[0xbadcafebee]

> They became popular because teams across the industry analyzed code responsible for bugs/SEVs, and all found high correlation between these metrics and shipping defects.

Yes, based on research of human code. LLMs write code differently. We should question whether the human research applies to LLMs at all. (You wouldn't take your assumptions about chimp research and apply them to parrots without confirming first)

> I think the worst outcome would be throwing out our collective wisdom because the AI labs tell us to.

We don't have to throw it out. But our current use of LLMs are a dramatic change from what came before. We should be questioning our assumptions and traditions that come from a different way of working and intelligence. Humans have a habit of trying to force things to be how they think they should be, rather than allowing them to grow organically, when the latter is often better for a system we don't yet understand.

[svachalek]

They write code differently but that doesn't mean that's the kind of code they prefer to read. Don't ascribe too much intention to a stochastic process.

Their coding style is above all else a symptom of their very limited context window and complete amnesia for anything that's not in the window.

[0xbadcafebee]

I don't think there's intention. And yes, its output is defined by its limits. But it's not just the context, is it? Their coding style is, above all else, a result of an algorithm and input. The training data, the reinforcement, the model design, the tuning, the prompt, the context. Change any one of those things and the code changes. They are a system, like an ecosystem. Let water flow and it finds its own path. But try to dam it and it creates unintended consequences. I think what we're going to find is some of our rules apply more to a human world than an LLM world.