A tool to organize, streamline and share your music discoveries

Hacker News

I love music and I'm always trying to discover new music that I can enjoy. In the past years I was using a fairly tedious process of organizing the music albums (or DJ sets, radio shows) in a few lists: - Want to Listen - Listened - Listened (and liked) I wanted to keep track of these lists, what I discovered each month/year and share all this activity and lists with my friends. For that, I was using a combination of tools that weren't made exactly for this job (Discogs+Spotify+IM apps), resulting in a cumbersome experience. So a few months ago I started this hobby project, with the purpose of streamlining my process (make it as easy as possible) and making it fairly simple to share it with fellow music explorers. The basic idea is that you add items to your "Want to Listen" list - these are music albums (or even mixes), that you want to listen. After you listened to them, you can mark them as "Listened" or "Listened and liked". Your activity is then shared with your friends, and you have a public profile where this activity and your lists appear. You can think of it kinda like "Goodreads but for music". There are a few ways to add items to your lists (more to be added in the future): - search by artist name or release title - add using a Discogs release URL - add using a Mixcloud URL - add using a Spotify album URL It's still in super early version, so there are a lot of missing features and for sure a lot of rough edges. I'm open to feedback and suggestions! You can find it at https://digs.fm. A public profile: Stack: Rail, Postgres (also powers the full-text search capability) External APIs used: Discogs, Spotify, Mixcloud Data provided partly by: Musicbrainz (~2.6M releases indexed currently) Hosted on: Render.com (but I'll probably move it to a VPS)

Visit product View on Hacker NewsMay 6, 2022

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

88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

78%78% 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

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps, month, way · Missing: mobile apps, ios, personal

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

15%15% 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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