Cr
Actual performance
3points
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →79%79% 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.
51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
40%40% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Incorrect prediction on native model
Similar products
Go
Go-apt-cacher and go-apt-mirror42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Go-apt-cacher and go-apt-mirror
Te
Textfiles Mirror42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Textfiles Mirror
El
ElgooG (Google Mirror)47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
ElgooG (Google Mirror)
So
Social Mirror38%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Social Mirror
Ma
Make a programmable mirror53%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Make a programmable mirror
Sc
Sci-Hub Mirror38%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Sci-Hub Mirror
Mo
Morgan – PyPI Mirror for Restricted/Offline Environments49%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Morgan – PyPI Mirror for Restricted/Offline Environments
LinkedinScope25%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
LinkedInScope: the AI mirror your ego didn’t ask for
ZF
ZFS utils to mirror snapshots and apply retention39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
ZFS utils to mirror snapshots and apply retention
A
A better smart mirror47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
A better smart mirror