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A simple benchmark for AIGC that is actually difficult
Hacker NewsA simple benchmark for AIGC that is actually difficult
I've created a benchmark of 10 text-to-image tasks and matched 10 pictures for each task, which are pictures that you can easily find on Google. It is extremely easy to check that for every task, all the current AIGC platforms are totally incapable of creating pictures that can fulfill them, so this is a benchmark that can be used to check the intelligence of future AIGC frameworks.
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TrustMRRFits verified-revenue profile · Strong signals: google · Missing: mobile apps, ios, personal58%58% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntUnlikely to reach the leaderboard · Strong signals: google, tasks · Missing: mac, agents, macos48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting41%41% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released38%38% 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: platform · Missing: plus, intuitive, reviews23%23% 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, revenue14%14% 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, crypto1%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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