Vanity Post – Create a stylish image/video of your post

Hacker News

Hey there, over the past fortnight I decided to create a stylish and simple way to create images or videos of your soon to be social media posts. Simply enter your twitter profile, toggle which vanity badges you would like, and write your post! All data is kept on the client side so we won't see your upcoming content either. We're currently live on Product Hunt as well so if you enjoyed the tool please support us there: https://www.producthunt.com/posts/vanity-post

Visit product View on Hacker NewsSep 19, 2022

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Actual performance

4points

Did not reach leaderboard

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AppSumoStrong fit for a featured deal · Strong signals: soon · Missing: plus, platform, intuitive

65%65% 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.

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

58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: video, way · Missing: mobile apps, ios, personal

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

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

34%34% 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

23%23% 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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