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Polibench – compare political bias across AI models

Hacker News

Polibench – compare political bias across AI models

Polibench runs the Political Compass questions across AI models so you can compare responses side by side. No signup. Built on top of work by @theo ( https://twitter.com/theo ) and @HolyCoward ( https://twitter.com/HolyCoward ). Question set is based on the Political Compass: https://www.politicalcompass.org/ Early and rough. Feedback welcome on revealing questions, possible misuse, and ideas for extending it. Happy to answer questions.

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
49%49% 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, podcasting
46%46% 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: ide, io · Missing: https docs, excited, just released
45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: model, models · Missing: mac, agents, macos
31%31% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% 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
8%8% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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