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GenOutfit – AI and scraping and fashion =personalized outfit generation

Hacker News

GenOutfit – AI and scraping and fashion =personalized outfit generation

I built a web app called GenOutfit that gives you outfit ideas tailored to your body type, style preferences, and whatever occasion you're dressing for. The idea came from a mix of personal frustration and observation. I like to dress decently, but honestly, sometimes I just can't picture how certain outfits would actually look on me. At the same time, I noticed my gf spends ages scrolling Pinterest and Instagram for outfit inspiration. That's when it kind of clicked, what if there was a way to see outfit ideas that look like they'd work for you? So I started with a spreadsheet , I listed out about 97 fashion influencer accounts and tagged each one by style (streetwear, casual, etc.), gender, and body type (athletic, plus-size, etc.). Then I scraped their Instagram content, images and captions and used AI to describe what each person was wearing in the pictures. Everything got stored in a database. Now, when someone uses the app, they just enter a few things like body type, skin tone, and optionally style preferences. Then they choose an occasion, like a date night or business event and the system pulls a look that actually fits their profile. The images are generated using AI, but the inspiration behind them comes from real fashion content that's current and trending. It's still early and there's a lot to improve, but I'm already using it myself and getting some value out of it. Hope it's helpful for others too, happy to chat or hear any feedback.

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
77%77% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: using · Missing: mac, agents, macos
69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs
53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews
33%33% predicted probability of success on AppSumo, 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
28%28% 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
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: chat · Missing: web3, crypto, cryptocurrency
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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