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So, it is loosely the same as copilot? I understand that approach is a tad different, but result of converting natural language descriptions into code-changes should be comparable.

And both are trained on large corpus of github sources

Is there a way to test it somehow? Public API maybe?



> converting natural language descriptions into code-changes

Do people actually use Copilot for that? I just let it work its magic uninstructed. I guess it sometimes uses comments and function/variable names for its suggestions but that's about it. 99% of the time it just looks at my code, the context and neighboring files to predict what I'm trying to do.


I've found writing a temporary comment can be particularly useful when working with Unicode. For example, something similar to

//insert a unicode dot between each character in the string, and convert the numbers to subscript

saved me a lot of copy-pasting.


I use both. Sometimes it feels easier to write five words of text than starting to write code.


I use it most of the time as smart auto-completion as well, but sometimes for boilerplate it helps to just write a comment what you want to achieve, basically like a ChatGPT prompt.


for my day job, no, not frequently. When I'm writing in an unfamiliar language like bash or something, I'll do a little # implement a function that does x, y and z




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