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I made a polling app based on Quadratic Voting

Hacker News

I made a polling app based on Quadratic Voting

Hey HN! I just want to share my weekend project. So a couple days ago I read an article here about Quadratic Voting, and I'm really interested to implement it myself. After some browsing I found only a couple other poling websites which used QV, but I found that their interfaces less intuitive. So I created https://puaspoll.com . Its very simple, but I think it could be a fresh approach compared to many similar polling website/apps. Let me know what you think! Best regards

Visit productView on Hacker NewsDec 19, 2021
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Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released
66%66% 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 HuntOn track for Day 1 leaderboard · Strong signals: apps · Missing: mac, agents, macos
53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: intuitive, interface · Missing: plus, platform, reviews
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting
32%32% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% 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.

Incorrect prediction on native model

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