Active Circles invite friends with snaps

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Hi, my name is Rushi and I launched Active Circles. The idea behind Active Circles is simple to gather a crowd and use the FOMO effect to pull it off. So why did I build this? I have a whole article on it on medium: https://medium.com/@rush86999/what-facebook-and-gambling-hav... long version short: I think social networks are becoming too unhealthy for everyone due to their effects on the reward system of our brains. I am a family doctor and I see that in young adults and how it's ruining our social fabric. here's what I came up with in response Planning for a new party, need friends for a quick yoga session. How about going for a run? Active Circles allows you to give a shout out to your close friends all at the same time. Have you ever noticed that sometimes that’s not enough? A picture may be worth a thousand words but more than that it creates a great story to tell and a great FOMO effect. Sometimes enough to give someone an extra little nudge to get up and get going to come over to whatever you are doing. This is the premise for Active Circles. A simple FOMO effect to invite friends over and create huge crowds! If more common friends are present at the same place then stronger the FOMO effect. What can I do with it? Anything that needs gathering a crowd- a huge party, yoga session, play a sport, cheer for an event. You can show off your snaps to friends' friends, even your school or workplace as long as you have an authenticated email address. So have fun and let me know how I can make it better! (Only Available in North America for now) https://itunes.apple.com/us/app/active-circles/id1348466506?...

Visit product View on Hacker NewsFeb 19, 2018

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

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

43%43% 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, io · Missing: https docs, excited, just released

33%33% 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 · Strong signals: active · Missing: arr, mrr, revenue

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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