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Inspiration

Student frustration surrounding Laundry @ Yale

What it does

SMS-based laundry assistant that helps students identify better times to do laundry. It combines live machine availability from One Tap Away with historical usage patterns, student responses, and optional Google Calendar availability to suggest convenient laundry windows.

How we built it

Developed the Boolawash interaction flow, including onboarding, suggested laundry windows, Y-responses, and real-time updates. To test the concept, we simulated groups of students attempting to do laundry with and without Boolawash and compared waiting times, failed trips, and demand distribution. We also developed a model for how Boolawash could improve over time using three main data sources.

Challenges we ran into

One of the biggest challenges was designing a system that could predict demand without knowing exactly who was using each machine. Because spontaneous users can enter the laundry room at any time, Boolawash cannot directly assume that an increase in machine use came from students who replied Y. We addressed this by focusing on aggregate patterns rather than individual tracking. The system would learn typical spontaneous demand and compare that baseline with changes in occupancy following Y-responses.

Accomplishments that we're proud of

Developing a system that works with existing infrastructure rather than replacing it. Boolawash uses One Tap Away as a data source while addressing a different problem: helping students anticipate availability rather than simply checking it in real time.

What we learned

The problem was not necessarily a lack of information. Students already have access to live machine availability, but that information requires them to actively seek it out. This shifted our focus from designing another interface to designing a system that works more passively in the background.

What's next for Boolawash

The next step would be to build and test a functional prototype within one Yale residential college. This would involve connecting Boolawash to live One Tap Away data, implementing SMS messaging, and allowing students to optionally connect their Google Calendar. A pilot could then measure actual outcomes such as failed trips, waiting time, prediction accuracy, response rates, and student satisfaction. Ultimately, the goal would be for Boolawash to become increasingly accurate while remaining almost invisible to the student: a system that quietly helps laundry fit into their day rather than interrupt it.