Drop Query

Inspiration

As AI rapidly transforms the tech industry, many people remain unaware of its environmental impact—especially the massive amount of water used to cool data centers. Tools like Opal and Grammarly have inspired us by showing how digital habits affect productivity or communication. Similarly, Drop Query aims to visualize the environmental footprint of AI usage, specifically water consumption, and help users reduce it. Our goal is to make people conscious of their digital water footprint and ultimately encourage them to bring their total usage closer to zero—or at least a sustainable level. What It Does Drop Query is a Google Chrome extension that estimates and displays the amount of water consumed by AI models—such as ChatGPT, Google Gemini, and other high-compute systems—based on the user’s activity. It provides real-time feedback through positive and negative reinforcement to motivate users to adopt more sustainable digital behaviors.

Key Features User Survey Initialization: Upon installation, users answer a short survey to personalize their water usage profile: Frequency of AI use (daily, sometimes, never) Awareness of online water consumption Type of usage (tool, roleplay, research, etc.) Daily screen time Other relevant usage questions This data helps estimate a personal baseline for AI-related water use.

Dynamic Water Tracking: Using estimated water consumption data per AI query, the extension tracks individual usage in real time and compares it against global averages.

Reinforcement System: Positive feedback for being below average: “You saved a day's worth of water for 3 children today!”. Negative feedback for above-average use: “Your AI queries today could have hydrated a small village for a day.” The system turns abstract metrics into meaningful humanitarian equivalents—making sustainability engaging and relatable.

UI/UX Design: The interface centers around a rounded floating widget, visible across all webpages, representing the user’s water usage visually.

Design Elements Water Bottle or Tank Visual: Fills as AI queries increase Resets after each full bottle (e.g., 500 ml or 1 gallon) Interactive Animations: Fade-in when opened Color and height changes on hover/click Realistic “water physics” when dragged Customizable Positioning: Draggable and removable widget Does not block webpage interaction Timed Feedback Messages: Comments appear beside the bottle for ~5 seconds after an update This design ensures the experience feels playful, informative, and non-intrusive.

Supabase Integration We use Supabase to manage and persist user data: Tracks daily, weekly, and lifetime water usage Stores both individual and collective totals Calculates differences between a user’s average and actual usage Triggers reinforcement messages based on results At user-defined intervals (daily, weekly, yearly), Drop Query sends a summary notification showing total water used or saved over time.

How We Built It

Frontend: HTML, CSS, and JavaScript using the Chrome Extension API for UI and background processes. Backend: Supabase for authentication, data storage, and usage tracking. Design: Figma prototypes to define the user flow, color palette, and widget interactions. Integration: Chrome’s content scripts link the real-time UI with Supabase data updates. Water Usage Research: Estimates based on academic sources and industry averages for AI model water consumption per query.

Challenges We Ran Into

Quantifying Water Use Accurately: AI models differ drastically in compute and cooling requirements, making consistent metrics hard to define. Performance Optimization: Ensuring the extension runs smoothly without slowing down web pages. User Engagement: Balancing informative environmental data with enjoyable, motivational feedback. Real-Time Syncing: Managing Supabase updates efficiently without excessive API calls.

Accomplishments We’re Proud Of

Developed a working Chrome Extension prototype with live visual feedback. Created an interactive UI that feels lightweight and user-friendly. Built a scalable database system to track individual and collective environmental impact. Turned an abstract environmental concept into something visual and actionable.