Co

Contestit.app – Social media giveaways with games

Hacker News

Contestit.app – Social media giveaways with games

Hey, I'm Stan and I made a app called Contestit. It's a social media giveaway app that let's users create giveaways. It gives the user a link they can share to their followers. What I always disliked or found annoying about giveaways is the fact that they were all the same, here's the prize. All you got to do is like, follow, and comment to be entered. That always bored me so i wanted to make it more interactive. Here's how it works: Users still get a shareable link for their giveaway, but after people sign up, they can play games to earn more entries instead of just waiting around. Right now there are three game types: -Daily spin wheel (self-explanatory) -Custom trivia contests (creators set their own questions) -Photo contests (either "best photo wins" judged by the creator, or points-based where more engagement = more entries) The photo contest is my personal favorite. If you got any ideas for some games i could add let me know. :)

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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 · Missing: supports, reddit linkedin, podcasting
71%71% 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: user · Missing: mac, agents, macos
67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal, users, way · Missing: mobile apps, ios, entrepreneurs
60%60% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, 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
25%25% 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
23%23% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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