Books

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.

A hands-on guide to downloading, running, serving, and maintaining open-weight LLMs on your own machine (489 manuscript pages).

Prompting is a design problem, not a syntax problem. Prompt Patterns introduces durable patterns for reliable work with large language models.

Pedagogical Philosophy of the BookThis book is designed with three guiding principles:1.     Clarity over Formalism While maintaining mathematical accuracy, the book avoids unnecessary formalism that can confuse beginners. Instead, it uses intuitive explanations, diagrams, and real-world analogies.2.     Integration of Computation Every mathematical concept is tied to computational practice. Readers are encouraged to implement simple code snippets (in Python, NumPy, or similar tools) to reinforce their understanding.3.     Balance Between Breadth and Depth The book covers the essential calculus concepts in sufficient depth to support AI applications, without delving into overly abstract branches that have limited relevance to machine learning. Who Should Read This Book?·        Students of Computer Science, Data Science, and AI – who want to strengthen their mathematical foundation for advanced courses and projects.·        Researchers in AI – who need a refresher or structured guide to connect calculus with modern algorithms.·        Industry Professionals and Engineers – who want to move beyond using libraries like TensorFlow or PyTorch blindly and instead gain an understanding of the mathematics behind the models.·        Educators – who seek a resource that connects abstract mathematics with practical AI examples for teaching purposes.Benefits of Studying This Book1.     Builds Mathematical Confidence – Readers who once found calculus intimidating will discover a fresh, accessible perspective tailored for AI.2.     Enables Deeper Understanding of Algorithms – Going beyond “black box” usage of AI tools, readers will understand why models work.3.     Enhances Problem-Solving Skills – By mastering calculus-driven optimization, readers can design new models and improve existing ones.4.     Supports Academic and Career Growth – Mastery of calculus strengthens research capabilities, technical interviews, and advanced study opportunities.5.     Encourages Critical Thinking – Rather than rote memorization, the book fosters curiosity about the connections between mathematics and intelligent systems. The Long-Term VisionArtificial Intelligence is not just a passing trend—it is shaping the future of science, technology, and human society. Calculus, as a timeless branch of mathematics, ensures that learners have the intellectual tools to adapt to new paradigms. As AI expands into quantum computing, neuroscience-inspired architectures, and beyond, the reliance on calculus will remain unshaken.This book provides readers not just with knowledge, but with intellectual independence—the ability to reason about algorithms, derive insights, and innovate confidently.   

A hands-on guide to building with Claude Code, MCP, the Claude API, and the Agent SDK. From mental models to production architectures — everything an experienced developer needs to work agentically, not just use AI tools.

All examples in Hy. The Hy language (Lisp that compiles to Python) allows Lisp programmers access to the rich Python ecosystem for Large Language Models, deep learning, artificial intelligence, and general data wrangling. Applications: LangChain, Knowledge Graphs, NLP, Deep Learning.

Most asyncio tutorials introduce async and await on page one and ask you to take the runtime on faith. This book does it the other way around. You build a working event loop in thirty lines of plain Python — no asyncio import — and meet the keywords as labels for parts of a runtime you have already watched run. By the end, async Python stops being intimidating and starts being readable.

AWS replaced Amazon Q with Kiro — an AI IDE that writes specs before code. 14 chapters covering spec-driven development, AI agents, hooks, MCP integrations, AWS deployment, and the honest comparison with Cursor and Copilot. The definitive guide.

Architecture. Teams. Business. Aligned. Finally, a guide that connects the dots. From Team Topologies to Flexible Project Management, learn how to build systems that align with your business goals instead of fighting against them. Stop estimating and start engineering your path to confidence.

Modern cybersecurity is no longer just about firewalls and antivirus. It is about architecture, governance, secure software delivery, cloud resilience, Zero Trust, AI security, and operational discipline.The Master Guide to Cyber Security brings these domains together into one practical enterprise-focused reference designed for modern security professionals, architects, engineers, and technology leaders.Built around real-world frameworks, secure-by-design principles, and current threat realities, this guide provides a structured roadmap for building secure systems in cloud-native and enterprise environments.

Master Codex CLI in Minutes, Not Hours Stop guessing. Stop scrolling through docs. This cheat sheet gives you everything you need to use Codex CLI like a pro — from model selection and cost control to MCP servers, hooks, and multi-session workflows.

"The AI confirmed X for me" used as proof of X. Outputs that sound brilliant but don't hold up to a severe re-reading. A three-page "AI policy" that nobody reads. Sound familiar? Thinking with LLMs, the Right Way is the system of critical thinking applied to LLMs: the Thinking-With Triangle (Intent / Adversary / Editor), the four meta-decisions of governance, the Socratic and adversarial practices for investigating and verifying. Not prompt engineering: the method for not letting yourself be mirrored.

Kubernetes exposes plenty of state, but operators still have to reconstruct operational meaning from scattered Pods, Services, EndpointSlices, Events, PVCs, owner references, and status fields. This ebook uses `kctx`, a small read-only Kubernetes context engine, to show how deterministic entities, relations, signals, graphs, namespace snapshots, CRD adapters, and stable JSON contracts can turn raw cluster data into reusable context for humans, tools, and AI agents. It is written for SREs, platform engineers, Kubernetes operators, infrastructure developers, and AI tooling builders who want better primitives than raw YAML and improvised troubleshooting pipelines.

Are you interested in learning about graph theory and applied network analysis, leveraging your Python skills? Then this is the book for you! See how network science & graph theory connects with a variety of data analysis problems, and use it to solve your next data science challenge!

A beginner-friendly introduction to machine learning with Python, that is based on the PyCaret and Streamlit libraries. Readers will delve into the fascinating world of artificial intelligence, by easily training and deploying their ML models!