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The primary technical risk associated with Rex R is the "Recursion Depth Problem." If the R-module is too aggressive in its auditing, it could trigger an infinite loop of self-doubt, rendering the system catatonic. This is known in computer science as "analysis paralysis." Designers must calibrate the "Confidence Threshold" to ensure that the system knows when to stop reflecting and act.
library(rex) df <- rex_read("logs/2024/*.csv") filtered <- df[df$status == 404, ] summarized <- aggregate(filtered$response_time, by=list(filtered$host), FUN=mean) result <- as.data.frame(summarized) # Only now does computation happen
The primary technical risk associated with Rex R is the "Recursion Depth Problem." If the R-module is too aggressive in its auditing, it could trigger an infinite loop of self-doubt, rendering the system catatonic. This is known in computer science as "analysis paralysis." Designers must calibrate the "Confidence Threshold" to ensure that the system knows when to stop reflecting and act.
library(rex) df <- rex_read("logs/2024/*.csv") filtered <- df[df$status == 404, ] summarized <- aggregate(filtered$response_time, by=list(filtered$host), FUN=mean) result <- as.data.frame(summarized) # Only now does computation happen
