Pe
Peakedin – archiving LinkedIn's most unhinged posts as satire
Hacker NewsPeakedin – archiving LinkedIn's most unhinged posts as satire
A few weeks ago I was scrolling LinkedIn (my fault, I know) and noticed the platform was full of posts that had absolutely no business being there. So I built PeakedIn. A weekly newsletter, five posts every Saturday, featuring LinkedIn's most unhinged moments. Best part is people can nominate posts as well I guess it will give another post idea to them "I am thrilled to announce I was featured in PeakedIn" lol anywho lmk your thoughts
Share cardActual performance
1points
Did not reach leaderboard
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Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting42%42% 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.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos35%35% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide · Missing: https docs, excited, just released29%29% 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, crypto3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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