Ripple, a Spotify playlist generator based on your mood and music taste

Hacker News

I had this idea when my friends and I were hanging out and playing music on the speaker. The problem is, Spotify curated playlists are not tailored for my friend groups because we have different music taste. So I passed my phone around for my friends to queue their songs. It was always a hassle. This project is built on top of the Spotify Web API. The two modes, "Fresh" and "Popular" rely on the recommendations endpoint. As always, your feedback is appreciated. :) Thank you!

Visit product View on Hacker NewsFeb 8, 2023

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Strong signals: songs · Missing: supports, reddit linkedin, podcasting

19%19% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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