Inspiration

Litter Ladder was inspired by location-based apps like Geocaching, Pokemon Go, and Randonautica and a passion for making environmental conservation both accessible and fun!

What it does

Litter Ladder is a native app that promotes friendly competition in environmental cleanup. Through our API, users can quickly discover optimal trash pickup locations based on coordinates. Leveraging reverse geocoding, the app provides valuable biodiversity insights using ChatGPT. Users actively collect and dispose of trash in these areas, aiming for the top of the leaderboard. Together, we make a positive impact on our surroundings while embracing a spirit of camaraderie.

How we built it

We used React Native and Typescript for the front end app. For the api we used Node, Express, a mix of Typescript and Javascript, a MySQL database and Prisma as an ORM to query the data. Other technologies include the Apple Maps API, OpenAI API, Nominatim API, React Query, Axios, and Native Base for UI components.

Challenges we ran into

With little algorithmic experience between the two of us, it was rather challenging to come up with and implement an optimal trash picking solution. We were able to use a combination of chatgpt and web articles to navigate this problem and come up with a k-means clustering solution. Although it's not flawless, it effectively accomplishes the task at hand and serves as a stepping stone for future optimization and enhancements of the application.

Accomplishments that we're proud of

At our day job, we almost exclusively work on existing codebases, so being able to go from 0 to 100 on Litter Ladder and select the right technologies for the job was a great learning experience. Going through this process has definitely improved confidence in our ability to start projects from scratch.

What we learned

This hackathon provided a valuable chance for us to delve into mobile development. We learned how to set up local mobile app environments and build out mobile UI components. We also refined our ability to transform an abstract project idea into concise, actionable steps within a tight timeframe of 5 days.

What's next for Litter Ladder

  1. Improve the finding trash algorithm to include a predictive ML model trained on crowdsourced data.
  2. Add more chatgpt prompts to provide more location-specific environmental advice to the user.
  3. Add authentication, allowing users to create profiles where they can keep track of their trash history and connect with friends.

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