Catalyst Patterns for AI Mac Apps

Most AI tools on macOS are Electron apps or web wrappers. They work, but they feel foreign. They don't respect system appearance settings correctly. They don't integrate with the menu bar properly. They don't support native drag and drop, Services menu, or Spotlight integration. They feel like web pages pretending to be applications.

Mac users notice. The Mac platform has strong conventions about how applications should look and behave, and users who chose macOS chose it because they value those conventions. An AI tool that respects them earns trust and adoption. An AI tool that ignores them feels like it was built for a different platform and ported as an afterthought.

Building a native-feeling AI application on macOS doesn't require writing everything in AppKit. Mac Catalyst (bringing iPad apps to Mac) and SwiftUI (cross-platform declarative UI) both produce Mac-native applications with appropriate conventions. The key is understanding which Mac patterns matter and how to implement them.

Key Takeaways

• Sidebars with NavigationSplitView provide the classic three-column Mac layout for AI tools with multiple contexts
• Toolbar integration puts frequently used AI actions in the window chrome where Mac users expect them
• Menu bar structure with proper keyboard shortcuts follows macOS Human Interface Guidelines
• Drag and drop enables natural file and text input to AI skills
• System integration (Services, Spotlight, Quick Actions) makes AI capabilities accessible system-wide

The Three-Column Layout

The three-column layout (sidebar + list + detail) is the canonical Mac application pattern. Mail, Notes, Finder, and dozens of other apps use it. For AI applications, the columns map naturally:

• Sidebar: Skill categories, conversation threads, project contexts
• List: Individual skills, messages, documents within the selected category
• Detail: The active skill interface, conversation content, or document view

SwiftUI's NavigationSplitView implements this pattern directly:

NavigationSplitView {
    SkillCategorySidebar(selection: $selectedCategory)
} content: {
    SkillList(category: selectedCategory, selection: $selectedSkill)
} detail: {
    SkillDetailView(skill: selectedSkill)
}

The three-column layout provides information architecture that users understand instinctively. They know the sidebar provides navigation, the list provides selection, and the detail provides content. No tutorial needed.

For AI applications that manage multiple concurrent conversations or skill executions, the three-column layout keeps everything accessible without tab proliferation or window management overhead.

Toolbar Patterns

Mac toolbars sit at the top of the window, integrated with the title bar. They provide quick access to common actions without menu diving.

For AI applications, toolbar items should include:

Primary action. A prominent button for the most common action ("New Query," "Run Skill," "Start Conversation"). This should be visually distinct, consistent with the design principles in the AI Skill Market design system.

Context controls. Model selection, temperature slider, or other parameters that affect the current operation. These belong in the toolbar because they're frequently adjusted.

Status indicator. A compact display showing whether the AI is idle, processing, or waiting for input.

Search. A search field for finding skills, conversations, or content. Mac users expect search in the toolbar.

Toolbar items should use SF Symbols for icons, which ensures visual consistency with the operating system. Custom icons should match SF Symbol weight and style.

Proper Menu Structure

A Mac application without a proper menu bar is immediately identifiable as a port. The menu bar is not optional on macOS. It's the primary command interface for keyboard-driven workflows and the place Mac users look first for capabilities.

An AI application's menu structure should include:

File menu: New Conversation, Open Skill, Save Conversation, Export Results

Edit menu: Standard Cut/Copy/Paste plus AI-specific options like "Copy Last Response," "Paste as Query"

View menu: Sidebar toggle, Appearance options, Font size

Skills menu: Browse Skills, Recent Skills, Manage Installed Skills

AI menu: Model selection, Parameters, API configuration

Window menu: Standard window management

Every menu item should have a keyboard shortcut. Mac users memorize shortcuts for frequent actions and expect them to work. An AI application where Cmd+N does nothing feels broken.

Drag and Drop

Mac applications support drag and drop as a primary interaction pattern. For AI tools, this enables:

File input. Drop a file onto the AI application to analyze it. PDFs, images, code files, and data files can all be processed by appropriate skills.

Text input. Select text in any application, drag it to the AI tool, and it becomes the query or input context.

Output reuse. Drag AI-generated output (code, text, images) from the AI application into other applications.

Implementing drag and drop properly requires:

• Accepting standard UTIs (Uniform Type Identifiers) for files and text
• Providing visual feedback during the drag (highlight the drop target)
• Handling the drop asynchronously (don't block the UI while processing)
• Supporting both inter-app and intra-app drag and drop

Services Menu Integration

The macOS Services menu provides system-wide access to application capabilities. An AI application that registers as a service allows users to:

1. Select text in any application
2. Right-click and choose "AI > Summarize" (or similar) from the Services menu
3. See the AI result in a notification or floating panel

This integration makes AI capabilities available everywhere without requiring the user to switch to the AI application. It's one of the most powerful Mac-specific integrations available to AI tool developers.

Register services for common AI operations: Summarize, Translate, Explain, Rewrite. Each service takes the selected text as input and produces output through a notification, clipboard replacement, or floating panel.

Spotlight and Quick Actions

Spotlight integration makes AI skills searchable through the system search interface. Users type a skill name in Spotlight and launch it directly. This requires implementing Core Spotlight indexing for your installed skills.

Quick Actions (accessible through the Finder's Preview pane and right-click menus) enable AI processing of files without opening the AI application. "Analyze Code," "Summarize Document," or "Extract Data" can be Quick Actions that process selected files and deliver results.

Both integrations make AI skills feel like system capabilities rather than features of a specific application. This level of integration is exclusive to native Mac applications and impossible for web-wrapped alternatives.

Performance on Apple Silicon

Native Mac applications on Apple Silicon have significant performance advantages over Electron and web-wrapped alternatives:

Memory efficiency. A native SwiftUI application uses 5-10x less memory than the equivalent Electron application. For AI tools that also load ML models, this memory savings is critical.

CPU efficiency. Native code compiled for Apple Silicon runs directly on the M-series cores without the overhead of V8 or JavaScriptCore. AI tools that perform local preprocessing or postprocessing benefit directly.

Neural Engine access. Only native applications using Core ML can access the dedicated ML hardware in Apple Silicon. Web applications and Electron apps use CPU or GPU inference, which is slower and less power-efficient.

For detailed optimization strategies, see Apple Silicon Optimization for AI and CI/CD on Apple Silicon With AI.

When to Use Catalyst vs. SwiftUI vs. AppKit

SwiftUI is the right choice for new AI applications that target macOS 13+ and don't need deep AppKit integration. SwiftUI's declarative model works well for AI interfaces, and Apple continues to close the gaps between SwiftUI and AppKit capabilities.

Mac Catalyst makes sense when you have an existing iPad application that you want to bring to Mac. Catalyst provides Mac conventions (menu bar, toolbar, keyboard shortcuts) with minimal code changes from the iPad version.

AppKit is the right choice when you need capabilities that SwiftUI doesn't yet support: custom NSViews, complex text editing with NSTextView, fine-grained control over window behavior, or integration with older frameworks.

Most AI applications should start with SwiftUI and drop into AppKit for specific capabilities using NSViewRepresentable. The SwiftUI readiness article evaluates the current state of SwiftUI for AI applications.

FAQ

Is Catalyst still relevant with SwiftUI available?

For new projects, SwiftUI is generally preferred. Catalyst remains relevant for teams with existing iPad codebases that want Mac distribution with minimal effort. If you're starting fresh, SwiftUI gives you Mac and iPad from a single codebase without Catalyst's compromises.

How do I handle large AI model files in a Mac app?

Use on-demand resources for models that aren't needed immediately. Store frequently used models in the application support directory. For models larger than 100MB, consider downloading them at first launch rather than bundling them in the app bundle.

Can a menu bar app also have a main window?

Yes. Many Mac apps combine a menu bar presence with a full window for extended interactions. The menu bar provides quick access and status. The main window provides the full interface. Use NSApp.setActivationPolicy to switch between accessory (menu bar only) and regular (menu bar + dock icon) modes.

Should I distribute through the Mac App Store or directly?

The Mac App Store provides distribution and update infrastructure but imposes sandboxing restrictions that may limit AI tool capabilities. Direct distribution via notarization provides full system access but requires your own update mechanism. Many AI tools ship directly because they need system-level access that sandboxing restricts.

Sources

• macOS Human Interface Guidelines - Apple
• Mac Catalyst Documentation - Apple Developer
• SwiftUI on macOS - Apple Developer
• Core ML Documentation - Apple Developer

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