Two students, Cole Bentley and Dakota Roth, at the Oregon Institute of Technology, developed an innovative solution using Grafana and TimescaleDB to monitor the availability of laundry machines in their college dorm. By leveraging internet-connected washing machines with service tags and QR codes, they could access and visualize real-time data on machine usage, allowing them to determine the busiest times and most popular machines. They initially collected data using a script written in Perl and later optimized it with Go, which fetched JSON data every minute from the machines’ APIs and stored it in TimescaleDB. Their Grafana dashboard, developed from Dakota's prior experience and interest in data visualization, provides insights into machine status, including running times and availability, enhancing the efficiency of their laundry routines. The project, which was well-received at GrafanaCON 2025, has inspired them to explore further unconventional applications of Grafana, such as correlating laundry data with weather conditions or even gamifying machine usage for potential stock trading insights.