I used to think like you until I actually tried copilot. I don’t even write much boilerplate code (it’s python, and the framework is already setup). It is still an effective and useful tool. It saves mental bandwidth, and the code it writes is exactly what I was intending to write, so it turns me into a reviewer than a coder and thus makes it easier to get the job done (I’m still confirming the code does what I want).
This is just gpt-3. With chatGPT i finally managed to get unit test backbones for the most annoying methods in our code base that stuck our coverage at 92%. Once we get full copilot gpt-4 integration we will likely get to 98% coverage in a days time. That’s not nothing.
I agree it's a game changer. I also think once I get access to copilot x I'll find it irresistible.
Something about the iterative context aware approach of chat helps me think through the problem. The only think I don't like about copilot is how you cant tell it to augment the output. Sometimes it's close but not perfect.
I think a lot of people turn off because they test it out on toy projects. IME it becomes a lot more valuable once there's enough context for it to start building off of, rather than just helping you lay early boilerplate.
It depends on the code. If the code is difficult to write, because you need to know a lot of details of other parts, Copilot will struggle.
But you can actually think of this as a code quality measure. You want to avoid this. You want to organize the project in a way that writing or modifying the code is easy. That means, make it straightforward how functions are called, how things are done. Minimize interdependencies, side effects.
This is just gpt-3. With chatGPT i finally managed to get unit test backbones for the most annoying methods in our code base that stuck our coverage at 92%. Once we get full copilot gpt-4 integration we will likely get to 98% coverage in a days time. That’s not nothing.