SoundLocate – Find your city match based on music taste

Hacker News

SoundLocate ( https://soundlocate.com ) matches you with cities based on your favorite artists. The idea: artists are popular in specific cities. If you love the same music as people in Berlin or Melbourne, you'd probably love those cities too. Connect Apple Music or enter artists manually → get matched → shareable result card. Tested it on myself: 7 out of 10 cities it suggested actually matched my lifestyle and preferences. Got heavy results on British cities (Glasgow, Manchester) and Australian ones (Melbourne, Sydney) - all have that indie/alternative vibe I gravitate toward in real life. Free, no signup. Curious how accurate it is for others.

Visit product View on Hacker NewsJan 29, 2026

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Actual performance

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Did not reach leaderboard

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Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

45%45% 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.

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

43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

42%42% 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

36%36% 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

26%26% 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

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: real life · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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