Books

How first-order PDEs propagate data along characteristics, from linear to fully nonlinear settings with explicit step-by-step solutions throughout.

The author, Kumar Eswaran's younger brother, who had followed his proof from its inception, has presented it here without dilution and all its antecedent history so that it can be understood by undergraduates. It is likely to be the most interesting book on higher mathematics you have ever read.

Learn the theory with short Scala code examples.

The tanatasa.SG Beauty Glow Journal is a practical skincare and beauty ebook created for Singapore’s hot, humid, air-conditioned lifestyle. It covers everyday topics like sunscreen, sensitive skin, retinol, dry skin, ingredients, and smarter beauty shopping online. Written in a clear and easy-to-understand style, this guide is for anyone who wants more confident skincare choices without complicated routines, confusing ingredient fear, or beauty hype.

A 24-year game design veteran's field manual for putting generative AI to work in real production — with every prompt, every line of code, and every verification step disclosed.

Gen AI on ungoverned data is a liability. This book hands you the complete toolkit to make data ownership the bedrock of compliance and Gen AI — GDPR lifecycle governance, Knowledge Graphs for accurate RAG, and EU data residency blueprints.

Si alguna vez pensaste que la ciberseguridad debería ser más que ethical hacking y que ISO 27001, entonces este libro es para ti. Contiene sólo aquello que ha probado ser útil y práctico, incluyendo estudios de casos y ejemplos reales. Una guía de recuerdo para el CISO experimentado, pero también una breve introducción para el CISO principiante.

Artificial Intelligence is transforming the world—but it is also creating an entirely new attack surface.What happens when attackers manipulate AI through language itself?Discover how cybercriminals exploit prompt injections, jailbreak models, steal sensitive information, and bypass AI guardrails. Through real-world examples, hands-on labs, secure coding techniques, and enterprise-ready architectures, Mastering LLM Security equips you with the knowledge needed to defend the next generation of intelligent systems.Whether you are building AI products, securing enterprise deployments, or leading cybersecurity programs, this book provides the blueprint for defending modern AI infrastructures.

Migrate existing, high-traffic production Android and iOS applications to Kotlin Multiplatform (KMP) and Compose Multiplatform (CMP) systematically—without pausing feature development or compromising release stability.

The Polymorph Data Language (PDL) is a versatile, compact and fast alternative to JSON, YAML, XML and CSV. PDL supports streams of fields, objects, compact objects, cyclic object graphs, tabular data, table trees, and custom instructions. PDL supports this with a syntax that is simple to tokenize and parse and which gives good performance during tokenization and parsing. PDL's custom instructions can be used to implement a whole Scheme-like programming language, should you want to.

This short book introduces process data scientists to knowledge graphs and GraphRAG, a powerful combination that overcomes the limitations of vanilla RAG when answering the kind of cross-document, relationship-heavy questions that process engineers usually care about. With a hands-on, application-driven approach, the book walks readers through the complete pipeline: designing an ontology for plant data, using LLMs to extract entities and relationships from incident reports, building and querying the graph in Neo4j, and finally assembling everything into a demo web application.

Code Is the Side Effect"Software engineers are not primarily code writers. We are clarity traders — and that hasn't changed."You've seen the demos. The AI builds a whole feature from a sentence. The agent writes tests, fixes the failing ones, opens the PR. It's remarkable.Then you come back three months later. The codebase is a tangle. Nobody knows why anything is the way it is. The agent that built it has no memory of what it decided or why. And every time you ask it to add something new, it breaks two things you didn't know were connected.This is the pattern that nobody talks about. AI coding tools make the easy parts of engineering dramatically easier. They leave the hard parts untouched — and they create new hard parts that didn't exist before.Ways of Working is the book for engineers who want to work with AI agents rather than be gradually replaced by them — who understand that the tools are genuinely powerful and genuinely limited, and want to build practices that get the most from each.What you will actually learnThe world model framework. Before an agent can build anything well, it needs to understand what it's building and why. This book teaches you to give agents what they need: a structured, queryable representation of your architecture, your component contracts, your behavior specifications, and your code patterns. No world model = no sustained agentic development.Intent documentation. The most expensive bug in agentic codebases is not a hallucination — it's a decision made without context. Why is this rule here? Why is this boundary where it is? Agents can't infer rationale from code. You have to write it down.Spec-Kit and formal specifications. GitHub's Spec-Kit brings machine-readable, traceable, CI-verified specifications to engineering teams. This book shows how to use it to turn requirements into agent inputs that are precise enough to generate correct implementations.Graph explainers. Tools like Graphify and Understand-Anything transform codebases and documents into queryable knowledge graphs — giving agents navigable context instead of flat text. This is the memory substrate that makes multi-agent systems reliable at scale.Agent architecture that holds. What makes an agent coherently itself? When do file-based agent systems break down and what replaces them? How does constraint-based coordination (borrowed from holocracy) solve the autonomy-coherence problem that has stumped AI researchers for decades?Claude Code, for real. A complete treatment of Claude Code's CLAUDE.md convention, permission model, hooks, and slash commands. Plus the oh-my-claudecode ecosystem: 15+ specialized agents, workflow orchestration patterns (autopilot, ralph, ultrawork), and the skills framework for team-specific automation.The AI-native organization. What genuine AI-native teams look like beneath the marketing. How to hire, structure, and lead them. What language-oriented programming and constrained natural language mean for the future of the human-code relationship.Who it's forEngineers who are past the "should I use AI?" question and into the "how do I use it without losing my engineering integrity?" question.Senior engineers. Engineering managers. Technical leaders. People who have noticed that the more they delegate to AI, the less certain they feel — and who want to understand why.From the AuthorI've been building production systems with AI agents for years. Not demos — systems that had to work reliably across months, maintain themselves as requirements changed, and produce outputs that engineers could understand and defend.That experience has made me skeptical in both directions.Skeptical of the "AI will do everything" vision — because I've watched too many AI-generated codebases collapse under the weight of accumulated misunderstanding.Equally skeptical of the "nothing fundamentally changed" position — because the engineers who treat AI coding tools as just faster autocomplete are making a category error they'll pay for in months of maintenance debt.Something genuinely new is happening. This book is my attempt to think about it clearly.