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I'm sorry, how many lines of code for that?
Ai can be powerful and destructive at the same time. (note: I didn't use Ai to write this).
Ai coding can help a lot in accelerating software development. In the right hands that is. Meaning the software engineer still reviews the code. Test it. And takes responsibility. In those cases there is nothing wrong with using Ai for software development.
The problem is that some programmers are using AI without even looking at the end results. Just approves everything, commits, push and release. That approach is wrong and especially inexperience engineers might fail into this trap. So in this case the code has most likely a lot of duplicated code, full with bugs and other issues. Some issues you encounter it for the first time, since it wasn't tested etc.
In the latter story, you feel the impact. And the downsides of Ai. And only see the negatives of Ai. You might say it's Ai slop even. Or vibe coded. Which is correct.
Tldr: Ai can be very powerful in the right hands. It still requires a lot of human time and effort to get it correct. And if the engineer is too lazy then you feel the consequences. If you got an experienced software engineer that takes the responsibility of the code. Reviews it thoroughly. Test all corner cases, etc. Then AI can be powerful and helpful.
Agreed. I have a sense that, eventually, development communities will figure out etiquette and policies to govern LLM usage. But how do you enforce that kind of policy? Right now, it's essentially a judgement call by the maintainers. It's hard to catch sneaky LLM usage.
On the other hand, I think there are objectively good ways to use LLMs for software:
Indeed also read the paper called Programming as Theory building. From 1985. Which is very relevant today again. Since people lose the connection with the code due to Ai.
Lot of hate for a project maintained by a volunteer and offered for free here. Nobody forces this free stuff on you.
"but reviewed and heavily tested over 2-3 weeks by me. I created comparison documents, went through all queries multiple times and reviewed the logic over and over again. I also did load tests and manual regression tests, which took lots of evenings."
This is the way.
Pretty much.
I've started using AI on a project last week and the first thing I do is write tests. Lots of tests.
With enough guardrails, you could actually get pretty decent quality output out of it and with enough regression tests, you can ensure that nothing's actually breaking.
Similarly, reviewing its changes and actually reading the code that's being generated to ensure correctness is necessary. However, I am finding ways to automate that and reduce the incident rate of problems to even lower than my co-workers.
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