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SongLink a web app to share songs across music streaming services

Hacker News

SongLink a web app to share songs across music streaming services

I built a web application after being frustrated about how my friends share music. In chat groups, we constantly share songs & albums with each other. But since everyone uses a different music streaming service, that means I had to open a link, dismiss cookie banners, copy/paste the title, find the right version etc... So I came-up with SongLink where songs, albums and artists can be shared across different services with one link!

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Actual performance

3points
3comments
Did not reach leaderboard

Launch Intel predictions

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AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: 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.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
49%49% 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
45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: open · Missing: mac, agents, macos
38%38% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
10%10% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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