Opine, a public polling website with maps

Hacker News

Hi, I made Opine ( https://opinepolls.com ), a poll-based social media site. Each post is a public poll asking users their opinion on something, anonymously, so no user sees any other user's information, but you can freely browse other polls. Private polls can also be made. The only information actually stored is the email, from Google sign in, and an approximate location for the map feature, if you enable it. This allows you to see a world map of votes, what opinions are present in what places. The intent is to catalog the world's opinions, organizing people's public thoughts so they're easy to sift through. This data can be extremely valuable, whether it's political polling, information about people's preferences, trends over time, and so on. Many people could benefit from this, and it's meant as a public service, to lift the veil on what people think about any topic, at any time. Easier political polling is just one obvious application of this. Please try it out and give any feedback, thoughts, questions, and so on, here or within the profile section on the website, which is fully anonymous. Thanks.

Visit product View on Hacker NewsSep 13, 2026

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2points

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

86%86% 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.

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

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: google, users · Missing: mobile apps, ios, personal

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: lua, io · Missing: https docs, excited, just released

43%43% 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

13%13% 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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