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Building a Fashion Search Engine. Need Feedback

Hacker News

Building a Fashion Search Engine. Need Feedback

Hi all - I'm building a Fashion Search Engine called FashionFeed ( http://fashionfeed.app ). I want to solve my own problem of not seeing clothing models that look like me on shopping sites. So I made a feed where you can filter by Ethnicity, Gender, Age, Height, Weight. If you see a photo you like, you can click into it and see the outfit product links. I feel like this may be a better way to shop, than going to a site like Shein or Zara, and just being overwhelmed with options. Looking for any feedback on this & ways I can improve please! Thanks.

Visit productView on Hacker NewsSep 9, 2023
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models · Missing: mac, agents, macos
66%66% 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.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, 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
38%38% 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: way · Missing: mobile apps, ios, personal
31%31% 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
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
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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