this post was submitted on 30 Sep 2025
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There's some real perks to using AI to code - it helps a ton with templatable or repetitive code, and setting up tedious tasks. I hate doing that stuff by hand so being able to pass it off to copilot is great. But we already had tools that gave us 90% of the functionality copilot adds there, so it's not super novel, and I've never had it handle anything properly complicated at all successfully (asking GPT-5 to do your dynamic SQL calls is inviting disaster, for example. Requires hours of reworking just to get close.)
More reliable ones.
Deterministic ones
Fair, I've used it recently to translate a translations.ts file to Spanish.
But for repetitive code, I feel like it is kind of a slow down sometimes. I should have refactored instead.
Some code is boilerplate and can’t be distilled down more. It’s nice to point an AI to a database schema and say “write the Django models, admin, forms, and api for this schema, using these authentication permissions”. Yeah I’ll have to verify it’s done right, but that gets a lot of the boring typing out of the way.
I use it for writing code to call APIs and is a huge boon.
Yeah, you have to check the results, but it’s way faster than me.
That's fair.
This is a thing people miss. "Oh it can generate repetitive code."
OK, now who's going to maintain those thousands of lines of repetitive unit tests, let alone check them for correctness? Certainly not the developer who was too lazy to write their own tests and to think about how to refactor or abstract things to avoid the repetition.
If someone's response to a repetitive task is copy-pasting poorly-written code over and over we call them a bad engineer. If they use an AI to do the copy-paste for them that's supposed to be better somehow?
Similarly I find it very useful for if I've written a tool script and really don't want to write the command line interface for it.
"Here's a well-documented function - write an argparser for it"
...then I fix its rubbish assumptions and mistakes. It's probably not drastically quicker but it doesn't require as much effort from me, meaning I can go harder on the actual function (rather than keeping some effort in reserve to get over the final hump).
I’ve had plenty of success using it to build things like docker compose yamls and the like, but for anything functional, it does often take a few tries to get it right. I never use its raw for anything in production. Only as a leaping off point to structure things.
For the missing 10% : the folder with copies of the code you have already wrote doing that.
Maybe it's the dynamic SQL calls themselves that are inviting disaster?
Dynamic SQL in of itself not an issue, but the consequences (exacerbated by SQL's inherent irrecoverability from mistakes - hope you have backups) have stigmatized its use heavily. With an understanding of good practice, a proper development environment and a close eye on the junior devs, there's no inherent issue to using it.
My feelings about C/C++ are the same. I'm still switching to Rust, because that's what the company wants.
So much of the AI hype has been pointing to ten year old technology repackaged in a slick new interface.
AI is the iPod to the Zune of yesteryear.
Repackaging old technology in slick new interfaces is what we have been calling progress in computer software for 40+ years.
I mean... I like to think we've done a bit more than that. FFS, file compression alone has made leaps and bounds since the 3.25" floppy days.
Also, as a T-SQL guy, I gotta say there's a world of difference between SQL 2008 and SQL 2022.
But I'll spot you that a lot of the last 10-15 years has produced herculean efforts in answering the question "How can we squeeze a few more ads into your GUI?"
There have been a few "milestone moments" like map-reduce Hadoop, etc. Still, there's a whole lot of eye candy wrapped around the same old basic concepts.