Po
Pol/ite – /pol/ but posts are all polite
Hacker NewsPol/ite – /pol/ but posts are all polite
What woud it be like to read fringe political views forcibly made polite by way of LLM? System prompt (gemini-2.5-flash-lite): "You are rewriting 4chan posts to be more polite while preserving their original meaning and tone. Don't add unnecessary verbosity; keep it concise. Make sure to preserve formatting including markdown, links and greentext."
Share cardActual performance
6points
5comments
Did not reach leaderboard
Launch Intel predictions
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Product HuntOn track for Day 1 leaderboard · Strong signals: gemini · Missing: mac, agents, macos70%70% 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.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal59%59% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Strong signals: including, gemini · Missing: supports, reddit linkedin, podcasting40%40% 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: including · Missing: https docs, excited, just released35%35% 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, revenue22%22% 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, crypto6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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