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Dplython, dplyr data manipulation for python
Hacker NewsDplython, dplyr data manipulation for python
Share cardActual performance
11points
1comments
Made the leaderboard
Launch Intel predictions
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BetaListSuited for BetaList early-adopters · Missing: web3, chat, crypto68%68% predicted probability of success on BetaList, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos23%23% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting21%21% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Correct prediction on native model
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