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I can give you an example of an entire (well, I still consider it alpha) library I wrote several months ago, using Copilot: https://github.com/osuushi/triangulate
This is an implementation of a 1991 paper on polygon triangulation into Go. So the deepest thinking about how to solve the problem was obviously already done for me, but there were a number of edge cases that I had to invent my own solutions to, and the translation itself involved keeping a lot of context in my head.
I can’t tell you in precise detail what Copilot did, and what I wrote by hand. I wasn’t taking notes or recording my screen. But there’s a reason you don’t see a lot of blocks in there where I forgot to comment anything, because my entire process for this was “type what I want to do in English, and see if Copilot will generate the next snippet, or something close”. I didn’t do this out of bloodyminded dedication to the AI cause, but because it continued to be an extremely effective way to get the code written quickly.
I can give a few specifics:
- My linear algebra is rusty, and Copilot was extremely helpful here. I would often just type the basic thing I was trying to do in pretty vague linear algebra terms, and it would generate the formula.
That regurgitated code exists on Github exists under an MIT license: https://github.com/jethrodaniel/fast_inv_sqrt
"jethrodaniel" does not appear to have the copyright to offer that license, but it's hard for Github to determine that in general, so I doubt they would be liable for the error.
That's because with a generative internet all you really need is blockchain + prompting.
https://huggingface.co/spaces/mullikine/ilambda
Language models are able to 'steal' the linguistic meaning-making 'essence' of the software, by modelling:
- How the software is used (mimicing its function) - externing function
- How functions are 'inspired' - internal function
https://github.com/semiosis/imaginary-programming-thesis
The models themselves should be clear about where the data came from.
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