But yes, I'm aware no ones got anywhere near there, mostly because most of the focus is on exploding the context and parameters. I'm saying that phase is done.
I'm also not sure what you mean by "we aren't there yet." Where?
Sorry, not trying to be difficult or dense, I'm just not sure what you are referring to.
> mostly because most of the focus is on exploding the context and parameters.
Large context allows a surprising amount of "learning" to happen at inference time rather than training time. I think that is relatively unexplored. As long as the model itself has passed a certain threshold of smarts, and the context is large enough (Gemini and its million token context being WAY past that point) you are not really limited by the model, you are only limited by how good the stuff you feed into that context is.
That's what happened when, nearly a year ago, I saw a major leap in capabilities that happened entirely on my end.... not in the AI, but in code written by the AI. I found it genuinely frighting to be honest. I think OpenClaw tapped into something similar, which seemed to surprise a lot of people. There were latent capabilities in the AI that were unknown until brought out by a clever harness.
Anyway, are you speaking of the harness? The harness on mine isn't AI, so speed just isn't an issue.
Chat Jimmy runs ~300X faster than the ~50 tok/s you are used to. What could you do differently when you are able to generate code 3,000 - 30,000X as fast as you could code it yourself? What if it was all good quality code? What would you do differently if it were 100,000X faster? mtok/s? gtok/s?
use the big models to code an adaptive small model. train it to use and build tools. give it a standard temple language for any project and bake it into a chip.
right now, LLMs are great because they dont need much data pruning, but once they break through to the functional components, the first thing to do is train a well scoped harness builder.