local independent band discovery service

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http://stagetrip.com/ Born from my frustration at finding good music and shows locally. This isn't a simple mapping aggregator, its (intended to be) a hand-curated local music discovery service. Right now its really just a shell, as I haven't had a tonne of time to get music into it. The eventual vision is that navigating to a "place" (i.e.: your hometown) on stagetrip you'll be able to find local bands, download some of their music (free), and see when/where they're playing (via songkick api for now). The other side of the experience is that it (in time) will provide a place for bands to connect with local fans and build traffic to their local shows. For instance, there's 4 bands in LA: http://stagetrip.com/places/united-states/california/losangeles 7 in california: http://stagetrip.com/places/united-states/california Etc. Individual song pages include a bunch of stuff: http://stagetrip.com/song/dont-move_416 http://stagetrip.com/song/faces_645 Obviously the download link, but also voting, location, genre, concerts, etc. Where available, concert listings show up: http://stagetrip.com/artist/phantogram http://stagetrip.com/song/dont-move_416 ...as do links to their social networks etc. You can browse by: Location: http://stagetrip.com/places Genre: http://stagetrip.com/genre Popularity: http://stagetrip.com/charts.php Artist: http://stagetrip.com/artist Something tells me that there's already someone doing this out there on a better/larger scale, but if there is, they're still pretty fragmentary. There's no business model implicitly built into this - its really a "scratching the itch" scenario. Over time, with traffic, I'd like to monetize it with paid promotion by location - i.e.: "Promoted bands in Chicago this Week" sort of thing - a low cost, targeted way of driving traffic to shows. Thoughts? Comments?

Visit product View on Hacker NewsJan 24, 2012

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

90%90% 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.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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.

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

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: model · Missing: mac, agents, macos

43%43% 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

10%10% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · 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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