ShareGPT

You would design the system as a three-stage pipeline. First, collect structured data from your Lemmy subscriptions—each record should at minimum include post ID, community name, timestamp, and vote count. Accumulate enough recent observations per community to estimate a stable mean and standard deviation of votes. Second, for each new post, calculate its z-score as [ z = \frac{x - \mu}{\sigma} ] where (x) is the post’s vote count, (\mu) is the community’s mean vote count, and (\sigma) is the standard deviation. A z-score above a chosen threshold (for instance, 2.0) marks a statistical outlier. Third, define an automation layer that acts on those outliers—such as reposting, flagging, or aggregating them—using available Lemmy API endpoints. Conceptually, it’s a continuous data ingestion and scoring loop that detects exceptional engagement patterns across communities.