
Actual performance
126upvotes
19comments
Made the leaderboard
Traction signals
Makers1
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.
57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.
29%29% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
21%21% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Incorrect prediction on native model
Similar products
Bo
Book discussion community for book lovers55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Book discussion community for book lovers
Th
The Groundwork Collections42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
The Groundwork Collections
li
libcodr7 – fundamental collections in the spirit of C42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
libcodr7 – fundamental collections in the spirit of C
Pr
Probed Collections42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Probed Collections
So
Something for chocolate lovers30%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Something for chocolate lovers
Pi
Pikast – The podcast app for book lovers37%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Pikast – The podcast app for book lovers
I
I built Bibliou – a platform for book lovers to read, share and connect39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
I built Bibliou – a platform for book lovers to read, share and connect
Crubles74%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Book Renting Platform
EB
EBITA, Valuation, Funding and the Chase of Theoretical Monetary Value25%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
EBITA, Valuation, Funding and the Chase of Theoretical Monetary Value
Va
Value My Mortgage31%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Value My Mortgage