Dr
DropCSS – A simple, thorough and fast unused-CSS cleaner
Hacker NewsDropCSS – A simple, thorough and fast unused-CSS cleaner
Share cardActual performance
134points
25comments
Made the leaderboard
Launch Intel predictions
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BetaListSuited for BetaList early-adopters · Missing: web3, chat, crypto58%58% 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.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released46%46% 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, personal44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue26%26% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos17%17% 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, podcasting17%17% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Incorrect prediction on native model
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