Vibe Engineering

Software development is changing fast, and Claude Code is at the center of that shift. Learn how to work effectively with AI agents to write code, automate workflows, and build larger projects with confidence. From setup and prompt design to real-world engineering practices, this book provides a practical guide to modern software development in 2026.

Stop fixing the same AI agent mistake twice. Turn each repeat into a small, tested guard: 34 pattern cards, 26 drills you can run offline, and free companion code.

Unlock the full potential of Claude Fable 5 with a practical guide built for developers who are shipping real AI applications. From prompt engineering and agent orchestration to RAG, memory, safety, evaluation, and production deployment, this definitive reference delivers proven patterns, hands-on examples, and reusable templates to help you build reliable, scalable AI systems with confidence.

OpenClaw in Production shows you how to run OpenClaw as a secure, reliable service that can handle real workloads. Whether you're deploying on a Raspberry Pi or operating a Kubernetes cluster, you'll learn the practical skills needed to keep your agents stable, secure, and easy to manage as they grow from a single instance to production at scale.

Most developers use AI coding tools like better autocomplete. The 10x Developer's Cursor Playbook shows you how to use Cursor for agent workflows, refactoring, MCP integrations, collaboration and security so you can ship faster, write better code and get more out of AI.

A working engineer's guide to building and maintaining production software with autonomous terminal coding agents (382 manuscript pages).

A working engineer's guide to building and maintaining production software with autonomous terminal coding agents (382 manuscript pages).

A hands-on guide to downloading, running, serving, and maintaining open-weight LLMs on your own machine (492 manuscript pages).

Get more out of Claude Code with practical tips, proven workflows and clear guidance you can put to work right away. Whether you're just getting started or already using it daily, this guide helps you write better code, work smarter and avoid common pitfalls while making the most of what Claude Code has to offer.

Rapid-development frameworks across Ruby on Rails, Node.js, PHP, Python, and .NET revolutionized how quickly we can ship software. But what happens when your business domain grows complex? Relying on Active Record and heavy ORM patterns tightly couples your database to your business logic. As your product matures, this coupling creates brittle, difficult-to-maintain codebases that resist change and slow down your team. The solution is the Domain Model Pattern (the foundation of Domain-Driven Design). It isolates business rules from infrastructure, creating a pure, scalable architecture. Historically, however, this pattern required a massive amount of effort and boilerplate, making it too slow for fast-paced development. That changes today. We are at an AI inflection point. Modern AI coding assistants excel at generating boilerplate and wiring infrastructure, effectively eliminating the traditional drawbacks of DDD. AI finally bridges the gap between the rapid speed of framework scaffolding and the architectural purity of a Domain Model. But to harness AI reliably, you cannot simply let it generate code unguided. You need a structured methodology to guide the machine. In this book, you will learn:How to build reliable, maintainable software using a strict, AI-assisted pipeline: Use Cases → Gherkin → Event Storming → Strategic & Tactical DDD.How to create implementation-agnostic class diagrams that serve as universal blueprints, allowing you to generate code in OOP languages (Java, C#, PHP, Python) or Functional languages (Elixir, Clojure, F#).How to future-proof your systems with a timeless methodology that prioritizes the longevity of business logic over the ephemeral nature of web frameworks.How to scale your architecture gracefully from a Majestic Monolith to Microservices without a complete rewrite. The methodology is language agnostics but in this edition all codes samples are in TypeScript. Whether you want to accelerate your development velocity, improve your system's resilience, or simply reclaim your craft by building beautiful, majestic systems, this book provides the blueprint.Discover how to combine the timeless principles of DDD with the power of AI.

AI can write code faster than you can read it—so why do projects still derail? The answer is the spec. This book teaches Spec-Driven Development hands-on, building a complete app one loop at a time with Spec Kit. Stop prompting and praying. Start shipping software AI actually gets right.

Generative AI can accelerate implementation, but effective software development still depends on sound engineering decisions. AI-Assisted Software Engineering explores how to distinguish engineering decisions from implementation tasks, decompose development work into well-defined AI-friendly tasks, apply engineering patterns, and use AI to work with unfamiliar technologies and code—all while keeping architecture, requirements, and technical direction under human control.

Learn how Prompt, Context, Harness, and Loop turn AI-generated code into reviewable engineering work. Visual explanations cover task framing, trustworthy context, safe tools, independent verification, and controlled iteration.

5. How This Book is Useful for StudyThis book is carefully designed to meet the needs of students, teachers, and researchers in computer science, artificial intelligence, and applied mathematics. Here’s how it will benefit learners:1.     Conceptual Clarity: The book explains abstract concepts like fuzzy sets, fuzzy logic, and fuzzy inference in simple and student-friendly language.2.     Step-by-Step Approach: Mathematical derivations, examples, and solved problems are provided in a logical sequence.3.     Exam-Oriented Content: The text covers topics that are important for BCA, MCA, B.Tech, M.Tech, and UGC NET examinations.4.     Practical Relevance: Numerous case studies and applications in AI systems are provided, making the subject practical and engaging.5.     Interdisciplinary Approach: The book connects mathematics, computer science, and engineering, preparing students for multidisciplinary research.6.     Hands-On Learning: Exercises, numerical problems, and small projects are suggested for practice.7.     Self-Study Friendly: Designed in such a way that even students without a strong background in higher mathematics can grasp the concepts.In summary, this book transforms fuzzy mathematics from a theoretical subject into a practical skill set essential for AI study and research. 6. Why This Book is Different and ImportantWhile there are many books on fuzzy logic or AI separately, very few combine mathematical foundations with AI applications in such a comprehensive, student-oriented way.·        Balanced Coverage: Unlike purely theoretical texts, this book strikes a balance between mathematical rigor and practical applications.·        Accessible Style: Concepts are explained in simple terms, supported with examples, diagrams, and illustrations.·        Focus on Students: Written with examination and career needs in mind, making it directly useful for academic success.·        AI-Oriented Approach: Unlike classical fuzzy mathematics books, this one emphasizes how fuzzy tools integrate into AI systems.·        Future-Ready Content: Emerging areas like fuzzy deep learning, quantum fuzzy logic, and hybrid systems are included to prepare students for future research.This makes the book not just a textbook, but a career-building guide for students of AI. 7. Pedagogical FeaturesTo make learning engaging and effective, the book is structured with special features such as:·        Definitions and Key Points highlighted for quick revision.·        Illustrations and Diagrams for visual understanding.·        Solved Examples to demonstrate problem-solving techniques.·        Exercises and Practice Problems at the end of each chapter.·        Case Studies to connect theory with real-life AI applications.·        Summary Sections for quick review before exams. 8. Who Should Read This Book? (Target Audience)This book is intended for a wide range of learners:·        Undergraduate Students (BCA, B.Sc., B.Tech): To build a foundation in fuzzy mathematics and AI.·        Postgraduate Students (MCA, M.Sc., M.Tech): For advanced study and research in AI and soft computing.·        Competitive Exam Aspirants (UGC NET, GATE): As a reference guide for key topics in AI and fuzzy logic.·        Researchers and Scholars: To explore applications of fuzzy systems in machine learning, NLP, and robotics.·        Industry Professionals: To understand the practical applications of fuzzy logic in AI-based projects. 9. Practical Outcomes After Studying the BookBy the end of this book, readers will be able to:·        Understand the mathematical foundations of fuzzy sets, relations, and logic.·        Apply fuzzy reasoning to solve uncertain and imprecise problems.·        Design fuzzy inference systems and controllers for AI applications.·        Implement fuzzy clustering and decision-making models in machine learning.·        Integrate fuzzy systems with neural networks and genetic algorithms.·        Contribute to research in advanced areas like fuzzy deep learning and quantum fuzzy logic.This ensures that learners not only gain theoretical knowledge but also practical competence in applying fuzzy mathematics in AI.

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.