An app that allows you to save your spare change to help you travel

Hacker News

Hello, My name is Jason Gray and I am founder of Chrip. Chris is a mobile first app that allows you to save your spare change from everyday purchases to help you finance your next trip. I recently built and launched the prototype on my landing page. If you could be so kind to check it out and sign up for our emailing list as we will need beta testers very soon. I call it the Acorns of trip savings. Landing Page: www.chrip.co Email: jason.chrip@gmail.com I am looking to build a team. If, after you looking at the video you are interested in joining the team -- please send me an email. My team is awesome and very fun to be around. Thank you!

Visit product View on Hacker NewsNov 12, 2015

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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.

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

44%44% 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: ide · Missing: https docs, excited, just released

40%40% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

38%38% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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