Let's say you have 40 files and you want to determine which are trash that you could delete.
You can iterate over them one by one, potentially doing many turns for each and execute it in 40*n turns and a huge context, or you can just let the LLM write a script that is executed "in the brain" and has access to LLM calls itself.
So you can just do
for (var f : files) {
if (llm.model("is this file trash?", [f]) {
rm f;
}
}
And you can even execute that in parallel, each call costing you minimal tokens (not needing every context just a targeted prompt plus much smaller model)
Thanks. I guess that's something you cannot easily do today.
Given my limitation with Qwen3.8-Flash of about 55t/s tg and ~1kt/s pp most of the work happening in harnesses today (around subagents, excessive use of skills, tools, huge system prompts etc.) is just not very interesting. My workflow works really well without any of it, tbh it seems more of a way to sell more tokens when you realize how much compute these techniques use to achieve modest workflow improvements.
I can really recommend running your own models to really appreciate the power and compute that goes into your prompts and tokens. Qwen3.8-Flash is good enough (Opus 4.7 level) for all of my coding needs.
You can iterate over them one by one, potentially doing many turns for each and execute it in 40*n turns and a huge context, or you can just let the LLM write a script that is executed "in the brain" and has access to LLM calls itself.
So you can just do
And you can even execute that in parallel, each call costing you minimal tokens (not needing every context just a targeted prompt plus much smaller model)