While one-shot coding results can be remarkable, humans try stuff and learn from the stuff they try. Some of it building on trusted components. It is strange to hold LLMs to a higher standard.
From: Friam <[email protected]> On Behalf Of Jon Zingale Sent: Monday, September 28, 2026 6:51 PM To: The Friday Morning Applied Complexity Coffee Group <[email protected]> Subject: Re: [FRIAM] Circuit bending Because I almost never go full harness, I forget that harnesses are a fine way to produce code. Often, I sit much closer to the zero-shot side of the spectrum, with a few iterations and myself as the validator. This got me thinking about the degree to which frontier model developers privilege our harness-produced code when generating their synthetic data. To the degree that I am willing to pay for tokens, I am, in some sense, vouching for the potential training value of the generated code. Even if it’s wrong, it may be better than random, in that it is text someone wanted. Path dependence and all of that. Producing idiosyncratic generators seems a lot more valuable than slurping up and training on pure state.
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