EdgeWhisper runs Voxtral Mini 4B Realtime (Mistral AI, Apache 2.0) locally on Apple Silicon via the MLX framework. Hold a key, speak, release — text appears at your cursor in whatever app has focus.

Architecture: - Native Swift (SwiftUI + AppKit). No Electron. - Voxtral 4B inference via MLX on the Neural Engine. ~3GB model, runs in ~2GB RAM on M1+. - Dual text injection: AXUIElement (preserves undo stack) with NSPasteboard+CGEvent fallback. - 6-stage post-processing pipeline: filler removal → dictionary → snippets → punctuation → capitalization → formatting. - Sliding window KV cache for unlimited streaming without latency degradation. - Configurable transcription delay (240ms–2.4s). Sweet spot at 480ms.

What it does well: - Works in 20+ terminals/IDEs (VS Code, Xcode, iTerm2, Warp, JetBrains). Most dictation tools break in terminals — we detect them and switch injection strategy. - Removes filler words automatically ("um", "uh", "like"). - 13 languages with auto-detection. - Personal dictionary + snippet expansion with variable support ({{date}}, {{clipboard}}). - Works fully offline after model download. No accounts, no telemetry, no analytics.

What it doesn't do (yet): - No file/meeting transcription (coming) - No translation (coming) - No Linux/Windows (macOS only, Apple Silicon required)

Pricing: Free tier (5 min/day, no account needed). Pro at $7.99/mo or $79.99/yr.

I'd love feedback on: 1. Would local LLM post-processing (e.g., Phi-4-mini via MLX) for grammar/tone be worth the extra ~1GB RAM? 2. For developers using voice→code workflows: what context would you want passed to your editor? 3. Anyone else building on Voxtral Realtime? Curious about your experience with the causal audio encoder.