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Airogami: Send messages/pix that target by age location and gender.

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Airogami: Send messages/pix that target by age location and gender.

How it works: It's Omegle meets Snapchat. 1) You can send messages (or images) that automatically seek other random users. 2) You can choose to designate where your message goes by: Age, Gender, or Location. (Location using Google maps API.) 3) When someone replies to your message you guys just initiated a chat. You can share images and snaps (captioned images) in the chat section. Both users can also clear the contents of the chat like a white-board, and it syncs at both ends. 4) The more messages you send the higher probability that you'll find someone to chat with. Messages are 100% destined to reach others. But if others reject the message, it goes back into the cloud then finds another user. (Some messages get rejected by everyone.) We've been live for 2ish days. I posted on Reddit yesterday, now we're closing in on 500 users, but running into major issues matching messages to recipients. Here's our website: www.airogami.com Here's the iTunes URL: https://itunes.apple.com/app/airogami/id778180190?ls=1&mt=8 We'd really appreciate any feedback/criticism. Thanks in advance. Also really sorry to any Android users, I'm working on that as we speak...

Visit productView on Hacker NewsDec 21, 2013
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
88%88% 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 · Strong signals: apple, google, user · Missing: mac, agents, macos
72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: google, users · Missing: mobile apps, ios, personal
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: io · Missing: https docs, excited, just released
49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
47%47% 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
16%16% 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, cryptocurrency
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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