MonsterMusic is a music player for Android platform

Hacker News

Advanced Equalizer: Customize your sound experience with our built-in equalizer. Fine-tune the audio to match your preferences and immerse yourself in crystal-clear, high-quality sound. Multiple Themes: Personalize your music player with a variety of themes. Switch effortlessly between themes to match your mood or style. Your music, your way! Lyrics Support: Dive deeper into the lyrics of your favorite songs. [MonsterMusic] supports lyrics display, you can import text or lyrics files. Click the lyrics word, you can find a feature.

Visit product View on Hacker NewsOct 16, 2024

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

89%89% 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 HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

31%31% 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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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