right, the model will listen to you for ~32k context; half of that is just getting them into the "downhill" path for being a coding assistant. You can throw in whatever your heart's desire and if it's a single fix, it might listen to you; but treating it like a smart-IDE basically means it'll follow whatever dominant pattern is in the training data.

If we had smart capitalism (the SAME PROBLEM) we'd realize what we want are per-framework or per-OS models that simply are 80% how to english and 20% the tools we need for a given project.

But that's no where the money is.

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m_ke 6 hours ago | parent [-]

yeah I tried rules, hooks and forbidding things like pip / python3 but it just led to the model failing to do what it wants and a bunch of token churn trying to get around my more rigid constraints.

main problem is that the harness files get loaded into context early in the session and slowly wash away as new information comes in.

	▲	cyanydeez 4 hours ago \| parent [-]
		i've been playing around with https://github.com/bytedance/deer-flow and they seem to figure out how to get around context sizing issues and has a more automated prompt system. Essentially the problem with current hanrsses is they just can't find/evict context and want to ensure KV cache isn't evicted. If they evict the old prompt and tried to keep all the proper rules, then you get token churn anyway, as what you want is an entirely new chain of events where your rules/guidance sit at the front of the model chain. What we're getting to is there really isn't an economical way for cloud models to do it; you can do it with a local model, but at comfortable speeds, you need smaller sized. I think a good harness via Qwen3.6-35B-A3B is possible, as the token gen is only 3B since that's the active parameters. So it's mostly that to keep rules pinned to the top of the context will either bloat the model or it'll evict the KV cache.