Birdsong-find people that love the same music (Spotify) (alpha users)

Hacker News

Looking for alpha users! See the last paragraph. Yesterday I randomly found a person on Spotify whose playlists I love. It's as if these playlists were made by me; I love them and now I have at least thee new good playlists to listen to. I immediately knew that I want to replicate this experience-finding people that love the same music as me. In the afternoon, I coded a simple web app. The app is very simple and halway complete but ready for users. The currently implemented part works a bit like Tinder selection: 1. you are presented one song you recently listened to a lot 2. a 30 second preview of the song starts playing and you are shown the song's cover, name, and artists 3. you must rate the song with either: - NO: I am sick of this song/I hate it - MEH: it's OK - LOVE: I loveee this song, it gives me the chills 4. repeat-go to step 1 with the next song See a screenshot here: https://twitter.com/touch_marine/status/1478772993602080775 The rest of the app (the half not yet implemented) will take this ratings and match them with others. You will get a list of matches and you will be able to go to their Spotify's profile and listen to their public playlists. I am looking for any users that would like to give the app a go. Unfortunately, I have a limited API access which means only 20 people can register and I first need to enter their Spotify email into the Spotify Developer Dashboard. To register send me your Spotify email to scout at touchlabs.io or on twitter to @touch_marine (https://twitter.com/touch_marine).

Visit product View on Hacker NewsJan 5, 2022

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

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

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BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

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