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The perfect time-waster – browse chat screenshots

Hacker News

The perfect time-waster – browse chat screenshots

I built ScreenToMe, a platform where you can share and discover those chat screenshots we all secretly enjoy reading. - Super smooth UX: Tinder-style swiping interface on mobile, arrow keys on desktop (←/→) - Multi-language support: filter content by language or discover across all - Categories like Funny, Cringe, Dark Humor, Dating, Red Flags, etc. - Strong moderation: all content is reviewed before publishing - Privacy first: must blur/remove personal info It's like a curated museum of internet conversations - you know, those screenshots you keep sending to your friends saying "you won't believe this one". Now there's a place for them. The site is completely free, no account needed to browse and vote. Just pure entertainment in bite-sized chat form. Try it out (and add your best screenshots!)

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
69%69% 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.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, interface · Missing: plus, intuitive, reviews
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: personal · Missing: mobile apps, ios, entrepreneurs
34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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