Fynspo. – Shop clothes that match your taste

Hacker News

Hey HN! I'm Dhruv from fynspo. We're building a unified app to shop for clothes across any brand, and search based on outfits personalized to your style. We've been working on our first version for about 3 weeks and we're excited to finally have it published on the Apple App Store. This idea was born when we participated in a SPCxOpenAI hackathon a few months back and built an early version using GPT-4V. I've found that most technical solutions should stay away from using LLMs, they suck. Instead, creating a pipeline consisting of smaller ML models trained for specific tasks yields more accurate, cheaper, and faster results. I’d love for you to try it out and share your feedback. We are making rapid improvements for our next update and any thoughts or suggestions would be very appreciated!

Visit product View on Hacker NewsJul 24, 2024

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, apple, models · Missing: mac, agents, macos

76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, pipe · Missing: https docs, just released, exist

60%60% 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

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

23%23% 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

12%12% 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.

Incorrect prediction on native model

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