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Kiven Aa – Talk Freely and Anonymously
Hacker NewsKiven Aa – Talk Freely and Anonymously
After talking on several online anonymous chatting apps, I always felt like this area is totally lacking in good quality apps. So, I am trying to build a new slick platform "Kiven Aa" ( https://kivenaa.com ), for anonymous chatting lovers. I would really appreciate, if you can try it out and provide me with your feedback. Every sort of feedback, both positive and negative are welcome. Thanks
Share cardActual performance
4points
1comments
Did not reach leaderboard
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting58%58% 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.
AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, way · Missing: mobile apps, ios, personal48%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: ide · Missing: https docs, excited, just released43%43% 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: apps, new · Missing: mac, agents, macos42%42% 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, revenue15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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