a viable solution to a complex problem

You mean how to structure a for loop in a bash script? Lmao

You are?

Yeah, I was lucky that I snuck into my company’s pilot program for it.

I’m impressed at how often it predicts what I’m about to do. The code almost always needs a slight bit of editing, but it almost always at least shaves a bit of time off of whatever task I was doing.

I no longer go straight to stackoverflow, I always ask the copilot first. Sometimes even just phrasing the question in natural language, something I wouldn’t do it trying to find it via search or stackoverflow, is kind of like rubber duck debugging, and I’ll come up with the answer while writing it out.

In my experience, Copilot does a fairly good job when you already know what you’re doing, but can’t be bothered to write the code yourself.

For example, basic stuff like read data from that file, use dplyr, remove these columns, do these calculations, plot the result using ggplot2, label the axes this way, use those colors etc. Copilot gives you the code that does roughly what you want, but you usually need to tweak it a bit it to suit your preferences. Copilot also makes absurd mistakes, but fixing them is fairly easy. If this is the sort of stuff you’re doing, copilot can indeed boost your productivity.

However, if you don’t know how to do something a bit more exotic like principal component analysis, and you ask copilot to do the job for you, expect plenty of trouble. You may end up on a wild goose chase, using the wrong tools, doing unnecessary calculations and all sorts of crazy nonsense. When you know what you’re doing, you can ask a very specific thing. When you don’t, you may end up being too ambiguous in your prompt, which will result copilot leading you down the wrong path.

You can do it this way too, but before implementing a single line of that garbage code, you absolutely have to ask copilot a bunch of questions just to make sure you really understand what you’re doing, what the new functions do, where do you really want to go etc. You’re probably going to have to tweak the code before running it, and that’s why you need to know what you’re doing. That’s the one big area you can’t outsource to copilot just yet.

But is it still faster than reading the documentation and building your own experimental tests? If you spend an hour and get a pile of broken garbage, then certainly not. If you spend a bit more, ask plenty of questions, make sure you know what you’re doing, then maybe it is worth it.

Run an open source one. Training requires lots of knowledge and even more hardware resources/time. Fine tuned models are available for free online, there is not much use in training it yourself.

Options are

https://github.com/oobabooga/text-generation-webui

https://github.com/Mozilla-Ocho/llamafile

https://github.com/ggerganov/llama.cpp

I recommend llavafiles, as this is the easiest option to run. The GitHub has all the stuff you need in the “quick start” section.

Though the default is a bit restricted on windows. Since the llavafiles are bundling the LLM weights with the executable and Windows has a 4GB limit on executables you’re restricted to very small models. Workarounds are available though!

Have a look at llama file models they’re pretty cool, just rename to xxx.exe and run on windows and chmod on Linux.

Though the currently supported ones are limited, you could try llama code.