Unitunes – Transfer, sync, and backup playlists on music services

Hacker News

Hey HN! I built unitunes to take control of my music. unitunes is a CLI tool that can transfer songs between services and keep playlists in sync. It is a free open source alternative to paid services such as Soundiiz and TuneMyMusic. unitunes stores your playlists in plain text, allowing you to version control your music. Playlists can be pushed and pulled from streaming services. Tracks from one service can be searched on another. Currently spotify, musicbrainz, and youtube music are supported. I hope to add support for all kinds of "services", such as local files, musescore, osu!, beatsaber, genius, or anywhere else music exists in some form. I'm looking for collaborators to help add more services and improve the search algorithm! https://github.com/platers/unitunes

Visit product View on Hacker NewsMay 17, 2022

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

72%72% 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.

Hacker NewsStrong engagement from HN community · Strong signals: exist, open source, io · Missing: https docs, excited, just released

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

Indie HackersIH features products with proven revenue · Strong signals: songs · Missing: supports, reddit linkedin, podcasting

48%48% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

36%36% 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

10%10% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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