Actual performance
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →68%68% 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.
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
46%46% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
44%44% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.
30%30% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
16%16% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Correct prediction on native model
Similar products
Tr
Training classifiers with Create ML – fruits, flowers, xray, sentiment56%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Training classifiers with Create ML – fruits, flowers, xray, sentiment
ZolTrain57%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Training the cannabis workforce
La
Labeled Training Data41%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Labeled Training Data
Da
Data Yoshi – Find the Newest Data and ML Jobs56%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Data Yoshi – Find the Newest Data and ML Jobs
In
In ML Data is not everything – a business perspective55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
In ML Data is not everything – a business perspective
ML
ML data annotations and tagging made easy65%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
ML data annotations and tagging made easy
Sp
SpotML – Managed ML Training on Cheap AWS/GCP Spot Instances58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
SpotML – Managed ML Training on Cheap AWS/GCP Spot Instances
Sp
SpotML – Pip Package for Managed ML Training on AWS Spot Instances45%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
SpotML – Pip Package for Managed ML Training on AWS Spot Instances
La
Lance – Alternative to Parquet for ML data60%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Lance – Alternative to Parquet for ML data
Sc
Scaling ML to 8M users with 3 data scientists71%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Scaling ML to 8M users with 3 data scientists