My

My web+mobile app - Travel memories;collaborative,safe,easy

Hacker News

My web+mobile app - Travel memories;collaborative,safe,easy

A mobile+web app that helps you(+other trip members) capture travel memories (collaboratively) and share a beautiful experience safely & quickly. http://blog.banjarey.com/2012/08/what-are-we-building-in-5-points/ 1 - Works on-the-go - never miss a checkin, a photo, video or an activity that you did. 2 - Built for groups - Everyone collaborates on building the entire trip experience. 3 - Privacy. Easy sharing. Trip members can share with anyone - only that particular trip member's items are viewable. Appreciate and Questions, Comments, feedback

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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: activity · Missing: mac, agents, macos
62%62% 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.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
51%51% predicted probability of success on Indie Hackers, 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
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
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: collaborate · Missing: web3, chat, crypto
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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