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Disco, a platform for independent artists to reach more fans

Hacker News

Disco, a platform for independent artists to reach more fans

Just launched the MVP of my startup today after two weeks of non-stop work. Right now, Disco is an artist discovery platform where artists can submit their Spotify tracks for users to listen to through a Tinder-like UI, swiping right on the ones they like. The vision is far grander, of course, and involves fostering greater artist <-> fan connection while also providing a sustainable source of revenue for artists. The music industry in its current state is too impersonal and not sustainable for all but the biggest artists. The solution to both problems is artist <-> fan connection/engagement, where an artist's fans can interact directly with the artist and actually connect with them rather than cheer them on from far away. I believe loyal fans will be willing to financially support their favorite artists in exchange for that level of connection. Disco is aiming to build the platform to enable that. I focused the MVP on discovery in order to focus on artists who are just starting their career. I'm excited to conduct lots of interviews to really understand the problems they face. More info on the future of Disco is here: https://info.disco.so The actual app is here: https://disco.so As always, I am open to any and all feedback :)

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Actual performance

6points
4comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
80%80% 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.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, io · Missing: https docs, just released, exist
48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: personal, users, way · Missing: mobile apps, ios, entrepreneurs
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: user, open · Missing: mac, agents, macos
41%41% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: revenue · Missing: arr, mrr, profit
11%11% 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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