Unsigned.fm – crowdsourced Spotify playlists for finding new artists

Hacker News

I started making music as a hobby during covid as many of us did. I soon noticed that with platforms like Spotify receiving around 40K new songs per day, tons of great artists are completely undiscovered. I made unsigned.fm so people can submit songs by artists with under 10k followers to genre-specific playlists. The top 30 most popular ones (with positive net votes) make it on the playlists. My hope is that this will make it easier for talented new musicians to gain a few listeners.

Visit product View on Hacker NewsJul 31, 2023

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

84%84% 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.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

64%64% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

45%45% 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 · Strong signals: platform, soon · Missing: plus, intuitive, reviews

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

18%18% 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.

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