Inspiration
We were inspired by the rapid increase of Georgia Tech Thrifting Instagram accounts that have popped up recently, as well as the desire of many of the students to thrift. As a great resolution to combat the downfalls of fast fashion, our app serves to promote it by centralizing the locations and availability of thrifting and second-hand clothing to students.
What it does
This is where our website comes into picture as a one stop shop for all Georgia Tech related thrifting needs. We connect buyers and sellers on campus by allowing them to complete their transaction online while we hold weekly distribution events to deliver the items. This provides a more time-efficient and accessible approach to the thrifting environment.
How we built it
We built our web application using HTML and CSS to design the user interface, javascript to handle user input, php to connect our front-end to back-end, and SQL to create our database. We implemented the google API to include the map of Georgia Tech.
Challenges we ran into
Since we had different operating systems we weren’t able to use XCode or Swift so it took us some time to figure out a different platform we could use. We started looking at Flutter or Xamarin but we decided to shift to working on a web app since that would be most convenient for us time wise. The challenges we faced in this mode were tackling the PHP communication needed.
Accomplishments that we're proud of
We are proud of creating a functional website that taught us all about web development, especially as students new to the subject.
What we learned
We learned a lot about what goes into building a web app, such as what components are necessary, how to link pages, and how to handle and store user data. We learned the importance of using frameworks and how stacks come into play.
What's next for Tech Thrifts
We want to add a rating system for users so that other users can rate and provide reviews for sellers so that other students can see which buyers. We also want to incorporate AI to give users personalized recommendations while they shop. This is done by analyzing the certain style of outfits and pieces the users buy over time and upload to the app.
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