Li
Lintly – Track code quality violations in a Python codebase via GitHub
Lintly – Track code quality violations in a Python codebase via GitHub
Share cardActual performance
2points
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
49%49% predicted probability of success on BetaList, based on ML models trained on real launch data.
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
19%19% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
17%17% 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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