Ho

Hot or not meets Twitter

Hacker News

Hot or not meets Twitter

I am working with a couple of friends on a new concept of social network / blogging platform. The idea is simple: we ask users to vote other users' messages in order to get points and use these points to post new messages. The idea is similar to hotornot.com in that the user has to vote other users' content in order to have his content delivered into the system and receive feedback from others. The idea is similar to twitter.com in that this is a micro blogging platform, where messages are limited to 500 characters. The website is still in its alpha status. We don't have any design yet, many things are still basic and some of the messages posted so far are in Italian (yes, we are from Italy). If you have time and if you would like to help, please take a look at the website, sign up and play a little with it. We would love to get your feedback, any kind of feedback. Thanks in advance. The website's address: http://www.milanfreelance.com/frames/ET/

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Actual performance

2points
2comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
72%72% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide · Missing: https docs, excited, just released
64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
60%60% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, new · Missing: mac, agents, macos
56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
33%33% predicted probability of success on TrustMRR, 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 · 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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