BreakBetter

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Inspiration

In today’s fast paced academic and professional environments, burnout has become far too common. We noticed that while there are countless productivity tools, few actually prioritize mental health and wellbeing. Inspired by our own struggles with focus, fatigue, and study overload, we wanted to create a system that doesn’t just push people to work harder but helps them work smarter, and rest better. Our goal: make a small but meaningful step toward a healthier, more balanced world.

What it does

Our project is an AI-powered study and break recommendation system designed to promote wellbeing and enhance productivity. Users input factors like their current energy level, type of work, and personal interests. The system then provides personalized suggestions for when to take breaks, how long they should be, and what to do during them whether it's a quick walk, a hobby-based reset, or a focused study sprint. The result? Improved focus, reduced burnout, and a more human-centered approach to productivity.

How we built it

BreakBetter was built using a modern tech stack with FastAPI for the backend and React for the frontend. The backend uses MongoDB for data storage and OpenAI's GPT-3.5-turbo for generating personalized study and break recommendations.

Challenges we ran into

Finding the balance between science-backed recommendations and personalization was tricky every user is different. Creating a UI that felt simple and approachable while capturing meaningful input took multiple design iterations. Ensuring that the system didn’t feel robotic or generic we wanted each suggestion to feel thoughtful and intentional.

Accomplishments that we're proud of

We were proud of creating a full-stack application that intelligently combines AI-powered recommendations with practical study tools. The implementation of a sophisticated recommendation system that adapts to user preferences and energy levels demonstrates strong technical skills. Most importantly, we built a system that can work reliably even when external services are unavailable, showing good system design and fallback planning.

What we learned

This project taught us about full-stack development, including API design, database management, and frontend-backend integration. We gained experience with AI integration and learned how to handle external API dependencies gracefully. The project also provided valuable lessons in error handling, logging, and creating robust systems that can handle various failure scenarios while maintaining a good user experience.

What's next for BreakBetter

Integrate machine learning to improve recommendations based on user feedback and behavior over time. Add integrations with Google Calendar or task managers to suggest breaks in real-world schedules. Build a mobile app version to help users get support on the go. Partner with student wellness centers or organizations to roll it out where it’s needed most.