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Slowcialize – A calmer social media platform for deeper connection
Hacker NewsSlowcialize – A calmer social media platform for deeper connection
Social media promised connection. What we got was noise, performance, and endless scrolling. We wanted something quieter — a space that actually helps you stay in touch with people who matter. So we built Slowcialize: no feeds, no likes, no performance. Just simple, reflective updates shared at your pace. It's being piloted at our university in India, and 250+ people are already using it. Would love your thoughts on how we can improve it — thanks!
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3points
1comments
Did not reach leaderboard
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AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews66%66% predicted probability of success on AppSumo, 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: using · Missing: mac, agents, macos60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting49%49% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released32%32% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue25%25% 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, crypto0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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