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The GP was just talking about not using common languages like TypeScript and frameworks like React, which is a bar of unusual that seems arguably far lower than mine, especially since I literally defined special custom DSLs with unusual semantics, and used libraries like SDL_gpu with little to no pre-existing training data. So this seems like shifting the goalposts.

Also, what would be highly unusual to you?

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> The GP was just talking about not using common languages like TypeScript and frameworks like React

i dont see anything like that in that comment.

"It's not an issue of usual vs unusual, I'm saying the models are way better at writing and maintaining django or react code bases than your own hand rolled architecture that you define in some docs that it has to learn and keep in context. All of the models do an amazing job making local edits and working in small greenfield projects but once you get to full production systems with close to a million lines things start to rot. The code still works and the models are able to make progress but all of a sudden you have 3-4 different versions of your concepts sprinkled in random corners of your code base.

My second point is that the models are way better at things like Rust or Lua than Python or JS, because the average person producing code in those languages has way more programming experience"

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