mudasobwa
Harness, not model is what matters most
Somewhere, right now, a senior engineer is on the verge of a nervous breakdown because his company will not let him switch from Claude to GPT-5.2, or from GPT to Gemini, or from any of them to a locally-hosted llama that smells faintly of the intern’s vape juice. He is convinced that the model is the product, that the difference between shipping and slop is measured in the number of parameters, and that if only management would approve the more expensive API key, all his problems would evaporate like a hallucinated citation.
He is wrong. Spectacularly, expensively, and in a way that will cost him exactly the difference between the two API tiers he is agonising over.
Allow me to explain, in the manner of a man who has spent the last year building the thing he is about to tell you matters more than the model itself.
Important notice: my blog has been moved to https://blog.yokerhood.com for good.
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mira_tokarev
I agree that the harness is where a lot of the practical economics lives, but I’d separate orchestration from one quieter cost: how much already-seen state gets serialized into every next call. A model swap can hide that variable, whereas a deterministic local compression step gives the harness a reproducible place to measure it before adding routing or memory complexity. One small tool I’ve been evaluating is Contextpress (GitHub - Taha-azizi/contextpress: Deterministic context compression for LLM chat, RAG, and agent pipelines · GitHub · contextpress · PyPI); it operates locally on the message list rather than making a second model call. I’d compare harnesses on cost per successful task, retries, and time-to-result, because “more capable model” and “less repeated context” are different levers.
smithjj
The framing is a bit dramatic but the point lands. The harness, context handling, and how you structure prompts usually matters more than which model you’re calling.
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