Books

A practical, code-first guide to physics-informed machine learning for scientists and engineers. You'll build a working Physics-Informed Neural Network from scratch in PyTorch, solve a real differential equation, and verify it against the exact solution — staying honest about both the power and the limits of the method.

Build a complete LLM inference engine in C++ — from a blankproject to a working Transformer that loads a real model andgenerates text. Forged one challenge at a time, with teststhat prove every piece works before you move on.

The latest version of Rust (1.85) has some great new features, like async closures, more stable associated function return types, and const generics that are now mature enough to underpin serious numerical libraries. The linfa and smartcore ecosystems have developed into decent classical machine learning stacks. The Burn training framework feels native to Rust, not like it's been ported from it. The Candle makes it so that loading pre-trained transformer models is more of an engineering task than a research exercise. The crates that used to need all sorts of workarounds now just work.

Turn data into intelligent decisions. Discover how machine learning works and learn to build predictive models using Python and Scikit-Learn. From data preprocessing and visualization to classification, regression, clustering, and model evaluation, this practical guide helps you develop real-world machine learning skills.

AI won’t replace people who know how to adapt—it will empower them. In a world transformed by artificial intelligence, your greatest advantage is not technology, but your ability to think critically, learn continuously, build meaningful relationships, and act with purpose. Discover the human skills that machines cannot replicate and learn how to stay relevant, productive, and future-ready.

Rewiring Software Delivery shows technology leaders how to move beyond AI tool adoption and redesign the operating model for agentic engineering. It introduces practical ways to govern autonomous execution, strengthen intent, separate generation from validation, and build a delivery system that turns AI activity into durable business advantage.

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.

Practical, safe AI for people who actually run networks.

The No-Jargon Guide for Everyday Workers Who Want to Save Time, Get Ahead, and Stop Feeling Left Behind. **91% of companies say they use AI. Only 21% of workers actually do. Close the gap in 30 days — no computer science degree required.**

From LLMs and RAG to Memory, MCP, Agent Frameworks and Enterprise AI Controls—discover how modern AI Agents work internally within today's rapidly evolving AI ecosystem.

Most AI systems can talk, but few can actually do. This book shows you how to build AI agents that reliably use tools, call APIs and automate real workflows. Using DSPy, Pydantic AI, the Claude Agent SDK, the OpenAI Agents SDK and Google ADK, you'll learn practical patterns for building reliable agents that work in production.

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.

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.

Threat modeling is broken. It takes days, costs thousands, and most teams skip it entirely. What if your AI coding assistant could do it for you — systematically, consistently, and in minutes? This book shows you how to build an MCP server that makes it happen. You'll create 80+ structured tools that guide any AI assistant through a rigorous 9-phase STRIDE threat modeling workflow. Not vague prompts that produce unstructured text — real, typed, validated tools that build up a complete threat model piece by piece: business context, architecture, threat actors, trust boundaries, data flows, STRIDE-based threats, mitigations, and a final JSON export compatible with AWS Threat Composer. **What you'll build:** - A full MCP server with FastMCP (stdio + SSE transport) - Pydantic v2 data models for type-safe threat modeling - Case-insensitive enum validation (because AI isn't always consistent) - 11 tool modules covering every phase of STRIDE analysis - Customizable organization security guidelines loaded from `.md` files - Docker deployment for team-wide access - Compliance gap analysis that validates against mandatory controls - A complete workflow orchestrator with progress tracking **What makes this different:** The server doesn't call any LLM itself. It provides the structure and tools — your AI assistant (Claude, Kiro, Cursor, Copilot) provides the intelligence. This means it works with any model, any provider, forever. No API keys, no token costs for the server itself. **Who this is for:** - Security engineers who want to automate repetitive threat modeling - Python developers building MCP servers for any domain - DevSecOps teams embedding security into AI-assisted workflows - Architects who need consistent, auditable threat models - Anyone curious about how MCP tools work under the hood **By the end of this book**, you'll have a production-ready MCP server, a deep understanding of how AI tools are structured, and transferable patterns for building MCP servers in any domain — not just security.

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.