HypeSwipe

Inspiration

Discourse around music trends is dominated by subjective opinions driven by personal sentiment, or at best references to incomplete data. For a niche with such huge mindshare as Music, we believe having a quantification layer of artists' hype, that takes into account metrics from streaming, social, and discover platforms, helps paint a more accurate image, and in turn makes up a solid basis for real discourse.

What it does

Hypeswipe presents daily challenges to users, where they simply have to swipe left/right, to indicate their prediction for an artists' hype going down/up the next day. The in-app leaderboard records score based on W/L ratio, acting as a "badge of honor" for people with repeatedly accurate predictions on music!

How we built it

The frontend components were build on top of Devvit web > React stack. On the backend, we source artist & hype related data from dripit.io - the data layer for Hype in Music.

Challenges we ran into

Accomplishments that we're proud of

What we learned

What's next for HypeSwipe

The current initial iteration, provides a group of challenges on a daily basis, from a curated list of artists, prompting the user to place a prediction for these artists' hype on a daily timeframe. We want to expand the app offering to allow for:

• community-driven artist curation
• support for longer timeframes (i.e. weekly/monthly predictions)
• allow users to create a post via our Devvit app, that shares their "performance card", proving their accuracy in music predictions to the public

Built With

• devvit
• dripit
• react
• typescript