ProNoise, where "the Masses Discover and Musicians Succeed."

Hacker News

There are millions of musicians around the world struggling to succeed, and a wide array of businesses that need popular music and musicians- be it for their next tv spot, live event, or ad campaign. We crowd source the selection of musical talent for these businesses using our unique online battles, voted on by the masses of music lovers and fans. At the end of a battle the sponsor is left with fan validated musicians that have proven their music to be both popular and able to energize a large fan base, exactly what these businesses need at a fraction of the time and cost of their current solution. On PROnoise the Masses Discover and Musicians Succeed.

Visit product View on Hacker NewsApr 5, 2012

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

74%74% 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.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

47%47% predicted probability of success on AppSumo, 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

34%34% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

26%26% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

9%9% 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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