An app for nightlife, greeklife, and gyms. Looking for feedback

Hacker News

We built Roodle http://www.roodleapp.com/ - An app that lets you instantly rate your location’s social potential and pass on the info to your friends. It’s simple. It’s private. It’s free. You can rate 3 factors: crowd quality, male to female ratio, and population density. Also, signing up is not required to see ratings. It's not connected to Facebook or Twitter. It doesn't even touch your phonebook. You can add people by searching their first name, last, or username if you know it. We soft launched on iOS and Android several months ago in Arlington, VA and it went as well as a soft launch could have gone. I've added in all the Greek Houses, Bars, and Gyms in Blacksburg (Virginia Tech), Stillwater (OKState), and Greensboro (ECU), plus spots in Cambridge, NYC, and Charlotte. I'm looking for people to try it out and give us feedback. You can even use it for Gyms to avoid people. It's still rough around the edges, but it's a start. We need beta testers. Even though it's a location based app, you can use/test it from anywhere. I'll even add a bunch of places around you if you want. Contact me on here or at jon [at] roodleapp.com to chat about anything. We have one press release out. http://www.arlnow.com/press-releases/localized-bar-and-hotspot-rating-app-launches-in-arlington/ - iOS 7+ - https://itunes.apple.com/us/app/roodle/id889246753?mt=8 - Android 4.4+ - https://play.google.com/store/apps/details?id=com.Roodle.RoodleApp

Visit product View on Hacker NewsApr 22, 2015

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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 · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

55%55% 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.

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

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: ios, apps, month · Missing: mobile apps, personal, entrepreneurs

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: apple, google, apps · Missing: mac, agents, macos

45%45% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

32%32% 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

28%28% 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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