I just recently got copilot in vscode through work. I typed a comment that said,“create a new model in sqlalchemy named assets with the columns, a, b, c, d”. It couldn’t know the proper data types to use, but it output everything perfectly, including using my custom defined annotations, only it was the same annotation for every column that I then had to update. As a test, that was great, but copilot also picked up a SQL query I had written in a comment to reference as I was making my models, and it also generated that entire model for me as well.
It didn’t do anything that I didn’t know how to do, but it saved on some typing effort. I use it mostly for its auto complete functionality and letting it suggest comments for me.
That’s awesome, and I would probably would find those tools useful.
Code generators have existed for a long time, but they are usually free. These tools actually costs a lot of money, cost way more to generate code this way than the traditional way.
So idk if it would be worth it once the venture capitalist money dries up.
Neither of those seem similar to GitHub copilot other than that they can reduce keystrokes for some common tasks. The actual applicability of them seems narrow. Frequently I use GitHub copilot for “implement this function based on this doc comment I wrote” or “write docs for this class/function”. It’s the natural language component that makes the LLM approach useful.
I think what you’re specifically referring to is accessibility or ease of use. For someone unfamiliar with those tools, I can see the appeal.
Personally, as a software dev, I think it’s just very inefficient way to accomplish this goal.LLMs consume vastly more resources than a simple script. So I wouldn’t use it, especially if I’m paying real money for it.
I just recently got copilot in vscode through work. I typed a comment that said, “create a new model in sqlalchemy named assets with the columns, a, b, c, d”. It couldn’t know the proper data types to use, but it output everything perfectly, including using my custom defined annotations, only it was the same annotation for every column that I then had to update. As a test, that was great, but copilot also picked up a SQL query I had written in a comment to reference as I was making my models, and it also generated that entire model for me as well.
It didn’t do anything that I didn’t know how to do, but it saved on some typing effort. I use it mostly for its auto complete functionality and letting it suggest comments for me.
That’s awesome, and I would probably would find those tools useful.
Code generators have existed for a long time, but they are usually free. These tools actually costs a lot of money, cost way more to generate code this way than the traditional way.
So idk if it would be worth it once the venture capitalist money dries up.
That’s fair. I don’t know if I will ever pay my own money for it, but if my company will, I’ll use it where it fits.
What are these code generators that have existed for a long time?
Lookup emmet.
I’ve also found IntelliJ’s generators useful for Java.
Neither of those seem similar to GitHub copilot other than that they can reduce keystrokes for some common tasks. The actual applicability of them seems narrow. Frequently I use GitHub copilot for “implement this function based on this doc comment I wrote” or “write docs for this class/function”. It’s the natural language component that makes the LLM approach useful.
There is also auto doc generators.
I think what you’re specifically referring to is accessibility or ease of use. For someone unfamiliar with those tools, I can see the appeal.
Personally, as a software dev, I think it’s just very inefficient way to accomplish this goal. LLMs consume vastly more resources than a simple script. So I wouldn’t use it, especially if I’m paying real money for it.