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Tool to download videos from RedNote/Xiaohongshu securely

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Tool to download videos from RedNote/Xiaohongshu securely

RedNote Video Downloader is a web application that allows users to download videos from RedNote (also known as Xiaohongshu or Little Red Book). It provides a simple and efficient way to save RedNote videos for offline viewing or content creation purposes. Please note that we are not affiliated with, endorsed by, or connected to RedNote/Xiaohongshu in any way.

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Actual performance

5points
1comments
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
78%78% predicted probability of success on Indie Hackers, 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 · Strong signals: efficient, users · Missing: plus, platform, intuitive
76%76% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: video, users, way · Missing: mobile apps, ios, personal
65%65% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
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.
Product HuntUnlikely to reach the leaderboard · Strong signals: user · Missing: mac, agents, macos
16%16% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
14%14% predicted probability of success on BetaList, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% 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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