HubLensAIBasedHardware/omi
// archived 2026-04-20
BasedHardware

omi

AI#AI#Wearables#Swift#Flutter#Python
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183

// summary

Omi is an open-source platform that functions as a second brain by capturing and transcribing your screen and conversations in real-time. It provides AI-driven summaries, action items, and a chat interface that remembers everything you have seen or heard. The system supports cross-platform integration across desktop, mobile devices, and specialized AI wearables.

// technical analysis

Omi is an open-source AI platform designed to function as a 'second brain' by capturing, transcribing, and summarizing screen activity and real-time conversations across desktop, mobile, and wearable devices. Its architecture utilizes a multi-platform approach, combining Swift and Rust for desktop, Flutter for mobile, and a Python-based FastAPI backend to process audio and visual data through advanced AI pipelines. By integrating hardware wearables like the Omi device and Omi Glass, the project solves the problem of information retention and context management, providing users with an AI assistant that maintains a persistent memory of their daily interactions.

// key highlights

01
Real-time transcription and summarization of conversations and screen activity to help users track important information.
02
Cross-platform support including macOS, iOS, Android, and dedicated wearable hardware for continuous capture.
03
Extensible architecture that allows developers to build custom apps and integrations using provided SDKs for Python, Swift, and React Native.
04
Comprehensive AI backend pipeline featuring VAD (Voice Activity Detection), diarization, and LLM integration for intelligent context retrieval.
05
Open-source hardware designs and firmware support for custom wearables like the Omi Glass, enabling community-driven hardware innovation.

// use cases

01
Real-time transcription and summarization of meetings and screen activity
02
AI-powered chat assistant that maintains a searchable memory of past interactions
03
Integration with custom AI wearables and hardware for continuous data capture

// getting started

To begin, you can download the pre-built macOS app or mobile applications directly from the provided links. For developers, you can clone the repository and run the desktop application using the provided shell script, or follow the full installation guide to set up the local backend stack with Python and Rust prerequisites.