zlacker

>>scared+(OP)
There is a certain amount of irony that people try really hard to say that hallucinations are not a big problem anymore and then a company that would benefit from that narrative gets directly hurt by it.

Which of course they are going to try to brush it all away. Better than admitting that this problem very much still exists and isn’t going away anytime soon.

>>nerdjo+A84
It's a huge problem. I just can't get past it and I get burned by it every time I try one of these products. Cursor in particular was one of the worst; the very first time I allowed it to look at my codebase, it hallucinated a missing brace (my code parsed fine), "helpfully" inserted it, and then proceeded to break everything. How am I supposed to trust and work with such a tool? To me, it seems like the equivalent of lobbing a live hand grenade into your codebase.

Don't get me wrong, I use AI every day, but it's mostly as a localized code complete or to help me debug tricky issues. Meaning I've written and understand the code myself, and the AI is there to augment my abilities. AI works great if it's used as a deductive tool.

Where it runs into issues is when it's used inductively, to create things that aren't there. When it does this, I feel the hallucinations can be off the charts -- inventing APIs, function names, entire libraries, and even entire programming languages on occasion. The AI is more than happy to deliver any kind of information you want, no matter how wrong it is.

AI is not a tool, it's a tiny Kafkaesque bureaucracy inside of your codebase. Does it work today? Yes! Why does it work? Who can say! Will it work tomorrow? Fingers crossed!

>>Modern+ib4
You're not supposed to trust the tool, you're supposed to review and rework the code before submitting for external review.

I use AI for rather complex tasks. It's impressive. It can make a bunch of non-trivial changes to several files, and have the code compile without warnings. But I need to iterate a few times so that the code looks like what I want.

That being said, I also lose time pretty regularly. There's a learning curve, and the tool would be much more useful if it was faster. It takes a few minutes to make changes, and there may be several iterations.

>>yodsan+Si4
1) Once you get it to output something you like, do you check all the lines it changed? Is there a threshold after which you just... hope?

2) No matter what the learning curve, you're using a statistical tool that outputs in probabilities. If that's fine for your workflow/company, go for it. It's just not what a lot of developers are okay with.

Of course it's a spectrum with the AI deniers in one corner and the vibe coders in the other. I personally won't be relying 100% on a tool and letting my own critical thinking atrophy, which seems to be happening, considering recent studies posted here.

>>gtirlo+Rt4
I've been doing AI-assisted coding for several months now, and have found a good balance that works for me. I'm working in Typescript and React, neither of which I know particularly well (although I know ES6 very well). In most cases, AI is excellent at tasks which involve writing quasi-custom boilerplate (eg. tests which require a lot of mocking), and at answering questions of how I should do _X_ in TS/React. For the latter, those are undoubtedly questions I could eventually find the answers on Stack Overflow and deduce how to apply those answers to my specific context -- but it's orders of magnitude faster to get the AI to do that for me.

Where the AI fails is in doing anything which requires having a model of the world. I'm writing a simulator which involves agents moving through an environment. A small change in agent behaviour may take many steps of the simulator to produce consequential effects, and thinking through how that happens -- or the reverse: reasoning about the possible upstream causes of some emergent macroscopic behaviour -- requires a mental model of the simulation process, and AI absolutely does _not_ have that. It doesn't know that it doesn't have that, and will therefore hallucinate wildly as it grasps at an answer. Sometimes those hallucinations will even hit the mark. But on the whole, if a mental model is required to arrive at the answer, AI wastes more time than it saves.

>>nkoren+wn5
> AI is excellent at tasks which involve writing quasi-custom boilerplate (eg. tests which require a lot of mocking)

I wonder if anyone has compared how well the AI auto-generating approach works compared to meta programming approaches (like Lisp macros) meant to address the same kind of issues with repetitive code.