Vibe Engineering

Discover the mathematics powering the future of Artificial Intelligence and cybersecurity.Algebraic Foundations of AI: Groups, Rings, and Fields in Security & Cryptography reveals how abstract algebra forms the backbone of modern AI security, cryptographic systems, blockchain technologies, privacy-preserving machine learning, federated learning, and post-quantum cryptography.

A hands-on guide to designing, building, testing, and deploying secure stdio and SSE MCP servers in Python and TypeScript (447 manuscript pages).

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.

Linear Programming and AI Optimization Models (VOL-3) delivers practical mastery in scheduling, supply chain, logistics, hybrid AI-OR systems, large-scale cloud optimization, and emerging technologies. With Python implementations, industry case studies, and forward-looking research trends, this volume turns theory into powerful real-world solutions.

Take your optimization skills to the next level with AI.Linear Programming and AI Optimization Models (VOL-2) dives deep into the algorithms driving modern Machine Learning and Intelligent Systems. Master Gradient Descent, Adam, Reinforcement Learning, Genetic Algorithms, Particle Swarm Optimization, Constraint Satisfaction, and advanced Network Models.

Unlock the power of optimization in the age of AI."Linear Programming and AI Optimization Models" delivers a masterful blend of classical Operations Research and modern Machine Learning applications. From the elegant Simplex Method to advanced decomposition techniques and KKT conditions, this Volume-1 builds a rock-solid foundation while demonstrating how these powerful algorithms .

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.

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.

Master DeepSeek V3 — one of the most capable AI models of its time.This practical guide teaches students, researchers, and professionals how to use DeepSeek V3 for learning, research, coding, content creation, automation, and productivity — with strong focus on prompt engineering and responsible AI usage.

Unlock the power of Llama 4 — the next generation of open-weight multimodal AI.This practical guide shows students, researchers, and professionals how to harness advanced AI tools for learning, research, teaching, and productivity. From generating study notes and research ideas to building educational chatbots and optimizing workflows, discover how Llama 4 can transform the way you work and learn

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.

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.

# 🚀 Master the Math Behind Artificial Intelligence!Stop drowning in complex textbooks. This premium, high-density **AI Math Cheat Sheet** is engineered specifically for Data Scientists, ML Engineers, and Python Developers who want to bridge the gap between mathematical theory and production-ready co

A practical guide to operating a fleet of AI coding agents through routing, memory, skills, MCP, guardrails, and a persistent control plane (322 manuscript pages).

Mastering Deep Learning with PyTorch: From Fundamentals to Real-World Projects This first edition delivers a complete end-to-end learning pathway for mastering modern deep learning using PyTorch. Major Topics Covered • Deep Learning Fundamentals• Artificial Neural Networks• PyTorch Framework and Tensor Operations• Automatic Differentiation (Autograd)• Feedforward Neural Networks• Convolutional Neural Networks (CNNs)• Recurrent Neural Networks (RNNs)• Long Short-Term Memory Networks (LSTMs)• Attention Mechanisms• Transformer Architectures• Hugging Face Ecosystem• Generative Adversarial Networks (GANs)• Computer Vision Applications• Natural Language Processing Applications• Model Evaluation and Optimization• Hyperparameter Tuning• Explainable Artificial Intelligence (XAI)• Ethical AI and Bias Mitigation• Model Deployment and Production Pipelines Practical Implementations Included • Image Classification Systems• Object Detection Models• Image Segmentation Applications• Text Classification Systems• Sentiment Analysis Models• Language Translation Pipelines• Transformer-Based NLP Applications• GAN-Based Image Generation Capstone Projects Project 1: Pneumonia Detection using CNNProject 2: Sentiment Analysis using LSTMProject 3: Image Colorization using GANProject 4: Real-Time Object Detection SystemProject 5: Transformer-Based Intelligent Chatbot Industry Tools and Technologies • PyTorch• TorchVision• Hugging Face Transformers• TensorBoard• Flask• ONNX• Docker Concepts• AWS Deployment Basics• Google Cloud Deployment Concepts Intended Audience • Undergraduate Students• Postgraduate Students• Data Scientists• Machine Learning Engineers• AI Researchers• Software Developers• Academic Professionals• Industry Practitioners Learning Outcomes Upon completion of this book, readers will be able to:• Design and train neural network architectures.• Build computer vision applications using CNNs.• Develop NLP solutions using RNNs, LSTMs, and Transformers.• Implement generative AI systems using GANs.• Evaluate and optimize deep learning models.• Deploy PyTorch models into production environments.• Understand ethical considerations in AI development.• Create portfolio-ready deep learning projects.This release establishes a strong foundation for academic learning, industrial applications, and advanced research in modern deep learning.