I used to think that, but ended up going the other direction, partly because I don't have the wherewithall to build a model but then I realized, with existing models that can take more than a tiny amount of context, you can just let any model bootstrap itself with a good prompt sent by the system.

There's a ton of other tricks to it, but mostly keeping the protocol simple for the AI so it can concentrate on coding logic and not stuff like managing BS boilerplate, dependencies, etc. (for instance I make extensive use of things like abstract syntax tree library to help with surgical edits from the LLM)

That said, I would be very open to collaborating with someone who builds such small models, I don't think the system strictly needs it, but it also could have some extra power if it had it.

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andai 5 hours ago | parent | next [-]

> mine also makes extensive use of things like abstract syntax tree library to help with surgical edits from the LLM

Tell me more! This takes me way back. I did one like this in the GPT-4 days! (8k context window)

	▲	robbrown451 5 hours ago \| parent [-]
		Start off with my video!!! You can also try it with zero setup (you can code right there on the static web page, it will save your edits in the browser indexed DB, and hotpatch them back into the code before it runs it.... also you can grant permission to the browser to read/write to a local directory) recursi.dev Seriously, I'm looking for collaborators. There's upwards of 80,000 lines of code in the editor system, a lot to it to make sure that even newbies don't get stuck.... so that's kind of proof the system works since it doesn't break down when the codebase grows large.

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cyanydeez 4 hours ago | parent | prev [-]

I'm aware we're not there yet, but think of something like https://chatjimmy.ai/ ; at some point, you're going to be able to dynamically build the harness so it creates the necessary consistency & dynamicism at a speed unheard of.

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.

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robbrown451 4 hours ago | parent [-]

I'm not sure what I am looking at with chatjimmy.... what is special about it? Speed?

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.

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cyanydeez 3 hours ago | parent [-]

image a streamlined model whose only job is to build then execute the harness at the speed youre seeing in chat jimmy.

	▲	robbrown451 an hour ago \| parent [-]
		Speed isn't really a big deal for me. I want good quality code. It's already able to generate code 10-100X as fast as I could code it myself. Anyway, are you speaking of the harness? The harness on mine isn't AI, so speed just isn't an issue.