Using digitally made garments for custom fit fashion

Hacker News

House of Supr is revolutionizing the way women shop for clothing with our innovative made-to-fit platform. Using advanced digital design and on-demand production techniques, we create unique and comfortable garments that are tailored to your specific measurements and fit preferences, without the waste and inefficiencies of traditional retail. Our platform also offers a range of customization options, including pockets, sleeve length, dress length, and initials, to help you create a one-of-a-kind garment that is truly your own. And with a range of sustainable fabrics to choose from, we are committed to reducing the environmental impact of our production. Join the House of Supr community and experience the future of fashion today.

Visit product View on Hacker NewsJan 2, 2023

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Actual performance

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2comments

Did not reach leaderboard

Launch Intel predictions

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

68%68% 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: platform · Missing: plus, intuitive, reviews

58%58% 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

35%35% 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, including · Missing: https docs, excited, just released

35%35% 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: using · Missing: mac, agents, macos

32%32% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

11%11% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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