Parlor On Device Ai

This gets a full multimodal voice assistant running locally with no API costs. Gemma 4 E2B handles speech and vision understanding, Kokoro TTS speaks back, and everything stays on device via a FastAPI WebSocket server. The architecture is clean: Silero VAD in the browser detects when you're talking, sends audio plus camera frames over WebSocket, and streams sentence-level TTS back so you hear responses before they're fully generated. Runs on Apple Silicon with MLX or Linux with ONNX. Expect around 2.5-3 seconds end to end on an M3 Pro. You can interrupt mid-sentence, tweak the system prompt to change behavior, and adjust VAD sensitivity. Good for prototyping private voice interfaces or experimenting with on-device inference without racking up cloud bills.

skills.sh installs: 588
GitHub stars: 61
last updated: Jul 14, 2026

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Install

$ npx -y skills add aradotso/trending-skills --skill parlor-on-device-ai --agent claude-code

Installs into .claude/skills of the current project.

Files

Parlor On-Device AI

Skill by ara.so — Daily 2026 Skills collection.

Parlor is a real-time, on-device multimodal AI assistant. It combines Gemma 4 E2B (via LiteRT-LM) for speech and vision understanding with Kokoro TTS for voice output. Everything runs locally — no API keys, no cloud calls, no cost per request.

Architecture

Browser (mic + camera)
    │
    │  WebSocket (audio PCM + JPEG frames)
    ▼
FastAPI server
    ├── Gemma 4 E2B via LiteRT-LM (GPU)  →  understands speech + vision
    └── Kokoro TTS (MLX on Mac, ONNX on Linux)  →  speaks back
    │
    │  WebSocket (streamed audio chunks)
    ▼
Browser (playback + transcript)

Key features: