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Stillis – An open-ended anonymous polling platform for anything

Hacker News

Stillis – An open-ended anonymous polling platform for anything

I was sitting in an auditorium where the speaker was taking questions that were submitted through an app, it got me thinking that there's got to be a better way for an audience to communicate with the speakers than individual submissions. Even in online spaces, while there tends to be only a limited number of speakers, I would imagine a lot of people listening in tend to have shared concerns that aren't possible for them to express, and a chat box or comment section wouldn't change that. I realized this was a normal story, and that a basic word-tree could solve this problem. It could facilitate large crowds to communicate effectively with just a single person, addressing the every elephant that could fit in the room. I then looked at the idea from a different angle, and presumed it could also be used as a slower, comment-driven platform that can represent aggregate opinions of those who wish to express them. So I built the idea and tried making room for all of those needs. It's always been my opinion that political polls so readily excluded large swaths of the populace to the point where their trustworthiness was questionable. I figured this could make for a general polling platform, and a format capable of holding more than just yes/no answers. Allowing people to say what they want can be uniquely quantitative if you limit their response to just a single comment. I then built the idea further, and realized it's basically a data format, and can be manipulated in various ways, so I worked out what those would look like, and made it into a social media platform. I plan on extending the existing user tiers for the sake of subverting bots. I hope you find it interesting!

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Actual performance

4points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
70%70% 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, single, open · Missing: mac, agents, macos
62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: answers, way · Missing: mobile apps, ios, personal
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
45%45% 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: platform · Missing: plus, intuitive, reviews
37%37% 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
14%14% 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
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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