HackStack

Project Info

Sugar Plum

Devpost

This project did not submit a demo video on Devpost.

Inspiration

Women experience fluctuations in energy, mood, and physical well-being throughout their menstrual cycle. Given hormonal changes can significantly impact daily life, we felt it would be good to have a tool that empowers women to understand and embrace these changes by providing tailored lifestyle and dietary recommendations.

What it does

On top of helping women keep track of their menstrual cycles, this app provides dietary recommendations and exercise/lifestyle tips to allow women to stay in sync with their body's natural rhythms and optimize their health and well-being.

How we built it

We developed Sugar Plum as a web application using Next.js for full-stake development. We integrated a calendar API for cycle tracking and created a Mongo database to store data locally. For the nutrition and exercise recommendations, we build a chat bot using OpenAI to based on scientific research. The app uses machine learning algorithms to personalize suggestions based on user input and cycle patterns.

Challenges we ran into

We were originally building a mobile app, which all of us are new to. After facing significant challenges there, we pivoted to a web application. Further, while attempting to build this and collaborate asynchronously, we ended up with lots of merge conflicts and our code broke several times! Also, the WiFi at the venue was not great, and that was a major inconvenience.

Accomplishments we're proud of

We overcame the issues by hopping on a call and debugging together, and doing peer programming sessions. Finally, given most of the team are first-time hackers, we're proud we put ourselves out there and participated in something we weren't comfortable with.

What we learned

Hacking is fun! The importance of flexibility and pivoting when faced with obstacles How to quickly research and synthesize scientific information into practical advice Collaborative problem-solving and effective team communication

What's next

We want to train the AI model better so that it gives more relevant suggestions that are backed by research. We will want to make the application cross-compatible on multiple devices. Plenty of UI improvements!

Analysis

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Metric

Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.

Technology

Found in codeClaimed only
  • CIn code
  • CSSIn code
  • ExpressIn code
  • Google GeminiIn code
  • JavaScriptIn code
  • MongoDBIn code
  • Next.jsIn code
  • OpenAIIn code
  • ReactIn code
  • SwiftIn code
  • Tailwind CSSIn code
  • TypeScriptIn code
  • RubyClaimed

12 of 13 appear in the indexed code. 1 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.

AI coding agents

No AI coding agent signals were found in this repository.

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

178 KB

Source files

34

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.

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