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Photorater.io – AI-Powered Photo Rater

Hacker News

Photorater.io – AI-Powered Photo Rater

Hey HN, I’m excited to share PhotoRater.io, a project I’ve been working on that provides AI-powered photo evaluation and improvement tips. The idea came from wanting to help people enhance their photos based on various criteria, whether for social media, professional profiles, or personal use. Features: Custom Evaluations: Get personalized feedback on your photos. Improvement Tips: Receive actionable advice on how to improve your photos. User-Friendly: Easy-to-use interface for quick evaluations. I’d love to hear your feedback and any suggestions you might have. Feel free to try it out and let me know what you think!

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
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: user · Missing: mac, agents, macos
61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: friendly, interface · Missing: plus, platform, intuitive
38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, lua, ide · Missing: https docs, just released, exist
36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
32%32% 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
10%10% 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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