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I recently tried Copilot out of curiosity and this is my experience too: It helps me getting started, which for me is 99% of the challenge. I know how to solve

I recently tried Copilot out of curiosity and this is my experience too: It helps me getting started, which for me is 99% of the challenge. I know how to solve problems, even complex ones, but for some reason getting started is just so extremely hard, sometimes. Copilot lets me get started, even if it's wrong sometimes. There have been times where I have been surprised by how it took something I wrote for a server, and presented the correct client-side implementation. I've used it a few times to describe a problem and let it handle the solution. It's not very good, but I wonder if one should place more blame on PEBCAK and put more time into problem-description. I gave it a few more paragraphs to describe the problem, and eventually I could take it from there. It was still wrong, but enough to get me started. Immensely helpful that way. Another aspect that I'm wondering about is if it will be able to do more with better documented code. Anyone have experience with that? I've started to write more doxygen comments, and hoping to see if there's a slow shift to more accurate predictions.

theshrike79 3y ago
> It helps me getting started This is like writing in general. It's easy to edit crappy text you've written into something better. It's completely impossible to do it to a text you didn't write at all. LLM models are pretty good in doing the crappy first version. It might use abandoned packages or old APIs but the skeleton is there. It's not that hard to add some meat on the bones when the structure exists. Recently I had to parse a pretty crappy XML format (planned by committee) with Go. I just fed the XML to GPT4 and asked it to parse specific values from it. It got like 95% there. I just had to do a few fixes and polish it a bit. Saved me a lot of headache and poking around in documentation.

theshrike79 3y ago

> It helps me getting started This is like writing in general. It's easy to edit crappy text you've written into something better. It's completely impossible to do it to a text you didn't write at all. LLM models are pretty good in doing the crappy first version. It might use abandoned packages or old APIs but the skeleton is there. It's not that hard to add some meat on the bones when the structure exists. Recently I had to parse a pretty crappy XML format (planned by committee) with Go. I just fed the XML to GPT4 and asked it to parse specific values from it. It got like 95% there. I just had to do a few fixes and polish it a bit. Saved me a lot of headache and poking around in documentation.