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OutfitScore – Free AI-powered outfit, makeup and accessory analysis

Hacker News

OutfitScore – Free AI-powered outfit, makeup and accessory analysis

Hi HN, I’m Saad and I built OutfitScore.com. We wanted to make professional style, makeup and accessory feedback accessible for everyone. With OutfitScore you can upload a photo of a full outfit, individual clothing piece, accessories, or a makeup look — the AI gives you a numerical score (0-100) in about 5 seconds plus actionable insights. :contentReference[oaicite:1]{index=1} Key features: - Free basic scoring with no account required. :contentReference[oaicite:2]{index=2} - Upload mirror selfies, flat lays, or individual items. :contentReference[oaicite:3]{index=3} - Premium plans unlock detailed breakdowns, body-type work, color theory advice, and analysis history. :contentReference[oaicite:4]{index=4} - Also handles accessories (bags, shoes, jewelry) and makeup looks. :contentReference[oaicite:5]{index=5} I’d love your feedback: - Does the score feel meaningful and trustworthy to you? - What would you like to see next (e.g., outfit comparison, style profiling, API access)? - Any biases or improvements you spot in the tool? Thanks for checking it out — happy to discuss how the system works under-the-hood or any of the tech stack if you like. Saad

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
76%76% 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 HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
41%41% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews
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.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
15%15% 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
14%14% 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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