Meşk Tune – Tuner App

Hacker News

Hi all, We have just released Meşk Tune, it is available at stores. (Android) https://play.google.com/store/apps/details?id=com.meskteknol... (iOS) https://apps.apple.com/us/app/me%C5%9Fk-tune/id1575937381 It is completely free with all features unlocked. (IAP is only for donation purposes). If you want to have a look at UI, checkout these short clips. https://www.youtube.com/watch?v=6td88DiQcGU https://www.youtube.com/watch?v=4WU4o7bvaYU https://www.youtube.com/watch?v=dC3eTUIDLEI

Visit product View on Hacker NewsJun 23, 2022

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Actual performance

1points

Did not reach leaderboard

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TrustMRRFits verified-revenue profile · Strong signals: ios, apps, google · Missing: mobile apps, personal, entrepreneurs

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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.

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

40%40% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

40%40% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

22%22% 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

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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