Computers and Programming

Behind every secure network lies an unbreakable mathematical truth. 'The Guardian Algorithms' bridges the gap between pure math and cutting-edge cyber defense. Step into the mind of a digital architect to decode the formulas that outsmart hackers, lock down infrastructure, and rewrite the rules of modern warfare. The future of security isn't coded—it is calculated.

Every web application has security headers, cookies, TLS configurations, and CORS policies that need to be correct. Most teams find out they're wrong only after a penetration test — or worse, after an incident. This book shows you how to build a scanner that catches these issues automatically. You'll create a Python CLI tool that uses Hurl (declarative HTTP testing) to assert security properties, SSLyze to validate TLS protocol versions, and AI to generate specific remediation for every failure it finds. The output: structured JSON reports, visual HTML dashboards, and AI-generated security posture assessments. **What you'll build:** - 9 declarative Hurl security test files (headers, cookies, CORS, HSTS, CSP, CSRF, redirects, TLS) - A preprocessor that handles domain substitution and authentication - An output parser with built-in remediation guidance for every test - A TLS scanner validating SSL 2.0/3.0 disabled and TLS 1.2/1.3 enabled - An AI analyzer that generates context-specific fixes using Ollama, OpenAI, or Bedrock - An HTML reporter with dark-themed visual dashboards - Docker multi-architecture deployment - CI/CD pipeline with GitHub Actions **Who this is for:** - Developers who want security checks before deploying - DevSecOps engineers building CI/CD security gates - Penetration testers who need quick baseline assessments - Security students learning web vulnerability concepts hands-on - Platform teams enforcing security standards across services Every failure includes: what was expected, what was received, why it matters, how to fix it, and a documentation link. AI adds domain-specific code examples on top.

Security threat modeling is expensive ($5K–$20K per engagement), slow (2–5 days), and requires rare expertise. What if you could automate it? This book shows you how to build AITM — an open-source Python tool that uses Large Language Models to generate comprehensive STRIDE threat analyses from a simple system description. One command. 30 seconds. Professional results. You'll build every component from scratch: - A stateless threat modeling engine with a 3-step AI workflow - Multi-provider LLM integration (Amazon Bedrock, OpenAI, Ollama) - Structured output parsing with Pydantic — no regex, no fragile parsing - Architecture diagram analysis using vision models - A professional CLI with progress indicators and colored output - Markdown and JSON report generation Whether you're a developer automating security reviews, a DevSecOps engineer integrating threat modeling into CI/CD, or a student learning how to build real-world LLM-powered tools — this book gives you the complete blueprint. Includes 5 real-world use cases, full source code on GitHub, and step-by-step instructions that work on macOS and Linux. No security expertise required. Just Python and curiosity.

A practical .NET/C# guide to building Genetic Algorithms from scratch and applying them to real-world optimization problems with 27 complete projects, visual demos, and full source code.

A fundamental architectural manifesto on AI behavioral safety and the transition from "word generation" to "state synchronization." Stop building "smart calculators"—start building reliable environments.

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The future of Artificial Intelligence is connected.From social networks and recommendation engines to autonomous vehicles and cybersecurity systems, modern AI increasingly relies on understanding relationships rather than isolated data points.How do Graph Neural Networks learn from complex networks?How do recommendation systems predict user preferences?How can AI detect fraud, misinformation, and cyber threats using graph structures?How will future Graph Foundation Models transform machine intelligence?Graph Theory with AI Applications: Foundations, Algorithms, and Modern Neural Approaches (VOL-2) provides a comprehensive guide to the technologies driving the next generation of AI.Explore Graph Neural Networks, graph embeddings, knowledge graphs, explainable AI, distributed graph learning, and cutting-edge research topics that are reshaping artificial intelligence.Whether you are a student, researcher, educator, or AI professional, this book will help you understand how intelligent systems learn from relationships, networks, and connected data.Learn the science behind Graph AI. Build the intelligence behind tomorrow's connected world.

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.

Ship a GitHub issue-triage agent from laptop PoC to production - one chapter at a time, fixing real failure modes with contracts, evals, and rollback plans. Built on Pydantic AI for engineers who already know how to ship software and need agents that behave the same way.

Are you preparing for SSC, CGL, Banking, UGC NET, Railways, Insurance, or other competitive examinations?Do you struggle with reasoning questions, logical puzzles, or aptitude-based problems under time pressure?Mastering Math & Reasoning for SSC, CGL, UGC & Banking Exams provides a complete roadmap to improve your problem-solving skills and examination performance.Inside this book, you will learn:✓ Analogy and Classification Techniques✓ Syllogism and Venn Diagram Methods✓ Coded Equations and Logical Patterns✓ Blood Relations and Direction Sense✓ Arithmetic Reasoning✓ Visual Reasoning and Paper Folding✓ Alphabet Tests and Series Analysis✓ Shortcut Techniques for Faster Problem Solving✓ Hundreds of Practice Questions with Solutions✓ Exam-Oriented Strategies for Accuracy and SpeedWritten in a simple and systematic style, this book helps candidates build confidence, strengthen logical thinking, and improve examination performance across multiple competitive examinations.Whether you are a beginner or an advanced aspirant, this guide provides the concepts, strategies, and practice needed to achieve success.

Closures, this, the event loop, async: the five topics that make people quit JavaScript, finally explained, so they click. Learn them all by building a retro arcade, one machine at a time.

Pull a model onto a machine you own, shape it with a Modelfile, fine-tune your own adapter, and build a chat app that calls tools and talks to an MCP server, all running on your own hardware. By the end, you'll know exactly where owning your AI beats renting it, and where it doesn't.

Master copywriting by doing it: 16 modules, 35+ templates, 16 worksheets, and a full portfolio project — learn to write copy that gets read, clicked, and acted on.

Certainty feels safe - but in an uncertain world, it's a bug, not a feature. A Data Engineer shows how the craving for absolute truth is a form of "overfitting," and how thinking in probabilities makes you more creative, resilient, and right more often.