A beautiful photo gallery for your Dropbox photos

Hacker News

[Demo] https://dl.dropbox.com/s/l71aiopn8ua6gv5/magicphotoshow-photobox.html [Features] - Uses the Photobox plugin: http://dropthebit.com/500/photobox-css3-image-gallery-jquery-plugin/ - Easy to create and manage - Beautiful - Responsive - Supports a soundtrack - Supports a background cover image [Download] https://www.dropbox.com/s/w07ly2tftxvhtoe/magicphotoshow-photobox.html [Version] MagicPhotoShow-0.1 [Requires] - Dropbox [Install] 1. Copy the file into any Dropbox folder with photos 2. Open the file (either via the Dropbox website or locally) and authorize the app to scan your Dropbox folder for photos 3. MagicPhotoShow will automatically generate a gallery from the photos in the folder [Bugs] - Audio doesn't work in the Facebook in-app browser - Breaks when using music files with non ASCII file names - More to come... =(

Visit product View on Hacker NewsMay 29, 2013

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Actual performance

2points

1comments

Did not reach leaderboard

Launch Intel predictions

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

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.

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

43%43% 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

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

Product HuntUnlikely to reach the leaderboard · Strong signals: using, open · 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

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: audio · Missing: web3, chat, crypto

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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