That is the general reason, i use llms to help myself with everything including coding too, even though i know why it’s bad

If you’re not an experienced developer, it could be used as a crutch rather than actually learning how to write the code.

The real reason? People are just fed up with AI in general (that has no real-world use to most people) being crammed down their throats and having their personal code (and other data) being used to train models for megacorps.

Two reasons:

1. my company doesn’t allow it - my boss is worried about our IP getting leaked
2. I find them more work than they’re worth - I’m a senior dev, and it would take longer for me to write the prompt than just write the code

I just dont know anything about bash’s syntax

That probably won’t be the last time you write Bash, so do you really want to go through AI every time you need to write a Bash script? Bash syntax is pretty simple, especially if you understand the basic concept that everything is a command (i.e. syntax is <command> [arguments...]; like if <condition> where <condition> can be [ <special syntax> ] or [[ <test syntax> ]]), which explains some of the weird corners of the syntax.

AI sucks for anything that needs to be maintained. If it’s a one-off, sure, use AI. But if you’re writing a script others on your team will use, it’s worth taking the time to actually understand what it’s doing (instead of just briefly reading through the output). You never know if it’ll fail on another machine if it has a different set of dependencies or something.

What negative experience do you have with AI chatbots that made you hate them?

I just find dealing with them to take more time than just doing the work myself. I’ve done a lot of Bash in my career (>10 years), so I can generally get 90% of the way there by just brain-dumping what I want to do and maybe looking up 1-2 commands. As such, I think it’s worth it for any dev to take the time to learn their tools properly so the next time will be that much faster. If you rely on AI too much, it’ll become a crutch and you’ll be functionally useless w/o it.

I did an interview with a candidate who asked if they could use AI, and we allowed it. They ended up making (and missing) the same mistake twice in the same interview because they didn’t seem to actually understand what the AI output. I’ve messed around with code chatbots, and my experience is that I generally have to spend quite a bit of time to get what I want, and then I still need to modify and debug it. Why would I do that when I can spend the same amount of time and just write the code myself? I’d understand the code better if I did it myself, which would make debugging way easier.

Anyway, I just don’t find it actually helpful. It can feel helpful because it gets you from 0 to a bunch of code really quickly, but that code will probably need quite a bit of modification anyway. I’d rather just DIY and not faff about with AI.

I have worked with somewhat large codebases before using LLMs. You can ask the LLM to point a specific problem and give it the context. I honestly don’t see myself as capable without a LLM. And it is a good teacher. I learn much from using LLMs. No free advertisement for any of the suppliers here, but they are just useful.

You get access to information you can’t find on any place of the Web. There is a large structural bad reaction to it, but it is useful.

(Edit) Also, I would like to add that people who said that questions won’t be asked anymore seemingly never tried getting answers online in a discussion forum - people are viciously ill-tempered when answering.

With a LLM, you can just bother it endlessly and learn more about the world while you do it.

Because despite how easy it is to dupe people into thinking your methods are altruistic- AI exists to save money by eradicating jobs.

AI is the enemy. No matter how you frame it.

People are not “mad” or “hate” about AI, more like “concerned.”

I have a coworker who is essentially building a custom program in Sheets using AppScript, and has been using CGPT/Gemini the whole way.

While this person has a basic grasp of the fundamentals, there’s a lot of missing information that gets filled in by the bots. Ultimately after enough fiddling, it will spit out usable code that works how it’s supposed to, but honestly it ends up taking significantly longer to guide the bot into making just the right solution for a given problem. Not to mention the code is just a mess - even though it works there’s no real consistency since it’s built across prompts.

I’m confident that in this case and likely in plenty of other cases like it, the amount of time it takes to learn how to ask the bot the right questions in totality would be better spent just reading the documentation for whatever language is being used. At that point it might be worth it to spit out simple code that can be easily debugged.

Ultimately, it just feels like you’re offloading complexity from one layer to the next, and in so doing quickly acquiring tech debt.

We built a Durable task workflow engine to manage infrastructure and we asked a new hire to add a small feature to it.

I checked on them later and they expressed they were stuck on an aspect of the change.

I could tell the code was ChatGPT. I asked “you wrote this with ChatGPT didn’t you?” And they asked how I could tell.

I explained that ChatGPT doesn’t have the full context and will send you on tangents like it has here.

I gave them the docs to the engine and to the integration point and said "try using only these and ask me questions if you’re stuck for more than 40min.

They went on to become a very strong contributor and no longer uses ChatGPT or copilot.

I’ve tried it myself and it gives me the wrong answers 90% of the time. It could be useful though. If they changed ChatGPT to find and link you docs it finds relevant I would love it but it never does even when asked.

Personally, I’ve found AI is wrong about 80% of the time for questions I ask it.

It’s essentially just a search engine with cleverbot. If the problem you’re dealing with is esoteric and therefore not easily searchable, AI won’t fare any better.

I think AI would be a lot more useful if it gave a percentage indicating how confident it is in its answers, too. It’s very useless to have it constantly give wrong information as though it is correct.

I use ai, but whenever I do I have to modify it, whether it’s because it gives me errors, is slow, doesn’t fit my current implementation or is going off the wrong foot.

Its not just AI code but AI stuff in general.

It boils down to lemmy having a disproportionate amount of leftist liberal arts college student types. Thats just the reality of this platform.

Those types tend to see AI as a threat to their creative independent business. As well as feeling slighted that their data may have been used to train a model.

Its understandable why lots of people denounce AI out of fear, spite, or ignorance. Its hard to remain fair and open to new technology when its threatening your livelihood and its early foundations may have scraped your data non-consentually for training.

So you’ll see AI hate circle jerk post every couple days from angry people who want to poison models and cheer for the idea that its just trendy nonesense. Dont debate them. Dont argue. Just let them vent and move on with your day.

• issues with model training sources
• business sending their whole codebase to third party (copilot etc.) instead of local models
• time gain is not that substantial in most case, as the actual “writing code” part is not the part that takes most time, thinking and checking it is
• “chatting” in natural language to describe something that have a precise spec is less efficient than just writing code for most tasks as long as you’re half-competent. We’ve known that since customer/developer meetings have existed.
• the dev have to actually be competent enough to review the changes/output. In a way, “peer reviewing” becomes mandatory; it’s long, can be fastidious, and generated code really needs to be double checked at every corner (talking from experience here; even a generated one-liner can have issues)
• some business thinking that LLM outputs are “good enough”, firing/moving away people that can actually do said review, leading to more issues down the line
• actual debugging of non-trivial problems ends up sending me in a lot of directions, getting a useful output is unreliable at best
• making new things will sometimes confuse LLM, making them a time loss at best, and producing even worst code sometimes
• using code chatbot to help with common, menial tasks is irrelevant, as these tasks have already been done and sort of “optimized out” in library and reusable code. At best you could pull some of this in your own codebase, making it worst to maintain in the long term

Those are the downside I can think of on the top of my head, for having used AI coding assistance (mostly local solutions for privacy reasons). There are upsides too:

• sometimes, it does produce useful output in which I only have to edit a few parts to make it works
• local autocomplete is sometimes almost as useful as the regular contextual autocomplete
• the chatbot turning short code into longer “natural language” explanations can sometimes act as a rubber duck in aiding for debugging

Note the “sometimes”. I don’t have actual numbers because tracking that would be like, hell, but the times it does something actually impressive are rare enough that I still bother my coworker with it when it happens. For most of the downside, it’s not even a matter of the tool becoming better, it’s the usefulness to begin with that’s uncertain. It does, however, come at a large cost (money, privacy in some cases, time, and apparently ecological too) that is not at all outweighed by the rare “gains”.

https://www.techrepublic.com/article/ai-generated-code-outages/

If you’re a seasoned developer who’s using it to boilerplate / template something and you’re confident you can go in after it and fix anything wrong with it, it’s fine.

The problem is it’s used often by beginners or people who aren’t experienced in whatever language they’re writing, to the point that they won’t even understand what’s wrong with it.

If you’re trying to learn to code or code in a new language, would you try to learn from somebody who has only half a clue what he’s doing and will confidently tell you things that are objectively wrong? Thats much worse than just learning to do it properly yourself.

Lots of good comments here. I think there’s many reasons, but AI in general is being quite hated on. It’s sad to me - pre-GPT I literally researched how AI can be used to help people be more creative and support human workflows, but our pipelines around the AI are lacking right now. As for the hate, here’s a few perspectives:

• Training data is questionable/debatable ethics,
• Amateur programmers don’t build up the same “code muscle memory”,
• It’s being treated as a sole author (generate all of this code for me) instead of like a ping-pong pair programmer,
• The time saved writing code isn’t being used to review and test the code more carefully than it was before,
• The AI is being used for problem solving, where it’s not ideal, as opposed to code-from-spec where it’s much better,
• Non-Local AI is scraping your (often confidential) data,
• Environmental impact of the use of massive remote LLMs,
• Can be used (according to execs, anyways) to replace entry level developers,
• Devs can have too much faith in the output because they have weak code review skills compared to their code writing skills,
• New programmers can bypass their learning and get an unrealistic perspective of their understanding; this one is most egregious to me as a CS professor, where students and new programmers often think the final answer is what’s important and don’t see the skills they strengthen along the way to the answer.

I like coding with local LLMs and asking occasional questions to larger ones, but the code on larger code bases (with these small, local models) is often pretty non-sensical, but improves with the right approach. Provide it documented functions, examples of a strong and consistent code style, write your test cases in advance so you can verify the outputs, use it as an extension of IDE capabilities (like generating repetitive lines) rather than replacing your problem solving.

I think there is a lot of reasons to hate on it, but I think it’s because the reasons to use it effectively are still being figured out.

Some of my academic colleagues still hate IDEs because tab completion, fast compilers, in-line documentation, and automated code linting (to them) means you don’t really need to know anything or follow any good practices, your editor will do it all for you, so you should just use vim or notepad. It’ll take time to adopt and adapt.