A lightweight music webapp we built in 12 hours

Hacker News

A buddy and I decided to hack on this little project this weekend, putting in about 12 hours. It's running Ruby on Rails, deployed to Heroku. We tried to keep things super lean with different gems handling user accounts, mailchimp for an email list and super lean styling/layout. Everything should work well enough on mobile devices, too, so give it a look! Right now the magic all happens via the Soundcloud API (of course), but we're exploring different ways to make this appeal to a broader audience of music listeners. We'd love your feedback! Here's the site: http://www.mvsic.co Feel free to register so that you can favorite some songs and curate your own playlist!

Visit product View on Hacker NewsJun 30, 2014

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

85%85% 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.

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

68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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.

Hacker NewsMay not resonate with HN audience · Strong signals: ide · Missing: https docs, excited, just released

38%38% 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.

Incorrect prediction on native model

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