We collectively know many people that have become caffeine dependent, many of them are college students or office workers simply trying to keep up with the massive workload that they are expected to uphold.
What it does
It is a caffeine consumption planning app, after inputting personal information such as weight and age as well as how long they plan to work, the app will a specialized algorithm to determine a schedule for caffeine consumption that will optimally increase your productivity without intaking too much.
How we built it
Since we didn’t have a solid idea for this project, we spend the first 3-4 hours at the venue coming up with a feasible topic and plan. After planning, we delegated, different parts fo the process. We worked on the core of the project first including the initial back end and front-end of the project. We then worked on adding improvements.
Challenges we ran into
Since this was our first hackathon none of us had experienced the grind of working on a project for a full 24 hours. We also all got involved in doing tasks and working in languages and platforms we never had before, thus having to learn them on the spot. Finally, perhaps our hardest challenge was that we repeatedly found it hard to get the spelling of caffeine right.
Accomplishments that we're proud of
We all showed really good teamwork skills, the tenacity to work for such a long period of time. We also successfully executed on a project that we were all passionate about and completed it within the timeframe. We spent a lot of time and did a lot of research to ensure the algorithm that we used in our calculations was state of the art.
What we learned
We all worked with languages that we weren't experienced with yet as well as worked with new platforms such as Google's.
What's next for Fueled by Caffeine
We plan to implement a leaderboard system that will allow users to “friend” people and see their performances on the leaderboard. In addition, we would provide our users with Daily Challenges for caffeine limits and hours of productive working, that if completed will also be displayed to their friends. We also plan to implement Machine Learning Algorithms that will improve upon the accuracy for an individual users' predictions using their previous data and reported levels of effectiveness. We also plan to add extra authentication and certification to ensure the privacy of our user's information.