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.

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

Mastering Claude Code montre comment collaborer avec des agents d’IA pour écrire du code, automatiser des flux de travail et réaliser des projets d’envergure. De la configuration aux bonnes pratiques d’ingénierie logicielle, ce guide pratique explique comment les logiciels modernes sont conçus en 2026.

The loop writes code and clears context. The commit log records MAX_RETRIES = 3, but the reason it's three vanished on Friday night. The artifact survived; the decision evaporated.The Loop That Remembers inverts the premise: memory isn't the sixth piece, it's what the loop exists to produce. Two graphs, a promoted lattice, bounded context, and an evaluator checking claims against edges.

AI coding agents can move faster than Git’s staging-and-commit workflow.Juju-chu! shows how Jujutsu🐦‍⬛ gives you automatic commits, reliable undo, and a safer way to reshape messy AI-generated changes—while staying compatible with Git and GitHub. For developers who already use Git and want a calmer AI workflow.

Die Softwareentwicklung wandelt sich rasant, und Claude Code steht im Zentrum dieses Wandels. „Mastering Claude Code“ zeigt, wie Entwickler mit KI-Agenten Code schreiben, Arbeitsabläufe automatisieren und auch komplexe Projekte effizient umsetzen. Praxisnah vermittelt das Buch Einrichtung, Prompt-Design und bewährte Engineering-Praktiken für moderne Softwareentwicklung im Jahr 2026.

 Who This Book Is ForThis book is designed for a wide range of readers: Students (BCA, MCA, B.Tech, AI/ML learners) Software Developers & AI Engineers Researchers in NLP and linguistics Entrepreneurs building global products Educators and trainers Business professionals working in international marketsNo prior expertise in AI is strictly required, although basic programming knowledge can be helpful.  Practical and Future-Focused ApproachUnlike many theoretical books, this guide emphasizes real-world implementation. Every concept is aligned with current industry practices and future trends.You will gain insights into:Responsible AI developmentData privacy and complianceEthical deployment of language systemsFuture trends shaping global communication  What Makes This Book Unique Clear and structured learning path  Balance of theory and practical application  Strong focus on ethics and cultural context  Real-world case studies and projects  Designed for 2026 AI advancements  Suitable for both beginners and professionals   Important NoteThis book is strictly educational and informational. It does not claim guaranteed results or outcomes. Readers are encouraged to apply concepts responsibly and adapt them according to their specific use cases.  Your Journey Starts HereLanguage connects people. AI enhances that connection. But true communication requires more than algorithms—it requires understanding.With Multilingual AI Translation Mastery, you are not just learning technology—you are building the future of global communication.

El desarrollo de software está cambiando rápidamente, y Claude Code lidera esa transformación. Aprende a trabajar con agentes de IA para escribir código, automatizar tareas y crear proyectos más grandes con confianza. Este libro ofrece una guía práctica para el desarrollo moderno de software en 2026.

Artificial Intelligence is not merely about data, learning, or neural networks—it is fundamentally about exploring vast spaces of possibilities.Every search algorithm, planning system, optimization framework, recommendation engine, and machine learning model faces a common challenge: navigating an enormous combinatorial universe of potential solutions.In Combinatorial Thinking in Artificial Intelligence, Anshuman Mishra reveals the mathematical foundations that drive modern AI systems. Through permutations, combinations, graph theory, probability, search strategies, and optimization techniques, readers learn how intelligent systems reason, search, and make decisions in complex environments.If you want to understand why AI algorithms work—not just how to implement them—this book provides the missing mathematical perspective.Discover the combinatorial engine behind intelligence.

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.

Every intelligent system faces a fundamental challenge:How do you find the best solution when there are millions—or even trillions—of possibilities?From A* search and heuristic optimization to neural architecture search, hyperparameter tuning, constraint satisfaction, and quantum optimization, modern AI depends on sophisticated strategies for navigating combinatorial search spaces.In this advanced second volume, Anshuman Mishra explores the powerful algorithms that enable intelligent systems to search smarter, optimize faster, and scale beyond brute force computation.Discover how combinatorial thinking drives machine learning, optimization, quantum AI, and the future of intelligent decision-making.The future of AI belongs to those who understand combinatorial complexity.

Build and operate production AI platforms on Kubernetes. Learn to manage NVIDIA GPUs, serve and optimize LLMs with vLLM, run training and batch workloads, and design secure, observable, multi-tenant infrastructure for AI at scale.

4. Who Should Read This Book?This book is specially designed for a wide audience:4.1 StudentsStudents of:Artificial intelligenceData scienceComputer scienceInformation technologyOperations researchApplied mathematicswill find this book essential for understanding foundations and applications of intelligent decision-making.4.2 ResearchersThis book helps researchers explore:Decision-making modelsPlanning algorithmsRisk-aware AIMathematical modelingOptimization under uncertaintyIt helps form a strong base for research projects and PhD work.4.3 Industry ProfessionalsEngineers and developers working on:RoboticsAutonomous vehiclesDecision support systemsPredictive analyticsAI toolsFinancial modelingwill find the algorithms, pseudocode, and frameworks highly practical.4.4 Faculty MembersTeachers and professors can use this book as:A primary textbookA reference guideA source of problems and case studiesA foundation for graduate and research courses 5. Learning OutcomesAfter studying this book, readers will be able to:Understand and construct utility functionsEvaluate rational choices under uncertaintyBuild decision treesConstruct influence diagramsDesign sequential decision systemsFormulate and solve MDPsApply POMDPs to real problemsImplement classical planning algorithmsModel multi-agent interactions using game theoryApply Bayesian decision theory to uncertain environmentsUnderstand the foundation of reinforcement learningBuild real-world decision and planning systemsThis ensures comprehensive mastery of both theory and practice.

As the author, I (Anshuman Mishra) have written this book with the spirit of mentorship — not just to explain how chatbots work, but to help you build one confidently and ethically. I have taught AI, programming, and computer science for nearly two decades, and I have seen countless students struggle to bridge the gap between theory and implementation.This book closes that gap. It teaches you what to do, why to do it, and how to do it right. It’s not just a manual — it’s a journey from curiosity to mastery.You are not just learning to build a chatbot; you are learning to create intelligence — responsibly, creatively, and with purpose.

Your AI-generated code passes 18,000 tests and reports healthy — while entire data pipelines silently produce nothing. Silent Wiring names the failure mode nobody's tooling catches, and shows you how to find it before your users do.