Project Info

car·e

Inspiration

Inspired by our own struggles, as college students, to: Find nearby grocery stores and Find transportation to the already sparse grocery stores, we created car•e to connect altruistic college kids, with cars (or other forms of transportation) and a love for browsing grocery store aisles, with peers (without modes of transportation) looking to grab a few items from the grocery store. Food insecurity is a huge issue for college campuses. It was estimated that over 10% of Americans were food insecure in 2020, but the number shot up to over 23% of college students. Food insecurity has a strong correlation with the outcomes of student education– not only are students less likely to academically excel, but they are also less likely to obtain a bachelor's degree. Our team wanted to address the sustainability track and the Otsuka Valuenex challenge because we identified a gap in organizations that address the lack of food access for college students. Being unable to easily access a grocery store would be inconvenient at best, but more than likely perpetuates health issues, economic inequity, and transportation barriers.

What it does

Our project is a space for college students, with the means to take quick trips to the grocery store, to volunteer a bit of their time to help out some of their peers. The “shoppers,” or students with access, input their grocery list and planned time and location of the trip. Then, “hoppers,” or college students who would like to hop onto a grocery trip would input their grocery list, and our matching algorithm sorts through the posted shopping trips.

How we built it

We implemented our idea with low-fidelity prototypes, working from diagrams on a whiteboard, to illustrating user flow and design on Figma, and then building the product itself on SwiftUI, XCode, and Firebase. When producing the similarity-matching results between shoppers and hoppers, we looked into Python, Flask, and Chroma, exploring various methodologies to achieve a good user experience while maximizing efficiency with available shoppers.

Challenges we ran into

Through this process, organizing the user flow and fleshing out logistical details proved to be a challenge. Our idea also required familiarization with new technology and errors, including Firebase, unknown XCode problems, and Chroma. When faced with these struggles, we dynamically built off each others’ ideas and explored various methodologies and approaches to our proposed solution; every idea and its implementation was a discussion. We drew from our personal experiences to create something that would benefit those around us, and we truly came together to address an issue that we all feel passionately about.

Accomplishments we're proud of

Ideating process Perseverance Ability to build off of ideas Team bonding Exploration Addressing issues we experience Transforming personal experiences into solutions

What we learned

This experience taught us that there are infinite solutions to a single problem. Thus when evaluating the most optimal solution, it’s important to evaluate on a comprehensive criteria, focusing on efficiency, impact, usability, and implementation. While this process may be time-consuming, it is necessary to keep the user in mind to ensure that the product and its features truly fulfill an unmet user need.

What's next

Building out future features (e.g. messaging capability) Building a stronger community aspect through features in our app: more bonding and friendships Business model Incentives to get ppl started on the app

Analysis

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Metric

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Technology

Found in codeClaimed only

PythonIn code
SwiftIn code
FirebaseClaimed
FlaskClaimed

2 of 4 appear in the indexed code. 2 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

50 KB

Source files

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