Lo

Loopy – share and find and music you love

Hacker News

Loopy – share and find and music you love

Hi, I created loopy, a website to share and discover music you love. A former coworker answered an ice breaker question saying his superpower would be to know every language fluently since he travels a lot. Mine would be to hear every song I would fall in love with. I realized that I will die without hearing every song that I will fall in love with. So many of my all-time favorite songs I randomly have heard at a club, coffee shop, traveling, walking by a store, etc. There is a high chance that I would have never heard those songs. Loopy aims to fix this. You can post your all-time favorite songs. If someone else love this song, there is a chance you will too :). Here is my profile: https://loopy.fm/kyle Happy listening :) - Kyle

Visit product View on Hacker NewsSep 11, 2023

Actual performance

46points

29comments

Made the leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, songs · Missing: supports, reddit linkedin, podcasting

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

13%13% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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