Pomo.

The ultimate pomodoro timer, built to track and improve your productivity through helpful data analytics.

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Inspiration

The inspiration from this project came from us wanting to improve and track how productive we were as students in our day to day lives.

What it does

Pomo. is the ultimate implementation of a pomodoro timer. Its concept is simple but powerful. By providing feedback on how productive a user is right after a pomodoro session, this allows individuals to track their progress as they become better and more efficient workers.

How we built it

We built the frontend using React JS JSX. In order to provide and store useful data we used a Supabase database to deliver productivity stats via an API. And then finally, the website is hosted using Vercel with a customer domain (www.pomotime.tech)

Challenges we ran into

Some of the React libraries we utilized were either very recently created or quite old. Because of this the support or documentation for these tools were limited. A large portion of time was spent looking at source code on github in order to reverse-engineer some of the necessary tasks.

Accomplishments that we're proud of

We're extremely proud that we built an entire multi-page web app with a modern UI, fantastic functionality, interactive charts, and full database support. In general our goal was to create a fully featured app. We tried to focus on the details, everything from our awesome README on github, to the little UI design tweaks. And additionally, we were proud to be able to deploy the app with Vercel using a custom domain.

What we learned

We deepened our knowledge of React and frontend frameworks, improved our skills in tracing errors and resolving github conflicts, and practiced dividing work to develop efficiently.

What's next for Pomo

Below is a list of ideas we have for "next steps":

  1. Support mobile devices
  2. Add more data to track and provide insights on
  3. Build an entire authentication system rather than just tracking by a unique user id number
  4. Possibly join forces with TensorFlow in Node to utilize the power of ML to understand common patterns and recommend customized productivity tricks.
  5. Implement a to-do list with the pomodoro timer functionality.

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