Oh wow. No shit. Anyway!
this post was submitted on 30 Sep 2025
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I'd be inclined to try using it if it was smart enough to write my unit tests properly, but it's great at double inserting the same mock and have 0 working unit tests.
I might try using it to generate some javadoc though.. then when my org inevitably starts polling how much ai I use I won't be in the gutter lol
I've seen it generate working unit tests plenty. In the sense that they pass.
...they do not actually test the functionality. Of course that function returns what you're asserting - you overwrote its actual output and checked against that!
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(39 replies)
Im not super surprised, but AI has been really useful when it comes to learning or giving me a direction to look into something more directly.
Im not really an advocate for AI, but there are some really nice things AI can do. And i like to test the code quality of the models i have access to.
I always ask for a ftp server and dns server, to check what it can do and they work surprisingly well most of the time.
AI companies and investors are absolutely overhyping its capabilities, but if you haven't tried it before I'd strongly recommend doing so. For simple bash scripts and Python it almost always gets something workable first try, genuinely saving time.
AI LLMs are pretty terrible for nearly every other task I've tried. I suspect it's because the same amount of quality training data just doesn't exist for other fields.
I've found it's pretty good at refactoring existing code to use a different but well-supported and well documented library. It's absolutely terrible for a new and poorly documented library.
I recently tried using Copilot with Claude to implement something in a fairly young library, and did get the basics working, including a long repetitive string of "that doesn't work, I'm getting error msg [error]". Seven times of that, and suddenly it worked! I was quite amazed, though it failed me in many other ways with that library (imagining functions and options that don't exist). But then redoing the same thing in the older, better supported library, it got it right on the first try.
But maybe the biggest advantage of AI coding is that it allows me to code when my brain isn't fully engaged. Of course the risk there is that my brain might not fully engage because of the AI.
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I always need to laugh when I read "Agentic AI"
The good news is: AI is a lot less impressive than it seemed at first.
The bad news is: so are a lot of jobs.
I can't even get Copilot to write Vitest files for React without making a mountain of junk code that describes drivel.
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Sounds exactly like my experience with Vibe Coding.
I would say absolutely in the general sense nost people, and the salesmen, frame them in.
When I was invited to assist with the GDC development, I got a chance to partner with a few AI developers and see the development process firsthand, try my hand at it myself, and get my hands on a few low parameter models for my own personal use. It's really interesting just how capable some models are in their specific use-cases. However, even high param. models easily become useless at the drop of a hat.
I found the best case, one that's rarely done mind you, is integrate the model into a program that has the ability to call a known database. With a properly trained model to format output in both natural language and use a given database for context calls, and concrete information, the qualitative performance leaps ahead by bounds. Problem is, that requires so much customization it pretty much ends up being something a capable hobbyist would do, it's just not economically sound for a business to adopt.