Yesterday, we released Snap, the first fully sub-quadratic language model that is generally available and in production at scale, and it is consistently winning at the frontier of decision model space in the real world, and in prod! Transformers have inverse economies of scale. As the input size grows, the compute you need even on a per-token basis also grows. 10x the input requires 100x the attention compute. Our model has constant compute-per-token no matter what the input size is, during both training and inference. This enables us to make smarter models with divergent capabilities and economics, no matter what the model category is. The community response so far has been amazing! Here are some of the cool things we have seen people build: - coding agent memory - UI design - recommendation systems - legal contract review - entity recognition - academic research curation - bug finder - etc. We can't wait to see what you build next with Snap.