Find out what music fellow HN users are listening to

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Hey lovely HN crowd, I am currently building a little tool which works on top of Spotify. It is called tastemaker.cc and offers different ways to discover new music. The most recent feature I build is called GroupFun™ (name is still work in progress) and allows to join a Spotify playlists which is automatically populated with the favorite tracks of all participants. Not sure if it is a good idea (no clue if the platform can handle the traffic), but I would like to start the following experiment: A huge HN playlists to see what the HN crowd is listening to :D You can either join the playlist by entering the code "hn" as group name on https://tastemaker.cc/ or directly via following link: https://tastemaker.cc/api/join/groupfun?code=hn After log in to your Spotify you can allow the application and will be added to the playlists. Let's see how my little project can handle this :) p.S. Yes I know it requires quite some privileges on your Spotify to get your top tracks...sorry that is how it is at the moment. If you are not comfortable with that, maybe you want to check out the public tastemaker playlists: https://tastemaker.cc/public

Visit product View on Hacker NewsApr 13, 2021

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, code · Missing: mac, agents, macos

85%85% 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 · Strong signals: platform, users · Missing: plus, intuitive, reviews

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: users, way · Missing: mobile apps, ios, personal

51%51% 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 · Missing: supports, reddit linkedin, podcasting

32%32% 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

14%14% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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