I

I built this app to share photos at weddings and other social events

Hacker News

I built this app to share photos at weddings and other social events

Hi all, I built this Twilio + Node app to allow folks at weddings to text a number and share their pictures in a slideshow displayable via projector. https://www.airpair.com/node.js/posts/using-twilio-to-advance-the-state-of-wedding-tech I'm also thinking about adding in the ability to save pictures and generate your own Twilio number through a GUI, possibly making it into a larger project that facilitates group photo sharing through SMS. Thoughts? Thanks HN!

Visit product View on Hacker NewsMar 29, 2015

Actual performance

5points

3comments

Did not reach leaderboard

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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.

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

54%54% 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 · Missing: plus, platform, intuitive

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

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

34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

24%24% predicted probability of success on Product Hunt, 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 · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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