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Priceless AI is currently the top product on Product Hunt
Hacker NewsPriceless AI is currently the top product on Product Hunt
Hi everyone, I noticed that AWS Rekognition and Google Cloud Vision offerings are very expensive when you need to process 100k+ images per month. I decided to solve this problem by using clever low-level optimization to better utilize the hardware while keeping the monthly price fixed Hope you check it out and give me your feedback as it means a lot to me :) https://www.producthunt.com/posts/priceless-ai
Share cardActual performance
2points
Did not reach leaderboard
Launch Intel predictions
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting66%66% 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: google, using · Missing: mac, agents, macos60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released53%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.
TrustMRRFits verified-revenue profile · Strong signals: month, google, monthly · Missing: mobile apps, ios, personal51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive25%25% 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, revenue20%20% 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, crypto0%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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