sweepstakes + Twitter = TweetStakes

Hacker News

A friend of mine and I were talking a while back and we realized that it would be cool to run some sweepstakes on Twitter. We put up a sign up page for a while to collect some emails and we got 200 sign ups. Once we received some sign ups we got together about 2 weekends ago and we put together a web application that lets people create sweepstakes on Twitter by counting the mentions and picking winners etc. I want to get your opinion on the site before we let those 200 people know that we are ready for beta. I would very much appreciate your feedback. Thanks in advance. The url is: http://www.tweetstak.es

Visit product View on Hacker NewsJun 14, 2012

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Hacker NewsStrong engagement from HN community · Strong signals: io · 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.

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

63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

29%29% 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

19%19% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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