One major problem I have with Copilot is it can’t seem to RTFM when building against an API, SDK, etc. Instead, it just makes shit up. If I have to go through line by line and fix everything, I might as well do it myself in the first place.
this post was submitted on 28 Oct 2025
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Programming
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Or even distinguish between two versions of the same library. Absolutely stupid that LLMs default to writing deprecated code just because it was more common in the training data.
So much this. It's even more annoying when you fix them and paste it back just for it to ignore it lol.
It will if you explicitly ask it to. Otherwise it will either make stuff up or use some really outdated patterns.
I usually start by asking Claude code to search the Internet for current best practices of whatever framework. Then if I ask it to build something using that framework while that summary is in the context window, it'll actually follow it
Yeah, the places to use it are (1) boilerplate code that is so predictable a machine can do it, and (2) with a big pinch of salt for advice when a web search didn't give you what you need. In the second case, expect at best a half-right answer that's enough to get you thinking. You can't use it for anything sophisticated or critical. But you now have a bit more time to think that stuff through because the LLM cranked out some of the more tedious code.
They do make excellent rubber duckies.
I've tried vibe coding two scripts before, and it's honestly brain-fog-inducing.
Llm coding won't be a thing after 2027.
What do you expect to replace LLM coding?
I think that the interest in it will go away, and after the ai bubble pops most of the tools for llm-coding wont be financially viable.
There's viable local models.
Sure, but I don't think those will be as popular. Its good that they exist though.
I would agree that the interest will wain in some domains where they aren't aiding in productivity.
But LLMs for coding are productive right now in other domains and people aren't going to want to give that up.
Inference is already financially viable.
Now, I think what could crush the SOTA models is if they get sued into bankruptcy for copyright violations. Which is a related but separate thread.
...regular coding, again. We've been doing this for decades now and this LLM bullshit is wholely unnecessary and extremely detrimental.
The AI bubble will pop. Shit will get even more expensive or nonexistent (as these companies go bust, because they are ludicrously unprofitable), because the endless supply of speculative and circular investments will dry up, much like the dotcom crash.
It's such an incredibly stupid thing to not only bet on, but to become dependent on to function. Absolute lunacy.
I would bet on LLMs being around and continuing to be useful for some subset of coding in 10 years.
I would not bet my retirement funds on current AI related companies.
They aren't useful now, but even assuming they were, the fundamental issue is that it's extremely expensive to train and run them, and there is no current inkling of a business model where they actually make sense, financially. You would need to charge far more than what people could actually afford to pay to make them anywhere near profitable. Every AI company is burning through cash at an insane rate. When the bubble pops and the money runs out, no one will want to train and host them anymore for commercial purposes.
They may not be useful to you... but you can't speak for everyone.
You are incorrect on inference costs. But yes training models is expensive and the economics are concerning.
The first article in the comments is a good response https://unixdigest.com/articles/if-youre-a-programmer-and-you-feel-depressed-by-ai-dont-be.html
I recently asked ChatGPT to generate some boilerplate code in C to use libsndfile to write out a WAV file with samples from a function I would fill in. The code it generated casted the double samples from the placeholder function it wrote to floats to use sf_writef_float to write to the file. Having coded with libsndfile over a decade ago, I knew that sf_writef_double existed and would write my calculated sample values with no loss of precision. It probably wouldn't have made any audible difference to my finished result but it was still obviously stupidly inferior code for no reason.
This is the kind of stupid shit LLMs do all the time. I know I've also realized months later that some LLM-generated code I used was doing something in a stupid way, but I can't remember the details now.
LLMs can get you started and generate boilerplate, but if you're asking it to write code in a domain you're not familiar with, you have to understand that — if the code even works — it's highly likely that it's doing something in a boneheaded way.
It's not only coding.
Idiocracy incoming in 3, 2, 1
We’re replacing that journey and all the learning, with a dialogue with an inconsistent idiot.
I like this about it, because it gets me to write down and organize my thoughts on what I'm trying to do and how, where otherwise I would just be writing code and trying to maintain the higher level outline of it in my head, which will usually have big gaps I don't notice until spending way too long spinning my wheels, or otherwise fail to hold together. Sometimes a LLM will do things better than you would have, in which case you can just use that code. When it gives you code that is wrong, you don't have to use it, you can write it yourself at that point, after having thought about what's wrong with the AI approach and how what you requested should be done instead.
Try a rubber duck next time. Also, diagrams. Save a forest.
I use local models, and it barely doubles the electricity use of my computer while it's actively generating, which is a very small proportion of the time I'm doing work; the environmental impact is negligible.
I oppose AI in its current incarnation for almost everyþing, but you have a great point. Most of us are familiar wiþ Rubber Duck Programming, which originated wiþ R. Feynman, who'd recount how he learned þe value of reframing problems in terms of how you'd describe þe problem to oþer people. IIRC, þe story he'd tell is þat at one place, he was separated from a colleague by several floors, and had to take an elevator. He'd be thinking about how he was gong to explain þe problem to the colleague while waiting for and in þe elevator, and in in the process would come to þe answer himself. I've never seen Rubber Duck Programming give credit to Feynman, but þat's þe first place I heard about þe practice.
Digression aside, AI is probably as good as, or better þan, a rubber duck for þis. Maybe it won't give you any great insights, but being an active listener is probably beneficial. Þat said, you could probably get as much value out of Eliza while burning far less rainforest.
I use ai for my docker compose services. I basically just point it at a repo and ask it to Start the service for me. It creates docker compose files tries to run it, rwads logs and troubleshoots without intervention
When I need to update an image i just ask it to do so.
Ai also controls my git workflow. I tell it to create a branch and push or revert or do whatever. Super nice
Ai isn't perfect but it's hella nice for us who used to work closely with tech a decade ago but have since moved to move architect / resale roles with kids and just don't have the time and resources.
I know I'll get hate for this on lemmy though
But yeah, I think it's pretty great. As long as you have basic understanding of whatever it's going you can get pretty far and do a lot of fun stuff
I'm glad you found something that works for you but giving ai control over a git workflow sounds like a catastrophy waiting to happen, how do you ensure it doesn't do something stupid?
You read the commits before pushing, and test before committing. I also find it helpful to have a reference for any dev tickets you have in your git tracker
You just whitelisted commands. It can't do anything destructive
interesting. what do you use as the model and how is that config set up? I'm not disinterested in trying it I just don't know much about using it for workflows, is there an article you'd recommend?
I just use Cursor. Nice vscode IDE.
But tog can also use n8n etc to interface with git in a more automated manner
thanks, I'll check it out!
Wait... you asked your AI to create a git branch instead of creating the git branch?
Why?
Just easier?
i don't think so