how can I get started with musickit on macOS?
Asked on 2026-01-12
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To get started with MusicKit on macOS, you would typically begin by exploring the relevant sessions from Apple's WWDC that cover MusicKit or related frameworks. Unfortunately, the provided context does not include specific information about MusicKit. However, you can generally start by visiting the Apple Developer website to access documentation, sample code, and other resources related to MusicKit.
If you are interested in other frameworks or tools mentioned in the context, such as MLX for machine learning on Apple Silicon, you can refer to the session "Get started with MLX for Apple silicon" which provides an overview and key features of MLX. You can find more information in the session chapters:
For more specific guidance on MusicKit, I recommend checking the latest WWDC sessions or Apple's official documentation for updates and tutorials.
What’s new in AppKit
Discover the latest advances in Mac app development. Get an overview of the new features in macOS Sequoia, and how to adopt them in your app. Explore new ways to integrate your existing code with SwiftUI. Learn about the improvements made to numerous AppKit controls, like toolbars, menus, text input, and more.
Enhance child safety with PermissionKit
Discover how PermissionKit helps you enhance communication safety for children in your app. We’ll show you how to use this new framework to create age-appropriate communication experiences and leverage Family Sharing for parental approvals. You’ll learn how to build permission requests that seamlessly integrate with Messages, handle parental responses, and adapt your UI for child users. To get the most out of this session, we recommend first watching “Deliver age-appropriate experiences in your app” from WWDC25.
Get started with MLX for Apple silicon
MLX is a flexible and efficient array framework for numerical computing and machine learning on Apple silicon. We’ll explore fundamental features including unified memory, lazy computation, and function transformations. We’ll also look at more advanced techniques for building and accelerating machine learning models across Apple’s platforms using Swift and Python APIs.