We
We made a Tesseract Python wrapper using pybind11 (not the CLI)
We made a Tesseract Python wrapper using pybind11 (not the CLI)
Share cardActual performance
7points
5comments
Made the leaderboard
Launch Intel predictions
Analyze your own launch →69%69% predicted probability of success on BetaList, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
34%34% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
22%22% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Incorrect prediction on native model
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