New Social Music Sharing and Discovery App

Hacker News

I've recently released the beta version of my app, Solance, to the App Store and Google Play Store. The app is aimed at people who want to share and discover new music to add to their playlists, however, it's a bit different as this app isn't based off algorithms finding music for you, it's based on see what your friends listen to. Within the app you will have your main Solance feed (similar to Instagram) where songs from your friends will appear in a post-like format which includes 4 songs at a time. In this feed you can listen to previews of the songs, interact with others by liking and/or commenting on the songs, and also saving the song directly to your Spotify. You will also get a Solance profile showing the music you recently listened to and your top artists. You can search for users to see who would have a music taste that you would like to see more of. The app is in beta at the moment and purely has core functionality only as we are just trying to get feedback to see what features to move forward with. It would be awesome if you guys could download and give the app a try and leave some feedback! The website is https://solance.app . If you need some Solance usernames to start off and get a feel for the app before inviting any friends, feel free to add users dan & ryan :)

Visit product View on Hacker NewsJan 26, 2025

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Indie HackersFits the IH revenue-focused audience · Strong signals: songs · 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.

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

79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: reviews, users · Missing: plus, platform, intuitive

56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

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

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

nativeThis product was originally launched on this platform.

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

12%12% 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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