Try my new app Miscon

Hacker News

Hey HN folks, would like to get some feedback on a new app: https://play.google.com/store/apps/details?id=com.miscon.miscon Miscon offers a new way of communication and discovery. You can search and browse posts (AKA miscons) in various channels that interest you. Channel tags are free form, so be as creative as you’d like. You can search channels by their tags, such as #miscon, or #people #OnCalTrain, or whatever. Someone might search for those tags as well and interact with you there. All while you’re being anonymous. Let’s see what miscons you come up with! Any feedback would be appreciated. Thx!

Visit product View on Hacker NewsJan 11, 2015

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Actual performance

4points

6comments

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, apps, new · Missing: mac, agents, macos

60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

45%45% 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

42%42% 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 · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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