All-in-One Social Media Profile Preview Tool

Hacker News

Over the years, through my experience in the internet and tech space, I’ve seen how personal branding has evolved. What was once mainly a focus for companies is now a crucial element for individuals, whether you're aiming for your dream job, building an audience, or growing a business. That’s why I created PreviewMyProfile.com. Preview your profile, bio, cover photo, and feed for Instagram, LinkedIn, YouTube, TikTok, X, and beyond, all in one place. No more jumping between multiple sites, checking desktop and mobile versions separately, or worrying about consistency. Would love to hear your thoughts!

Visit product View on Hacker NewsJan 29, 2025

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Actual performance

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Did not reach leaderboard

Launch Intel predictions

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

80%80% 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.

TrustMRRFits verified-revenue profile · Strong signals: personal, para · Missing: mobile apps, ios, entrepreneurs

62%62% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

25%25% 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, revenue

21%21% 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

0%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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