Misty

UI of our Chrome Extension Misty
App Icon for Chrome Extension Misty

Inspiration

The high consumption of water utilized in AI and cloud computing data centers has become on high alert for the global world. As AI continues to grow, individuals, companies, and governments have a higher demand in utilizing their capabilities. While there are ongoing research and initiatives into alternative cooling methods to replace or reduce water usage, the implementation of these technologies remains in discussion with complications and certain environmental requirements making the application difficult.

What it does

Our approach to this issue can directly influence individuals allowing the users to be aware and mindful of their activity. Through Misty, a Chrome extension that tracks the user's AI usage and reflects it into a visual experience by a teardrop character.

Misty’s Emotional Feedback:

Neutral - Moderate usage (also the starting point for new users)
Happy/ Super Happy - Low usage
Sad/ Super Sad - High or excessive usage

Misty’s Other Features:

Happiness Level - Reflective of emotional feedback through numerical values
Water Level Tracker - Depicts the consumption of water
Notifications - Progress and changes of user activity

How we built it

The process of how we designed and planned this extension was by using programs such as Figma and Procreate. We also used languages such as HTML, JavaScript, and CSS to build the Front-end Application. We emphasized both to make our design unique and our application user-friendly. Most of our code was written by hand, but we did use ChatGPT in order to debug parts of our code we couldn't fully understand. In summary, we were able to create an extension that is both visually appealing and reliable for everyday use, which helps users become aware about their water footprint.

Challenges we ran into

Through the development of this extension, we noticed similarities to water intake apps, but addressed the differences:

Personal health vs. Environmental impact
Passive vs. Manual Input
Awareness vs. Habit Tracking

Another challenge was estimating water usage, since exact data varies across data centers and AI systems. We decided on a generalized approximation of usage to create the feedback.