This is pretty close to a process I use (regardless of model size) and I would agree getting it into a workflow I can run with a one-liner is a good idea. There are a few ways of doing this and I have tended to fallback to markdown engineering. (custom agents and subs in opencode)

It's really interesting to see the progress in the 27-30B range, they have gotten quite good but still make noticeably more hallucinations or bad assumptions (ime).

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octoberfranklin 2 hours ago | parent [-]

Yeah unfortunately the confabulations are the real problem with models of this size.

IMHO you need at least 500B weights before the confabulation problem is really under control. i.e. DeepSeek 4.1 or GLM.

I admit some days I'm just lazy and throw a whole Kimi or QwenMax at it.

	▲	verdverm 2 hours ago \| parent [-]
		> before the confabulation problem is really under control It's definitely much better at bigger sizes, but I've still seen it at the frontier when coworkers ask me about changes their Ai made to code I originally wrote. (a good habit that organically emerged on the dev team)