Programming

Many companies are tying monitored AI usage to performance evaluations. So yeah, we’re going to be ”willing” under that pretext.

Finding one senior dev who is willing to debug a multi-thousand line application produced by genAI…they’re going to be reluctant, at best, because the code is slop.

MBAs and C-suites keep trying to manufacture consent for this tech so their stock portfolios outperform, and the madmen are willing to sacrifice your jobs to do it!

I'm a slow adopter of new technologies like ~~AI~~ LLMs. My reasoning is that if it turns out to actually be a good product, then it will eventually prove itself, and the early adopters can be the "beta testers" so to speak. But if it turns out to be a bad product, then I won't have wasted my time on something that isn't worthwhile.

Maybe a day comes when I start using these tools, but they clearly just aren't all that useful in their current form. In all honesty, I'm pretty sure that they will never be useful enough for me to consider them worth learning, but definitely not so today.

The number-one frustration, cited by 45% of respondents, is dealing with "AI solutions that are almost right, but not quite," which often makes debugging more time-consuming. In fact, 66% of developers say they are spending more time fixing "almost-right" AI-generated code.

Not surprising at all. When you write code, you're actually thinking about it. And that's valuable context when you're debugging. When you just blindly follow snippets you got from some random other place, you're not thinking about it and you don't have that context.

So it's easy to see how this could lead to a net productivity loss. Spend more time writing it yourself and less time debugging, or let something else write it for you quickly, but spend a lot of time debugging. And on top of it all, no consideration of edge cases and valuable design requirement context can also get lost too.

We have copilot as an automatic code reviews. It mostly catches my bad spelling, occasionally finds a real issue, and often is incorrect.

I would imagine SO has seen a majorly significant drop in site traffic in the past few years

At my job, I have found it useful generating mediocre frontends under extremely tight time constraints. Clients are happy with the outcome and I find it more easily customizable than WordPress.

Looking at the code though, it's not a good idea to use it to build anything complex. Best it can do is "Company X needs ANY website before their presentation tomorrow." or whatever.

In other words, it's OK at covering for poor to nonexistent planning.

I'd like to run a model locally and experiment with it though. Problem is it seems no one discloses how they trained their models, open source or not.

If anyone has any suggestions, I'm open. I see Tabby has a Neovim plugin, but, again, no idea what it's trained on.

35% of developers use 6-10 distinct tools to get their work done, highlighting the need for seamless integration.

No ? I like my tools being separate. The only "seamless integration" I want is that the tools work in the terminal.

I use LLMs mainly for "editing text". Like if I have to refactor 100 lines of code and it can't be easily done with a regexp replace I will use LLM to do it. When I have to actually modify some logic I find it easier and faster to just do it than to explain what needs to be done to a LLM and carefully check its response for subtle bugs.

Developers remain willing but reluctant

Management: "Maybe we're not pushing hard enough"

GitHub Copilot with Claude Sonnet 4.5 works okay in codebases that have established and obvious patterns. It can continue following those patterns with careful guidance, review, and a healthy dose of manual testing. I generally get a modest productivity boost for code that is pretty straight forward, though you either have to spend time ensuring quality up front or fixing the slop afterwards, and there are times when it just won’t get something right that you’re better off doing yourself. Don’t expect it to come up with any clever or innovative solutions to hard problems: it’s just going to make a big bowl of hacky slop soup for you instead. It also does a shit job writing clean, maintainable code using things like the DRY principle, high cohesion / loose coupling, properly naming things, etc.

With all the babysitting required, the productivity increase is modest, but existent.

It's still very meh. Like does anyone remember those visual studio wizards for doing something? AI tools are about that good, roughly. But the difference is, you still have to review everything with the AI tool because it'll still make mistakes.

I use AI maybe once or twice a month, but it's far from, replaced my snippets with Ctrl+H replacing keywords.

Use AI to ask it questions and learn. Do not use it to do your work.

I have caught myself genuinely thinking that management needs to unearth more budget, if they so desperately want us to use these AI tools, so that we can onboard another person to compensate for the productivity hit.

Unfortunately, they believe the opposite to be true, that we just need to use AI tools and then our productivity will be through the roof...