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I'm building the Costco for apparel

Hacker News

I'm building the Costco for apparel

Hey there, I’m building blnkfabrics.com Think of it as a Costco for apparel, pay a yearly membership and get the best prices on high quality clothes with no weird logos or designs. The apparel is typically sold at cost+10%(to cover handling and logistics) Site is almost finished, technically is fully operational meaning you can buy the membership right now and the orders will fulfill, but need to fix some factors(landing page, copy, favicon, etc.) Also make sure to check the FAQ page, will answer most questions. If there are other questions you think I should answer let me know Would love any feedback, be honest! Thanks.

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Actual performance

21points
60comments
Made the leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
83%83% 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.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
38%38% predicted probability of success on Product Hunt, 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.
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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