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Stardrift – A 3D galaxy of all music, to explore and learn

Hacker News

Stardrift – A 3D galaxy of all music, to explore and learn

Music streaming services have tons of music, but no easy way to simply shuffle over all of it, or explore it outside of their recommendation algorithms. So I took the database of 43m songs from Musicbrainz.org and made my own player, with b̶l̶a̶c̶k̶j̶a̶c̶k̶ ̶a̶n̶d̶ Wikipedia integration and playback via Apples MusicKit, plus a 3D VR version via WebXR. Short demo: https://stardrift.gerlach.dev/demo/stardrift.mp4 Direct link to the 3D galaxy: https://stardrift.gerlach.dev/galaxy That last bit was the most fun to build (with AI help) and thus escalated a bit into a VR playground. 2.3 million stars/albums, a custom Three.js UI kit and a bunch of extras to have something to do while the music plays. Like a full-size USS Enterprise to fly around with Jerry Goldsmiths soundtrack. Or a Mahjong game, black hole, Theremin, banana duck etc. Works best on Quest 3 and /should/ be useable on a Vision Pro with gestures. If you have an AVP, I would appreciate feedback, the Xcode simulator only goes so far. The Venn diagram of people liking to explore random music, having an Apple Music account and a VR headset is probably pretty small, but I for one love it and have discovered a bunch of new music and learned a lot. Frontend built with Svelte 5, Three.js, backend PHP. It takes about 50GB in Postgres + ~150GB for cover images. I'll keep it online as long as my little VPS doesn't melt.

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: songs · Missing: supports, reddit linkedin, podcasting
73%73% 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: apple, new, code · Missing: mac, agents, macos
63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
61%61% 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 · Strong signals: way · Missing: mobile apps, ios, personal
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews
40%40% 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
17%17% 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.

Incorrect prediction on native model

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