Compose Multiplatform makes it easy to build cross-platform desktop apps with Kotlin and Compose, but what about native APIs, like iCloud on macOS? Accessing such APIs isn't possible through the regular Compose Multiplatform toolchain. However, with a bit of "magic", we can turn dreams (or feature requests) into reality.\n\nIn this talk, we'll explore how to combine Kotlin/Native and the JNI (Java Native Interface) to bridge the gap between a JVM-based UI and native system features. We'll write Kotlin code, compile it into a native library, and call it back from Kotlin.\n\nYou'll learn how to build Kotlin/Native code into a native macOS dynamic library and integrate it into a Compose Multiplatform desktop app, unlocking access to iCloud and enabling features like backup and restore for your app’s data.

AI Agents and using them for AI-assisted development can be powerful, save a lot of time, and shrink the time-to-value horizons. They even allow you to automate things that you would have never considered before. However, there are gotchas: code quality, architecture, code review, testing, and actual customer need. These concepts become real bottlenecks in the end-to-end business & customer value delivery via software development. In this talk, you’ll learn about the nature of the problem and how to mitigate and even overcome these challenges.

In this talk, we’ll explore how Compose Multiplatform can reshape not just the user interface, but the entire architecture of your app. You’ll learn techniques for structuring state, managing business logic, and creating modular, testable, and maintainable systems across platforms. Whether you’re targeting mobile, desktop, or beyond, this session will give you the tools and perspective to design applications that exploit the efficacy of Compose.

This in-depth guide covers a wide range of topics, including chapters on linear algebra, root finding, curve fitting, differentiation and integration, solving differential equations, random numbers and simulation, a whole suite of unconstrained and constrained optimization algorithms, statistics, regression and time series analysis. The mathematical concepts behind the algorithms are clearly explained, with plenty of code examples and illustrations to help even beginners get started. In this book, you'll implement numerical algorithms in Kotlin using NM Dev, an object-oriented and high-performance programming library for applied and industrial mathematics. Discover how Kotlin has many advantages over Java in its speed, and in some cases, ease of use. In this book, you’ll see how it can help you easily create solutions for your complex engineering and data science problems. After reading this book, you'll come away with the knowledge to create your own numerical models and algorithms using the Kotlin programming language. What You Will Learn Program in Kotlin using a high-performance numerical library Learn the mathematics necessary for a wide range of numerical computing algorithms Convert ideas and equations into code Put together algorithms and classes to build your own engineering solutions Build solvers for industrial optimization problems Perform data analysis using basic and advanced statistics Who This Book Is For Programmers, data scientists, and analysts with prior experience programming in any language, especially Kotlin or Java.