zhutony / ambianic-edge

Ambianic Core Edge Runtime. Configure to observe sensors and cameras in your house or workspace for important moments.

 

Project mission

Ambianic.ai is an Open Source Ambient Intelligence platform for Home and Business Automation

Ambianic Edge runs on edge devices such as Raspberry Pi. It monitors sensors, cameras and other inputs, passes them through AI inference and makes actionable observations.

Ambianic UI is the user interface to Ambianic Edge deployments. Latest live version available at ui.ambianic.ai

Following is a conceptual flow diagram:

High level architecture diagram below:

A more detailed project background document is available here.

A deep dive technical discussion of the project architecture is available in this WebRTCHacks article.

Project Status

Ambianic Edge is in now in public Beta. Here is a 5 Minutes Quick Start Guide.

All constructive feedback is most welcome!

Product design highlights

• Less than 15 minutes setup time
• Less than $75 in hardware costs
  • Reference hardware platform: Raspberry Pi 4 B, 4GB RAM, 32GB SDRAM
• No coding required to get started
• Decomposable and hackable

How to run in development mode

If you are interested to try the development version, follow these steps:

1. Clone this git repository.
2. ./ambianic-start.sh
3. Study config.yaml and go from there.

Documentation

An introduction to the project with user journey, architecture and other high level artifacts are now available here.

Additional content is coming in daily as the project advances to its official release.

Community Support

If you have questions, ideas or cool projects you'd like to share with the Ambianic team and community, please use the Ambianic Twitter channel.

Contributing

Your constructive feedback and help are most welcome!

If you are interested in becoming a contributor to the project, please read the Contributing page and follow the steps. Looking forward to hearing from you!

Acknowledgements

This project has been inspired by the prior work of many bright people. Special gratitude to:

• Yong Tang for his guidance as Tensorflow SIG IO project lead
• Robin Cole for his invaluable insights and code on home automation AI with Home Assistant
• Blake Blackshear for his work on Frigate and vision for the home automation AI space