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b12a3 No.1946

Every time I sit down with an AI coding assistant, I notice the same thing: it is very good at Spring. Annotations, profiles, @Autowired, the whole call-stack-driven dance of beans wiring into beans. AI has seen twenty years of this. It guesses well, even when it has to infer how a profile-specific bean is going to be selected at runtime. This is because it has seen ten thousand examples of exactly that pattern. Which raises an uncomfortable question for anyone working on a new architecture: if AI is this fluent in 2020-era patterns, are we as an industry going to stay locked into those patterns simply because that's what the model knows? Is AI a conservative force that quietly drags software architecture backward to its training data's center of mass, no matter how good a newer idea might be?

article: https://dzone.com/articles/does-ai-dictate-2000-architectures

b12a3 No.1947

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the training data is basically a graveyard of modern design patterns legacy boilerplate. if you try to prompt it with something highly decoupled or functional, it usually tries to force-fit an observable pattern or some dependency injection mess because thats what the weights favor



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