AI to Find Your Best Dating App Photos

Hacker News

I built a web app that uses a Resnet50 based model to score a photo's quality for dating apps. The app also contextualizes the score by showing how your photo scored compared to other users and giving feedback on how the photo can be improved. The feedback function uses deep learning models to identify the presence of certain characteristics in a user's photos to tell them how to improve, or what's good about the photo. There is a sign up component (sorry about that!). Would love any feedback on how to improve the product, market it, etc. Thank you! You can email me at loughlin@firstpic.ai

Visit product View on Hacker NewsFeb 17, 2024

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78%78% 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 HuntOn track for Day 1 leaderboard · Strong signals: model, apps, user · Missing: mac, agents, macos

55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: apps, users · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, 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

49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

21%21% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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