I've also tried reverse engineering the algorithm recently to plot a scattergraph of story ranks vs age-adjusted score. I'm looking for outliers in order to wor

I've also tried reverse engineering the algorithm recently to plot a scattergraph of story ranks vs age-adjusted score. I'm looking for outliers in order to work out where HN moderators have intervened. https://docs.google.com/spreadsheets/d/1q6HS4Gcw8KVp5nTRC_H0T6Fg5jiGbo6YgWwrOq5WXdo/edit?usp=sharing https://docs.google.com/spreadsheets/d/1q6HS4Gcw8KVp5nTRC_H0... It turned out to be very difficult as there are so many arbitrary factors, and the HN API doesn't expose those factors. It's good to see the OP's graphs over time, showing the large jumps which clearly indicate manual intervention. That's a much more effective approach than my own analysis. My motivation is to surface any patterns in those interventions. Annecdotally I notice that positive news about Elon Musk and his companies has been suppressed more than once. * Elon Musk nobel prize nomination: https://twitter.com/chrisbeach/status/1760277351621878066 https://twitter.com/chrisbeach/status/1760277351621878066 * xAI open-sourcing Grok: https://twitter.com/chrisbeach/status/1769755409391222868 https://twitter.com/chrisbeach/status/1769755409391222868 Obviously, the moderators are responding to things like user flags, but in my opinion there should be more transparency around this, in order to prevent users brigading certain topics, using flags inappropriately, to manipulate the narrative.