i am going to try to address a couple of topics that came up often:

- i'll keep publishing stronger evals, including more difficult coding and agentic benchmarks, to map out more precisely the differences with sota

- the public eval dashboard will keep expanding and be updated (very open to more benchmark suggestions as well!)

- some people found issues in the eval dashboard ui and the sign up flow, should be now all fixed in prod

some important precisions as well:

- NO credit card is required to try Echo

- each acount includes 10$ of free credits to try on both the API and the chat

on the approach itself: the idea i'm exploring is more broader than model routing, i'm looking at how to allocate inference efficiently across open-weight models, deciding not only which models to use, but also how much computation a request deserves and how intermediate work should be combined.

ensembling by itself is not new. since random forests and probably even before in statistics/classic ml we knew that bringing multiple models together can outperform individual ones. the interesting problem for Echo is how to model and leverage this without paying the full ensemble cost at each request.

while there are conceptual similarities with systems like Fusion or Fugu, the architecture and optimization objective are different.

thanks again for all the thoughtful feedback.