Books

Vibe coding makes it easier than ever to build software fast, but shipping a real product takes more than speed. Vibe Coding to Production shows how to turn AI-generated code into secure, reliable and scalable SaaS applications with practical advice, real-world examples and the engineering habits needed to build production-ready systems.

AI is changing exploit development. Learn how to use large language models with tools like IDA Pro, Ghidra and Binary Ninja to speed up vulnerability research, reverse engineering and proof-of-concept exploit development while understanding where human expertise still matters.

This book is a practical guide to building and running local AI systems in 2026. Learn how to choose hardware, run modern LLMs, build RAG pipelines and AI agents, and deploy secure, efficient infrastructure while keeping full control of your models and data.

Learn Claude Code by building real projects. This hands-on companion turns the Claude Code Masterclass workshop into a practical self-paced guide for planning, coding, testing, reviewing, refactoring, and shipping software with AI.

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.

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.

Discover how the Model Context Protocol is transforming the way AI applications connect to tools, data, and external systems. With clear explanations and practical guidance, this book shows you how to build, secure, and deploy production-ready MCP solutions, from your first server to enterprise-scale AI applications.

Every test you have ever written assumed the same input gives the same output. Large language models broke that assumption, and with it most of what our profession knows about verification. This is the discipline that replaces it — golden datasets, property assertions, LLM judges you can actually trust, retrieval evaluation, adversarial suites, and CI gates that let a pipeline say no to a model.

"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.

Master automated workflows for your Python code with GitHub Actions for Python Projects. This practical guide covers automated testing, dependency management, coverage reports, documentation builds, and artifact generation to help you build reliable CI/CD pipelines effortlessly.

You leave Claude Code running and come back to something missing. This book is arranged by the accident, not the mechanism: databases dropped, files deleted, git history rewritten, the rewind command taking work with it. Three of the four get the exact hook that blocks the command before it runs, with the recovery if it already happened; no PreToolUse hook sees the rewind command, and the book gives the settings-level lever and names the one place a refusal could still live. Appendix B prints all 195 commands the guards were fired at, refusals and allowances both.

Graphs are everywhere.From social media networks and recommendation systems to autonomous vehicles, cybersecurity platforms, and modern artificial intelligence, graph structures have become the language of connected data.But how do machines understand relationships?How do search engines rank billions of pages?How do recommendation systems predict what users will like next?How do AI systems learn from complex networks?Graph Theory with AI Applications: Foundations, Algorithms, and Modern Neural Approaches (VOL-1) provides the answers.This book takes readers on a structured journey through graph fundamentals, graph algorithms, shortest path methods, network optimization, social network analytics, community detection, and graph mining techniques. Designed for students, researchers, educators, and professionals, it combines mathematical foundations with practical AI applications.If you want to understand the science behind connected intelligence and prepare yourself for the future of Graph Neural Networks and AI-driven graph learning, this book is your starting point.Discover the foundations. Master the algorithms. Build the future of Graph AI.