I keep seeing headlines like this, and of course conveniently leaving out how Torvalds chewed people out for vibe coding.
Torvalds didn’t vibe code away the Linux bug. He actually knows what he’s doing.
There’s a difference between using a tool as a knowledgeable professional, and using a tool as a novice or a member of the general public: the professional can likely tell when the answer an AI gives is wrong.
AI is a tool that can be useful sometimes.
unpopular opinion, especially on the fediverse, but: AI is not inherently bad.
it is a tool, that can do everything to a pretty low standard but rather fast. so it is up to the learnt human to use the thing at the right time. i use the company ai at regularly at work, but treat it as an apprentice: every line of code has to be checked, because it will fail hilariously. but it does type faster than me, so simple and boring jobs can be done by it.
i don’t see the appeal of avoiding generated code at all cost. it is not different from human code in that it can fail horribly. the important thing is oversight. is there someone with the knowledge and keen eye to ensure no BS is merged. Linus, in my opinion, spent a lifetime building that reputation.
Using AI is not the same as vibe coding.
Asking AI to write a piece of code and then read it, understand and use, is a very reasonable use case.
AI is like a power tool. If you give a contractor a nail gun instead of a hammer he can build a house faster. If you give someone who has no idea how to build a house a nail gun they’ll just fail at building a house faster than if you only gave them a hammer and they’ll probably hurt themselves.
I personally don’t care about the tech. I care about the economy around the tech.
Finally got where I stand: why uncomfortable? It’s just being hard to impose a sensible approach to it:
- yes, llms can be used as a tool efficiently
- no, killing the planet along the way is not an option, this has to
stopbe stopped
What AI haters don’t get is that the people fanning the flames of AI(LLM’s) hate are the same people who want you to buy their subscriptions, because they know the only place the real damage occurs is in OpenSource communities.
AI use for Open Source projects is arguably the one use case that benefits everyone, plus it’s a sweet irony against corps and their locked-down proprietary software. Denouncer will loudly hawk that LLMs will make it trivial to circumvent the GPL, but they fail to mention that the pendulum swings both ways; with LLMs, it’s now easier than ever to reverse engineer proprietary systems and programs, recently there was a similar discourse on lemmy due to the mario kart wii decompilation.
They want loud people to sow division into OpenSource communities so that true opensource LLMs solutions are shunned and never reach maturity, corporate meanwhile is unaffected.
People are forgetting the AI hype train is reliant on the fact that businesses and individuals can’t host the AI themselves, which is rapidly changing.
All that insane power that OpenAI and Anthropic have harnessed is suddenly completely useless when you can just spin up a competing model at home, with HF hosting a crap ton of finetuned models like uncensored/abliterated to do whatever you want with it.
Even Deepseek released their full size models if you happen to have 400Gb (or 1.5Tb) of VRAM lying around.
When the bubble (hopefully) pops, AI will just continue to be a tool that devs use where applicable, and likely not on a massive data center cloud as it migrates into consumer electronics.
LLMs suck at writing code, but they are absolutely amazing at reading code and finding bugs. I am firmly against the usage of LLMs to write anything, but they have a place as a diagnostics tool when verified by a human.
I find that depends a LOT on what you’re asking the LLM to write, how well you’re specifying it, etc. As for the code reviews, if it’s code that matters: remember to open a new instance and ask the exact same question again on the code that has been reviewed and “fixed”. Back a year ago, that could get you into a waffle-loop where the engine would change its mind back and forth about what’s optimal and just oscillate between the two. These days they seem to record (and read) enough context to prevent that behavior, but I definitely get behavior of: “Are there any bugs?” “Yes, here are seven bugs.” “Fix those bugs.” “The bugs are fixed.” (and they ususally really are…) “Are there any more bugs?” “No, we have fixed ALL the bugs.” “Are you sure, look again.” “Yes, I am sure we have found and fixed ALL the bugs.” — new context window — “Are there any bugs?” “Yes, here are seven bugs.” different bugs.
I did that on a bigger project and literally repeated 20 times, finding 140 real bugs - granted, the later bugs were getting pretty trivial / far out edge cases, but they were still real, still fixed, still denied there were any more bugs until opening a fresh context and asking again. This was on Google’s Gemini 3.7 Flash High… Claude Opus 4.8+ seems quite a bit better about being able to continue in a context without becoming blind to issues “it has already solved.”
Line completion is pretty damn handy, it’s when you start asking for whole functions that things go downhill
Yeah I’ve been using LLM’s in Rider for over a decade. I don’t have a problem with LLM’s. I do have a problem with how they’re currently used, and how people keep trying to use them to replace their own thinking.
I think they have a place in the coding scene, a limited niche place, but a place none the less. They just aren’t a replacement for software engineers. Architecture and intention are the big differences to me. An LLM cannot understand intention, it just makes statistical guesses that are often wrong.
An LLM cannot understand intention, it just makes statistical guesses that are often wrong.
True, when you give a prompt like: “make me a contact management / constant contact app which I can deploy on AWS and scale to 100,000 users.” you get, mostly garbage. If you specify how you want the UX to flow, what fields are most important, what fields should be included in deeper interfaces, what the scheduling looks like, how it gets tuned, what the reports look like, etc. etc. etc. - in other words: give it real requirements and specifications.
Then, pay attention as it develops, you’ll ususally find that the requirements you gave it aren’t exactly what you really wanted, and when you see what it built that doesn’t match with your visions, you can have it revise the requirements and specs.
Sure, but for me its just faster to write it myself, in a way that needs to fit into the project. And while I’m aware you can give this context to an LLM, it still can’t read what your previous intentions were, nor what they are currently. So its hard for it to build on it.
To me they’re best as auto-complete, or research tools in the same vein as StackOverflow. Something to help speed up your existing workflow. Also good at translating both spoken languages, and functions into other coding languages. Everything else seems to get in the way for me.
Also good at translating both spoken languages
I know more than a few fluently multi-lingual people who would just roll their eyes…
Oh I bet. I’m not multi-lingual, unless you count body language :p. Learning English was hard enough as a kid. When I was talking about spoken languages I was refer to random comments online, and sometimes not fully translated games to help get the gist of what’s going on.
For professional work, I prefer to hire an expert.
For professional work we hire experts (who use AI as their first pass, but then “clean it up” to their “high professional standards using native speakers”) - and then we do another pass with “subject matter experts” who both speak the language and work directly in our field, becaue the “professional humans” typically don’t have those niche experiences. We recently made this procedure 100% required after the “human professionals” effectively translated On to mean Off for one of our controls…
There definitely are tasks where using the “standard tools” goes far faster than asking the LLM to do it for you, such as: copying signatures from images onto a .pdf contract - they’re pretty hopeless at editing out background noise, etc. but if you clean up the signature input images enough, they’ll take it home and make the ink solid and the background transparent and overlay them in the .pdf faster than you can open the four files in Photoshop or whatever your tool of choice is.
By the way, images of signatures on electronic documents have been an outrageous farce since 20 years now, LLMs just make it easier than ever to edit them into an existing .pdf
Thing is, there’s literally millions of common “computer tasks” and the LLMs themselves are just starting to “learn” which ones they’re good at and which they are not. It would be cool if Opus would self-identify “hey, I’m really good at this…” and “I’m pretty challenged with that, you’d be better off downloading this FOSS tool and doing it yourself, here are helpful instructions…”
IMO: Specifically, they’re bad at architecture and refactoring a small project into a large project. You have to jump through some hoops to make it craft something that needs more than 8m of context ram. If you can manage orchestration and multi-agents that don’t need to know each others context, you can start to pull off bigger stuff, but it’s not a forgone conclusion that it’ll be fine. The worst output comes from it getting stuck on something and trying less likely answers successively until it works. You really have to watch for it to struggle and at the very least stop and start to try some new randoms.
Also, anything other than Claude-code with some really well-done project definitions is a waste of time.
There are already several large projects that have hundreds of thousands of users and those projects were almost completely vibe coded. It’s almost the entire retro “recomp” scene now.
I have done several smaller projects with it, and they have been stable / performant for months - better than similar stuff I coded years earlier and spent 5-10x the effort on.
At my company we are currently moving to implement AI for exactly that. A cost effective review buddy.
Various developers have tried to use it for coding and I think apart for one time scripts or getting an initial structure generated, noone is convinced of AI.
Opus-class and higher models are actually great at writing code, but maybe it’s language dependent? I primarily work in TypeScript.
More than anything, it depends on the developer using it. In my experience, most are just slop shovelers that don’t read or review what Claude does. Then it falls on the PR reviewer (me) to be an asshole: Did you review your own code? Do you understand what this part over here does? Why did you choose this method versus alternatives?
Unless you tell Claude/Opus otherwise, it’s not going to take the initiative to refactor things, tidy up parts it didn’t touch, or hell, it won’t even spellcheck things. Can you really call it a language model if it outputs misspelled words? Its unit tests are great boilerplate but never comprehensive enough for me.
Claude is the developer that puts zero pride or effort in their work beyond the bare minimum to not get fired.
Prompting for refactoring, requirements, specifications, unit tests, integration tests, distribution / install scripts, all of those “good ideas” are things you need to nudge even Claude to do. Once you get it in the habit of doing them it does them automatically more often but still requires the occasional nudge.
Yes I agree, and honestly code review is now the key skill.
My company recently started exploring “AI interviewing”, to test candidates on “AI proficiency”, and the questions are all around prompt engineering (is that even a thing anymore?) and stuff. Like no, make sure they can read the fucking code!
I’m not terribly concerned if people write code and test it with AI, so long as they verify the results themselves.
I recently saw someone try to add a PR to something on github with thousands of lines of changes and then they got mad when people didn’t want to do the free labour verifying it. That is a bullshit use of AI
AI or far-east coders, we encourage them to keep their PRs as small as possible.
The lemmysphere will never agree
I used to be of that mind as well. But the newer models are just too good to deny it any longer.
Same. There’s an overwhelmingly negative opinion here.
Opus in my experience sucks for writing code.
What are you trying to have it do? I work in a very large and complex codebase, and it honestly scares me sometimes.
Refactoring and consolidating a big preexisting codebase. I try to introduce new concepts on a semantic level and it tries to give me interfaces and abstractions that would obstruct my work in the long run.
If we ever find a way to deal with the whole “burning the planet” issue, their best use case is to find and match patterns, not to imitate them.
The idea of using a language model to process search engine input isn’t the dumbest part about Gemini and plenty of people report good results finding information easier and quicker with ChatGPT. In those cases, the deviation from rigid keywords is desirable because it can match results with related words rather than literal word-matching. Google Search already did a decent job at that (before the enshittification ran rampant), which more complex language models could improve even further.
The landmine is in their reproduction of those results, where the generated “summary” is the equivalent of a cunning bullshitter that convincingly sounds like he understood the topic but actually has no clue and just delivers a best guess. That’s where the deviation becomes a risk of misinformation or introducing bugs.
They should narrow things down by finding the likely answers where that matters, not produce more stuff that humans will have to double-check.
LLMs suck at writing code,
So far.
I would trust Linus Torvalds to use AI to do this because the AI is literally trying to copy him when it writes that code. In that context, he is the training data.
People who are vehemently against any form of AI will be left behind. That may be difficult to hear, and even more difficult to accept, but that is the cold, hard, truth.
Its a tool, and just like any tool it has uses. It can be used inappropriately, and it’s valid to criticize those instances. But if you are plugging your ears and going “Lalala!” when people talk about actual valid use cases, or stubbornly pretending that it has none, you will be left behind.
You will be like office employees from the 80’s and 90’s who refused to use a computer and then suddenly found themselves without a job. It’s your choice if you use it or not. It’s also every company’s choice to not hire you because of your unwillingness to adapt.
If you haven’t learned it yet, this is your warning and your wake-up call: Learn how to use AI now, or find a different career. It’s not going away, no matter how much you may bitch and moan. There will soon be two classes of people, those who learned how to use this emerging technology, and those who refused. Things will be much harder for that second group.
I don’t say this to be snarky or smug. I hate AI myself, but I also recognize how fucking stupid I’d have to be to refuse to use it. It’s a bitter pill to swallow, but it’s also the truth.
The AI cabal is a very small group with a very large amount of power and powerful friends who want to push this on you. Remember, walking away from this does not impact hunger or shelter at all. You can survive without this shimmering facade.
Well it’s not that this shit CAN’T be useful, it’s that it’s being pushed at people heavily in ways that aren’t.
Fuck off with these fake AI bubble pumping stories. fake ass grifters reading the same script wherever you go (“Im a mathamatician and IM SCARED” lol)
It’s all horse shit.
Sorry guys but your AI powered sex doll is going in the chipper along with her unpublished Unified Field theory.
