Vibe Engineering

How do voice assistants understand speech?How does a chatbot track conversation context?How can machines identify speakers, translate languages, recognize named entities, and process sequential information?The answer lies in sequence modeling.In Volume-2 of Hidden Markov Models and AI, readers move beyond theory into practical applications of Hidden Markov Models in speech recognition, natural language processing, machine translation, speaker verification, conversational AI, and intelligent decision-making systems.Learn how modern AI systems transform speech signals and language sequences into meaningful intelligence using probabilistic models that continue to influence today's most advanced technologies.Whether you are an AI student, NLP researcher, speech engineer, or machine learning professional, this volume provides the practical knowledge required to master sequential learning systems.

Artificial Intelligence is not only about neural networks and transformers. Behind many of the world's most influential AI systems lies a powerful probabilistic framework known as the Hidden Markov Model (HMM).From speech recognition and natural language processing to robotics, cybersecurity, finance, and bioinformatics, HMMs remain one of the most important sequence modeling techniques ever developed.This book takes readers on a complete journey through Markov Chains, probabilistic reasoning, Hidden Markov Models, Forward-Backward algorithms, Viterbi decoding, Baum-Welch training, and advanced HMM architectures.Designed for students, researchers, and AI professionals, the book combines rigorous mathematics with practical applications, making complex concepts accessible and immediately useful.If you want to truly understand how intelligent systems model uncertainty, learn from temporal patterns, and reason about hidden information, this book provides the foundation.

Build four real mobile apps from scratch — no coding experience required. Claude Code handles the code. You handle the idea.

What if the unpredictable behavior of intelligent systems is not a flaw—but a feature?In Nonlinear Dynamics and Chaos Theory in Artificial Intelligence (VOL-II), Anshuman Mishra explores the fascinating intersection of chaos, complexity, fractals, adaptive intelligence, and machine learning.Discover how chaotic neural networks, nonlinear optimization, fractal learning architectures, autonomous robotics, reinforcement learning, and emergent intelligence are transforming the future of AI.Through mathematical rigor, practical Python implementations, real-world case studies, and cutting-edge research directions, this book reveals how chaos can become a powerful tool for building smarter, more adaptive, and more resilient intelligent systems.For researchers, students, engineers, and AI innovators, this volume opens the door to one of the most exciting frontiers of next-generation artificial intelligence.

What if the unpredictable behavior of AI systems is not a flaw but a consequence of deeper mathematical laws? Explore chaos theory, nonlinear dynamics, fractals, emergence, and complexity to uncover how intelligent systems learn, adapt, self-organize, and evolve in ways that traditional linear models cannot explain.

Explore the advanced world of AI reasoning through SAT and SMT solvers, knowledge representation, intelligent agents, logic programming, formal verification, and neuro-symbolic AI. Learn how modern intelligent systems reason, prove, verify, and explain decisions with mathematical precision.

Discover the mathematical foundations behind intelligent reasoning. Explore propositional logic, predicate logic, theorem proving, SAT and SMT solvers, knowledge representation, formal verification, and AI reasoning systems in one comprehensive guide designed for students, researchers, and AI professionals.

Explore the advanced frontier of Category-Theoretic Artificial Intelligence. Discover how transformers, symbolic reasoning, reinforcement learning, higher categories, topos theory, and compositional learning can be unified through the powerful language of category theory. An essential guide for researchers, AI professionals, and future AGI innovators.

Discover how Category Theory is becoming the mathematical language of modern Artificial Intelligence. Explore categories, functors, natural transformations, compositional learning, neural networks, probabilistic models, and functorial machine learning through an AI-first approach designed for students, researchers, and AI professionals.

Coder ne suffit plus. N'importe quel LLM peut générer du code. Pour vingt dollars par mois.Ce qui change tout, c'est d'avoir une **méthode**. Sans méthode, l'IA te produit du code.Avec une méthode, elle te produit le logiciel dont tu as besoin. --- Le vibe coding a un problème Les deux premières semaines sont magiques. Chaque session produit duvrai code. Le projet grandit vite. Troisième semaine : tu corriges un module, quelque chose casse ailleurs.Quatrième semaine : l'agent ne se souvient plus des décisions prises.Cinquième semaine : ce qui prenait une heure en prend maintenant trois. Ce n'est pas un bug. C'est l'absence d'artefact de référence partagé. --- La solution : la spec comme artefact primaire **Spec-Driven Development (SDD)** inverse l'ordre habituel. Le code ne sert plus de source de vérité. La spec l'est.Le code implémente la spec. Les tests vérifient la spec.Et quand quelque chose dérape, tu mets d'abord à jour la spec. Un `.md`. Versionné avec git. Toujours à jour. C'est tout. --- Ce que tu vas apprendre - Pourquoi le vibe coding se dégrade et les 3 symptômes universels- Ce qu'est réellement le SDD (et ce qu'il n'est pas)- Les 7 phases du développement sérieux avec l'IA- Comment écrire un PRD en ~500 mots qui guide tout le reste- Comment transformer un PRD en issues GitHub actionnables- La boucle d'exécution : spec → agent → révision → itération- Les outils : GitHub SpecKit, OpenSpec, et les flux agnostiques- Les antipatterns les plus coûteux et comment les éviter- SDD en équipe, en greenfield et en brownfield- 3 cas complets : webhook, notifications, API publique --- Ce livre est fait pour toi si… - Tu utilises déjà l'IA pour coder mais les projets deviennent ingérables- Tu veux livrer plus vite sans perdre le contrôle de l'architecture- Tu travailles seul ou en équipe et tu cherches un cadre reproductible- Tu veux comprendre la méthode, pas juste copier des prompts

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.

MCP is the protocol powering the next generation of AI agents, and this is the only book that teaches you all of it. From Python fundamentals to low-level SSE transport, go from zero to production-ready MCP developer.

O guia definitivo do Cursor, a IDE nativa de IA usada por metade do Fortune 500. Do tab completion ao Modo Agente, do .cursorrules ao Composer — 14 capítulos cobrindo tudo que desenvolvedores precisam para dominar a IDE que pensa com você.

Crie apps reais sem escrever código. Guia passo a passo de vibe coding com Cursor, Bolt.new, Replit e v0 — do primeiro app ao deploy em produção. Para empreendedores, designers e qualquer pessoa com ideias.

The definitive guide to Cursor, the AI-native IDE used by half the Fortune 500. From tab completion to Agent Mode, from .cursorrules to Composer — 14 chapters covering everything developers need to master the IDE that thinks with you.