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Pseudo Profound AI Bullshit Generator
Hacker NewsPseudo Profound AI Bullshit Generator
Yesterday I read Pennycook etc al.'s paper on pseudo-profound bullshit, and realized that's what I hate most about the current AI culture. Meaningless but important sounding bullshit that's said only to make the speaker sound smart. So then I made this little web app to randomly generate AI bullshit. Your next pitch deck awaits. Pennycook's paper: https://www.cambridge.org/core/journals/judgment-and-decisio...
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3points
1comments
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting26%26% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · 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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