alt Hacker NewsIt has definitely made me more productive. That said, that productivity isn't coming from using it to write business logic (I prefer to have an in-depth understanding of the logical parts of the codebases that I'm working on. I've also seen cases in my work codebases where code was obviously AI generated before and ends up with gaping security or compliance issues that no one seemed to see at the time).
The productivity comes from three main areas for me:
- Having the AI coding assistance write unit tests for my changes. This used to be by far my least favorite part of my job of writing software, mostly because instead of solving problems, it was the monotonous process of gathering mock data to generate specific pathways, trying to make sure I'm covering all the cases, and then debugging the tests. AI coding assistance allows me to just have to review the tests to make sure that they cover all the cases I can think of and that there aren't any overtly wrong assumptions
- Research. It has been extraordinarily helpful in giving me insight into how to design some larger systems when I have extremely specific requirements but don't necessarily have the complete experience to architect them myself - I know enough to understand if the system is going to correctly accomplish the requirements, but not to have necessarily come up with architecture as a whole
- Quick test scripts. It has been extremely useful for generating quick SQL data for testing things, along with quick one-off scripts to test things like external provider APIs
> Research. It has been extraordinarily helpful in giving me insight into how to design some larger systems when I have extremely specific requirements but don't necessarily have the complete experience to architect them myself - I know enough to understand if the system is going to correctly accomplish the requirements, but not to have necessarily come up with architecture as a whole.
I agree, this is where coding agents really shine for me. Even if they get the details wrong, they often pinpoint where things happen and how quite well.
They're also great for rapid debugging, or assisted bug fixing. Often, I will manually debug a problem, then tell the AI, "This exception occurs in place Y because thing X is happening, here's a stack trace, propose a fix", and then it will do the work of figuring out where to put the fix for me. I already usually know WHAT to do, it's just a matter of WHERE in context. Saves a lot of time.
Likewise, if I have something where I want thing X to do Y, and X already does Z, then I'll say, "Implement a Y that works like Z but for A B C", and it'll usually get it really close on the first try.