Stripp – capture your travel experience

Hacker News

Hi guys! I traveled to Tokyo a few months back and fell in love with the travel lifestyle. I took many pictures and videos. Social media was great for sharing my travel moments, however it didn't capture my experience. Because of this, I created a new app for capturing and sharing authentic travel experiences. To me, its not about the beautiful picture perfect moments, but the struggles to get there. The app is called Stripp. Stripp gives you a beginning and an end, you fill the middle with meaningful content that moves your story forward. What are the benefits? Separation: Under your profile will be a collection of your stripps. You create a new stripp for every travel adventure. Unlimited Posting: You and those invited can post unlimited photos and videos to your stripp. Privacy: Everything posted is private! Only you and those invited can see the photos and videos. Sharing: If you decide to share, you share the stripp. The stripp will playback in chronological order and starts with a cover you design, followed by all your posts. You can share it exclusively to a few users or all of your followers If you are interested, i wrote this article: https://www.strippapp.com/new-blog/share-experiences to give you more info. Thanks and let me know what you think! Peter

Visit product View on Hacker NewsJun 28, 2018

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91%91% 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 HuntOn track for Day 1 leaderboard · Strong signals: user, new · Missing: mac, agents, macos

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

28%28% 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

11%11% 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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