441–450 of 762 posts
Of course they are. The interesting thing isn't how good LLMs are today, it's their astonishing rate of improvement. LLMs are a lot better than they were a year ago, and light years ahead of where they were two years ago. Where will they be in five years?
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Same here. When I'm teaching coding I've noticed that LLMs will confuse the heck out of students. They will accept what it suggests without realizing that it is suggesting nonsense.
This was what promptly led me to turning off Jetbrains AI assistant: the multiline completion was incredibly distracting to my chain of thought, particularly when it would suggest things that looked right but weren't. Stopping and parsing the suggestion to realize if it was right or wrong would completely kill my flow.
My main annoyance? If I'm in that same function, it still remembers the debugging / temporary hack I tried 3 months ago and haven't done since and will suggest it. And heck, even if I then move to a different part of the file or even a different file, it will still suggest that same hack at times, even though I used it exactly once and have not since.
Once you accept something, it needs some kind of temporal feedback mechanism to timeout even accepted solutions over time, so it doesn't keep repeating stuff you gave up on 3 months ago.
Our codebase is very different from 98% of the coding stuff you'll find online, so anything more than a couple of obvious suggestions are complete lunacy, even though they've trained it on our codebase.
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> the conversational interface, for some reason, seems to turn off the natural skepticism that people have n=1 but after having chatgpt "lie" to me more than once i am very skeptical of it and always double check it, whereas something like tv or yt videos i still find myself being click-baited or grifted (iow less skeptical) much more easily still... any large studies about this would be very interesting...
I get irrationally frustrated when ChatGPT hallucinates npm packages / libraries that simply do not exist. This happens… weekly for me.
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Same here. When I'm teaching coding I've noticed that LLMs will confuse the heck out of students. They will accept what it suggests without realizing that it is suggesting nonsense.
Tbf, there's a phase of learning to code where everything is pretty much an incantation you learn because someone told you "just trust me." You encounter "here's how to make the computer print text in Python" before you would ever discuss strings or defining and invoking functions, for instance. To get your start you kind of have to just accept some stuff uncritically. It's hard to remember what it was like to be in…
There really shouldn't be. You don't need to know all the turtles by name, but "trust me" doesn't cut it most of the time. You need a minimal understanding to progress smoothly. Knowledge debt is a b*tch.
This matches my experience. I actually think a fair amount of value from LLM assistants to me is having a reasonably intelligent rubber duck to talk to. Now the duck can occasionally disagree and sometimes even refine. https://en.m.wikipedia.org/wiki/Rubber_duck_debugging I think the big question everyone wants to skip right to and past this conversation is, will this continue to be true 2 years from now? I don’t kno…
This is my first comment so I'm not sure how to do this but I made a BYO-API key VSCode extension that uses the OpenAI realtime API so you can have interactive voice conversations with a rubber ducky. I've been meaning to create a Show HN post about it but your comment got me excited!
In the future I want to build features to help people communicate their bugs / what strategies they've tried to fix them. If I can pull it off it would be cool if the AI ducky had a cursor that it could point and navigate to stuff as well.
Please let me know if you find it useful https://akshaytrikha.github.io/deep-learning/2025/05/23/duck...
This matches my experience. I actually think a fair amount of value from LLM assistants to me is having a reasonably intelligent rubber duck to talk to. Now the duck can occasionally disagree and sometimes even refine. https://en.m.wikipedia.org/wiki/Rubber_duck_debugging I think the big question everyone wants to skip right to and past this conversation is, will this continue to be true 2 years from now? I don’t kno…
LLMs aren't my rubber duck, they're my wrong answer. You know that saying that the best way to get an answer online is to post a wrong answer? That's what LLMs do for me. I ask the LLM to do something simple but tedious, and then it does it spectacularly wrong, then I get pissed off enough that I have the rage-induced energy to do it myself.
I see these comments all the time and they don’t reflect my experience so I’m curious what your experience has been
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LLMs follow instructions. Garbage in = garbage out generally. When attention is managed and a problem is well defined and necessary materials are available to it, they can perform rather well. On the other hand, I find a lot of the loosely-goosey vibe coding approach to be useless and gives a lot of false impressions about how useful LLMs can be, both too positive and too negative.
So what you’re saying is you need to be very specific and detailed when writing your specifications for the LLM to spit out the code you want. Sounds like I can just skip the middle man and code it myself.
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LLMs aren't my rubber duck, they're my wrong answer. You know that saying that the best way to get an answer online is to post a wrong answer? That's what LLMs do for me. I ask the LLM to do something simple but tedious, and then it does it spectacularly wrong, then I get pissed off enough that I have the rage-induced energy to do it myself.
LLMs follow instructions. Garbage in = garbage out generally. When attention is managed and a problem is well defined and necessary materials are available to it, they can perform rather well. On the other hand, I find a lot of the loosely-goosey vibe coding approach to be useless and gives a lot of false impressions about how useful LLMs can be, both too positive and too negative.
People are expecting perfection from bad spec
Isn’t that what engineers are (rightfully) always complaining about to BD?
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This is the response to most new technologies; folks simply don't want to accept the future before the ramifications truly hit. If technology folk cannot see the INCREDIBLE LEAP FORWARD made by LLMs since ChatGPT came on the market, they're not seeing the forest through the trees because their heads are buried in the sand. LLMs for coding are not even close to imperfect, yet, but the saturation curves are not flatten…
Lawyers don't even use version control software a lot of the time. They burn hundreds of paralegal hours reconciling revisions, a task that could be made 100x faster and easier with Git. There's no guarantee a technology will take off, even if it's really, really good. Because we don't decide if that tech takes off - the lawyers do. And they might not care, or they might decide billing more hours is better, actually.
The guiding principle of biglaw.
Attorneys have the bar to protect them from technology they don’t want. They’ve done it many times before, and they’ll do it again. They are starting to entertain LLMs, but not in a way that would affect their billable hours.