SocialFolders, "Dropbox for social"

Hacker News

Hey fellow HN-ers, I'd like to ask HN about my latest startup: SocialFolders. We take a Dropbox-like approach to managing your content on your social networks by mirroring all the content you have on these networks in folders on your computer. Moving this content around, between folders or between services, is mirrored in the all-mighty "cloud" (ugh, I hate this word). We'd love to know what you think. We think our approach solves the problem of managing content at scale. We put together a quick 2'30 screencast to show HN how it works: http://www.youtube.com/watch?v=4s2TFsVezZQ If you want to try it out, you can download the app (for free of course) on socialfolders.me. Thanks for your feedback. Next step: adding a kitty to the video and sending it to Mashable! Edit: upvote the clickable links if you can please :)

Visit product View on Hacker NewsJan 27, 2012

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

83%83% 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.

Product HuntOn track for Day 1 leaderboard · Strong signals: computer · Missing: mac, agents, macos

64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide · Missing: https docs, excited, just released

53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal

31%31% 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, revenue

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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