Fashiongram - Shoppable Instagram Fashion

Hacker News

Hey, Link here: http://apluscontent.de/fashiongram/ First off: This is obviously a super simple MVP. I know 'Refresh for more' is not a great solution, neither is the 'hover for more info'. But you should get the idea about what it does - it links popular Fashion on Instagram with online shops in one click. Content is added manually by checking for images/captions with links from the popular feed, and putting useful "combinations" into a database. Code might be far away from best practices, but I'm a hobbyist coder with limited experience in PHP/MYSQL/CSS/HTML. Feedback is appreciated (especially in terms of legal/copyright issues). Thanks a lot!

Visit product View on Hacker NewsJan 9, 2013

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1points

Did not reach leaderboard

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

66%66% 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.

AppSumoStrong fit for a featured deal · Strong signals: plus · Missing: platform, intuitive, reviews

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

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

36%36% 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, io · Missing: https docs, excited, just released

33%33% 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

16%16% 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

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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