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Digs.fm – For passionate music explorers (like Goodreads but for music)

Hacker News

Digs.fm – For passionate music explorers (like Goodreads but for music)

Hello everyone! As someone who's constantly on the look for new music to discover and being very deliberate about the things I'm listening, I needed a better way to organize the albums I want to listen to, listened and liked. And also I would like to see the discoveries of other folks who I know I like. So I started creating the tool I wish I had in the first place. In Digs, the basic idea is that: - you can add music releases (albums, EPs, singles, mixes) in three lists: Want to Listen, Listened, Digged. You can also use tags and notes to better organize these lists. - you get a public profile where your activity is visible (i.e. what you added to your lists). Example profile: https://digs.fm/alskn . - you can add other people as friends. Then you'll see their activity in your home feed. - you can either like or add a comment to any activity of your friends (or yours) - you can explicitly recommend a release to one of your friends You can think of it like Goodreads, but for music. I would assume it's mostly targeted to people that like to listen whole albums and would like to keep track of what albums/mixes they want to listen to, sometime in the future. This is very early yet and there are a lot of rough edges. You can find a few screenshots of the basic functionality in the homepage, from where you can also create an account - https://digs.fm . I'd appreciate any feedback, thanks in advance! --------- EDIT: I figured it's worth expanding a bit on some highlights: - In the search box, apart from searching, you can copy/paste any release URL from Discogs/Spotify/Bandcamp/Mixcloud/MusicBrainz and it will basically fetch the release and then you can add it to your lists. - There are browser extensions for Firefox and Chrome, so that when you're on some of the aforementioned sites, and you stumbled upon an interesting album, you can click the extension icon and the item will be added to your "Want to Listen" list. - For certain releases, you'll notice there's an embedded web player, for convenience.

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, single, activity · Missing: mac, agents, macos
88%88% 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.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
45%45% 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
16%16% 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.

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