Bridge Meta Ray-Ban microphone audio into an offline ManuAI assistant workflow.
npx skills add https://github.com/J4Joshua/manuaI --skill glasses-bridge
Please help me install this Agent Skill. Skill: glasses-bridge Source: https://github.com/J4Joshua/manuaI/tree/main/.claude/skills/glasses-bridge Command: npx skills add https://github.com/J4Joshua/manuaI --skill glasses-bridge
This Skill solves the challenge of operating ManuAI hands-free on the factory floor by bridging Meta Ray-Ban glasses microphone input into a fully offline assistant workflow without relying on cloud connectivity.
Use the glasses-bridge skill to connect Meta Ray-Ban microphone audio to ManuAI and run the offline voice assistance pipeline.
High-intent search queries and answers about installing and using this skill.
To connect Meta Ray-Ban smart glasses to an offline factory AI, use a WebSocket audio bridge to route microphone input into a local ManuAI deployment for speech transcription and grounded response generation without internet access.
Yes, hands-free machine troubleshooting is possible by streaming operator speech through glasses audio into Whisper transcription and SOP retrieval, delivering laptop-based voice and screen responses without touching a device.
Running speech recognition locally for factory automation is best achieved by bridging smart glasses audio via WebSocket to an offline ManuAI pipeline, processing speech transcription and SOP retrieval entirely on a laptop.
No, the glasses-bridge workflow does not require cloud connectivity, as it applies offline audio transport, local speech transcription, and laptop-based output handling to keep all ManuAI processing completely disconnected from the internet.
WebSocket audio streaming bridges smart glasses microphone input directly into a local ManuAI pipeline, routing voice through Whisper transcription and SOP-grounded retrieval to generate offline troubleshooting responses.
Offline voice assistance for factory operators requires a laptop for output handling and local ManuAI deployments, limiting mobility compared to fully integrated wearables while needing offline audio transport and local speech transcription models.
HKTITANIdentify the correct Meta wearable target and build path for an app.
J4JoshuaGenerate grounded answers from retrieved SOP chunks using local Qwen2.5-Instruct.
zcaceresDownload YouTube audio with yt-dlp and transcribe it to markdown using Whisper.
J4JoshuaConvert operator speech into offline text transcription with Apple Silicon Whisper models.
