Draped – Virtual try-on to help people choose outfits they own

Hacker News

Hi HN, I’m building Draped, a virtual try-on product focused on helping people choose outfits using clothes they already own. The idea came from a simple problem: People have clothes, but still don’t know what to wear each day. With Draped, users can: Digitize their wardrobe Get outfit suggestions based on context (weather, day, occasion) Preview outfits via a lightweight virtual try-on (still early) I’m currently iterating fast and validating use cases before scaling. I’d really appreciate: Feedback on the problem itself Thoughts on the virtual try-on approach Any similar products I should study Website: https://draped.me Happy to answer questions and share what I learn building this. Thanks

Visit product View on Hacker NewsDec 23, 2025

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

81%81% 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.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

49%49% 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

47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: user, context, using · Missing: mac, agents, macos

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

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

27%27% 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

15%15% 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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