We use CoPilot at work and there is no babysitting required.
We are software developers / engineers and it’s saves countless hours writing boilerplate code, giving code blocks based on a comment, and sticking to our coding conventions.
Sure it isn’t 100% right, but the owner and lead engineer calculates it to be around 70% accurate and even if it misses the mark, we have a whole lot less key presses to make.
Using Copilot as a copilot, like generating boilerplate and then code reviewing it is still “babysitting” it. It’s still significantly less effort than just doing it yourself though
Until someone uses it for a little more than boilerplate, and the reviewer nods that bit through as it’s hard to review and not something a human/the person who “wrote” it would get wrong.
Unless all the ai generated code is explicitly marked as ai generated this approach will go wrong eventually.
Agreed, using LLMs for code requires you to be an experienced dev who can understand what it pukes out. And for those very specific and disciplined people it’s a net positive.
However, generally, I agree it’s more risk than it’s worth
I’m a developer and typing encompasses most of my day. The owner and lead engineer has many meeting and admin work, but still is writing code and scaffolding new projects around 30% of his time.
I’m a developer and typing encompasses most of my day as well, but increasingly less of it is actually producing code. Ever more of it is in the form of emails, typically in the process of being forced to argue with idiots about what is and isn’t feasible/in the spec/physically possible, or explaining the same things repeatedly to the types of people who should not be entrusted with a mouse.
Simply false in my experience.
We use CoPilot at work and there is no babysitting required.
We are software developers / engineers and it’s saves countless hours writing boilerplate code, giving code blocks based on a comment, and sticking to our coding conventions.
Sure it isn’t 100% right, but the owner and lead engineer calculates it to be around 70% accurate and even if it misses the mark, we have a whole lot less key presses to make.
Using Copilot as a copilot, like generating boilerplate and then code reviewing it is still “babysitting” it. It’s still significantly less effort than just doing it yourself though
Until someone uses it for a little more than boilerplate, and the reviewer nods that bit through as it’s hard to review and not something a human/the person who “wrote” it would get wrong.
Unless all the ai generated code is explicitly marked as ai generated this approach will go wrong eventually.
Undoubtedly. Hell, even when you do mark it as such, this will happen. Because bugs created by humans also get deployed.
Basically what you’re saying is that code review is not a guarantee against shipping bugs.
Agreed, using LLMs for code requires you to be an experienced dev who can understand what it pukes out. And for those very specific and disciplined people it’s a net positive.
However, generally, I agree it’s more risk than it’s worth
What if I told you that typing in software engineering encompasses less than 5% of your day?
I’m a developer and typing encompasses most of my day. The owner and lead engineer has many meeting and admin work, but still is writing code and scaffolding new projects around 30% of his time.
I’m a developer and typing encompasses most of my day as well, but increasingly less of it is actually producing code. Ever more of it is in the form of emails, typically in the process of being forced to argue with idiots about what is and isn’t feasible/in the spec/physically possible, or explaining the same things repeatedly to the types of people who should not be entrusted with a mouse.