Building a Local Voice-to-Text macOS App with Claude Code: Vext Case Study
A developer shared their experience building Vext—a native macOS voice-to-text app that runs entirely on-device using Whisper on the Apple Neural Engine. No cloud, no accounts, no subscription. The app uses a Rust core with Swift/SwiftUI UI and Core ML for inference, with Claude Code as the primary coding partner.
Key Features
- Hold a hotkey anywhere → speak → release → text appears at cursor
- Transcribes 60 seconds of audio in ~400ms (150x real-time)
- Smart cleanup: removes filler words, restructures speech for readability
- Real-time translation to 99+ languages
- Meeting transcription with speaker diarization + auto-summaries
- Screen recording during voice recordings (auto-attaches screenshots)
Claude Code Wins
- Whisper on Apple Silicon: Helped iterate through quantization strategies, model chunking, and memory layout for Core ML conversion to run efficiently on the Neural Engine.
- Hotkey system architecture: Suggested using a CGEventTap with proper accessibility permissions, and helped debug race conditions between recording start/stop and clipboard injection.
- Rust ↔ Swift FFI: Generated FFI bindings and caught several memory safety issues in the C interface layer.
Claude Code Limitations
- Struggled with macOS-specific API nuances not well-documented online—CGEventTap edge cases required digging into Apple's headers directly.
- Context window became a bottleneck across the full Rust + Swift codebase; the developer split the project into modules and worked on one at a time.
Pricing
Free to download and try at getvext.app. $49 one-time to keep it (no subscription). Code VEXT50 for 50% off.
📖 Read the full source: r/ClaudeAI
👀 See Also
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