Ripple – a proximity based method to spread information

Hacker News

Hi HN, I've been working on a concept called Ripple. It allows users to create content and send it to people geographically near them. If these folks find the content engaging or relevant, they can pass on the content to more people near them. Information on Ripple travels so long and so far as the content is found relevant or engaging. So far, Ripple has been attracted an audience made up mostly of photographers, artists, and bloggers who are looking to expose their work to a large audience. We're excited about the potential for Ripple to be the go-to platform for people looking to expose their views and work with people near them and around the world, and as a great way to allow for local updates on events and other information. Take a look for yourselves! http://getRipple.io iOS: https://itunes.apple.com/us/app/ripple-explore-your-world/id946792245?mt=8 Android:https://play.google.com/store/apps/details?id=com.KefiLabs.Ripple

Visit product View on Hacker NewsJul 24, 2015

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4points

3comments

Did not reach leaderboard

Launch Intel predictions

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

74%74% 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.

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

60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

59%59% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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.

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

41%41% 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

15%15% 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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