Ridesharing for Daily Commuters

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Otherwise known as "carpooling" :) I'm calling my project Ramble Rides, and it's (hopefully) the start of a technology platform to help people get to work every day. The url is: www.ramblerides.com Right now, I'm looking to validate that there is demand. If you work at a company with lots of commuters (especially in the Bay Area), I'd like to work with you to find potential carpooling matches at your company. All I need is a simple survey filled out - http://bit.ly/13I1UYM - and I'll put together a list of matches for free. I know that others operate in this space, including Zimride and regional programs like 511.org; in both cases I believe that there is an opportunity to provide better discovery of other riders (especially through social networks) and integrate value-added services like payments and real-time matching. If you're interested in learning more about my vision for this product or just generally chatting about it, my email is thomas dot vladeck at gmail dot com

Visit product View on Hacker NewsSep 10, 2013

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

79%79% 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.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io, including · Missing: https docs, excited, just released

52%52% 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 HuntUnlikely to reach the leaderboard · Strong signals: email · Missing: mac, agents, macos

50%50% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

44%44% 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

13%13% 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.

Incorrect prediction on native model

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