

Go from “AI as a copilot” to a team of AI testing agents that analyze, design, run, triage and review tests, with a human test lead making the decisions. Hands-on, in TypeScript, and vendor-agnostic: Claude, GPT, Gemini or a local model, with tools shared through MCP.