I find if I ask it about procedures that have any vague steps AI will stumble on it and sometimes put me into loops where it tells me to do A, A fails, so do B, B fails, so it tells me to do A…
The study is garbage. No wonder it is a big hit with the tech illiterate fediverse community. AI is far better than humans.
SOURCE: I have used LLMs to help me write code for three years. I had a traumatic brain injury so I can’t work.
I’d never ask a friggin machine to do coding for me, that’s MY blast.
That said, I’ve had good luck asking GPT specific questions about multiple obscure features of Javascript, and of various browsers. It’ll often feed me a sample script using a feature it explains … a lot more helpful than many of the wordy websites like MDN … saving me shit-tons of time that I’d spend bouncing around a half-dozen ‘help’ pages.
No one would ever ask you to write code for them. I’ve seen your github. You code like a fucking high schooler
Humans are bad at code. AI is trained on humans. AI is bad because we are bad.
Ai is literally just copy pasting. Like if you think about AI as a control C control V machine, it makes sense. You wouldn’t trust a single fucking junior Dev that didn’t actually know how to code because they just Ctrl C control V from stack overflow for literally every single line of code. That’s all fucking AI is
all you do is copy and paste other people’s opinions into your empty skull
Shocker.
Anyone blindly having AI write their code is an absolute moron.
Anyone with decent experience (5-10 years, maybe 10+?) can absolutely fucking skyrocket their output if they properly set up their environments and treat their agents as junior devs instead of competent programmers. You shouldn’t trust generated code any more than you trust someone fresh out of college, but they produce code in seconds instead of weeks.
I have tripled my output while producing more secure code (based on my security audits), safer code (based on code coverage and security audits), and less error-prone code (based on production logs and our unchanged QA process).
Now, the ethical issues and environmental issues, I 100% can get behind. And I have no idea what companies are going to do in 10 years when they have to replace people like me and haven’t been hiring or training replacements. But the productivity and quality debates are absolutely ridiculous, as long as a strong dev is behind the wheel and has been trained to use the tools.
Consider: the facts
People are very bad at judging their own productivity, and AI consistently makes devs feel like they are working faster, while in fact slowing them down.
I’ve experienced it myself - it feels fucking great to prompt a skeleton and have something brand new up and running in under an hour. The good chemicals come flooding in because I’m doing something new and interesting.
Then I need to take a scalpel to a hundred scattered lines to get CI to pass. Then I need to write tests that actually test functionality. Then I start extending things and realize the implementation is too rigid and I need to change the architecture.
It is as this point that I admit to myself that going in intentionally with a plan and building it myself the slow way would have saved all that pain and probably got the final product shipped sooner, even if the prototype was shipped later.
It depends on the task. As an extreme example, I can get AI to create a complete application in a language I don’t know. There’s no way that’s not more productive than me first learning the language to a point where I can make apps in it. Just have to pick something simple enough for the AI.
Of course the opposite extreme also exists. I’ve found that when I demand something impossible, AI will often just try to implement it anyway. It can easily get into an endless cycle where it keeps optimistically declaring that it identified the issue and fixed it with a small change, over and over again. This includes cases where there’s a bug in the underlying OS or similar. You can waste a huge amount of time going down an entirely wrong path if you don’t realize that an idea doesn’t work.
In my real work neither of these really happen. So the actual impact is much less. A lot of my work is not coding in the first place. And I’ve been writing code since I was a little kid, for almost 40 years now. So even the fast scaffolding I can do with AI is not that exciting. I can do that pretty quickly without AI too. When AI coding tools appeared my bosses started asking if I was fast because I was using one. No, I’m fast because some people ask for a new demo every week. Causes the same problems later too.
But I also do think that we all still need to learn how to use AI properly. This applies to all tools, but I think it’s more difficult than with other tools. If I try to use a hammer on something other than a nail, it will not enthusiastically tell me it can do it with just one more small change. AI tools absolutely will though, and it’s easy to just let them try because it’s just a few seconds to see what they come up with. But that’s a trap that leads to those productivity wasting spirals. Especially if the result actually somehow still works at first, so we have to fix it half a year later instead of right away.
At my work there are some other things that I feel limit the productivity potential of AI tools. First of all we’re only allowed to use a very limited number of tools, some of them made in-house. Then we’re not really allowed to integrate them into our workflows other than the part where we write code. E.g. I could trivially write an mcp server that interacts with our (custom in-house) ci system and actually increases my productivity because I could save a small number of seconds very often if I could tell an AI to find builds for me for integration or QA work. But it’s not allowed. We’re all being pushed to use AI but the company makes it really difficult at the same time.
So when I play around with AI on my spare time I do actually feel like I’m getting a huge boost. Not just because I can use a claude model instead of the ones I can use at work, but also just basic things like e.g. being able to turn on AI in Xcode at all when working on software for Apple platforms. On my work Macbook I can’t turn on any Apple AI features at all so even tab completion is worse. Or in other words, those realities of working on serious projects at a serious company with serious security policies can also kill any potential productivity boost from AI. They basically expect us to be productive with only those features the non-developer CEO likes, who also doesn’t have to follow any of our development processes…
AI has made being OE insanely easy.
Water makes things wetter than fire does.
Similarly, the sky is made of air.
I’m not a programmer, but I’ve dabbled with Blender for 3D modeling, and it uses Node trees for a lot of different things, which is pretty much a programming GUI. I googled how to make a shader, and the AI gave me instructions. About half of it was complete nonsense, but I did make my shader.
No shit.
I actually believed somebody when they told me it was great at writing code, and asked it to write me the code for a very simple lua mod. It’s made several errors and ended up wasting my time because I had to rewrite it.
In a postgraduate class, everyone was praising ai, calling it nicknames and even their friend (yes, friend), and one day, the professor and a colleague were discussing some code when I approached, and they started their routine bullying on me for being dumb and not using ai. Then I looked at his code and asked to test his core algorithm that he converted from a fortran code and “enhanced” it. I ran it with some test data and compared to the original code and the result was different! They blindly trusted some ai code that deviated from their theoretical methodology, and are publishing papers with those results!
Even after showing the different result, they didn’t convince themselves of anything and still bully me for not using ai. Seriously, this shit became some sort of cult at this point. People are becoming irrational. If people in other universities are behaving the same and publishing like this, I’m seriously concerned for the future of science and humanity itself. Maybe we should archive everything published up to 2022, to leave as a base for the survivors from our downfall.
That’s not a bad idea. I’m already downloading lots of human knowledge and media that I want backed up because I can’t trust humanity anymore to have it available anymore
The way it was described to me by some academics is that it’s useful…but only as a “research assistant” to bounce ideas off of and bring in arcane or tertiary concepts you might not have considered (after you vet them thoroughly, of course).
The danger, as described by the same academics, is that it can act as a “buddy” who confirms you biases. It can generate truly plausible bullshit to support deeply flawed hypotheses, for example. Their main concern is it “learning” to stroke the egos of the people using it so it creates a feedback loop and it’s own bubbles of bullshit.
It can’t even copy and paste a Hello World example properly. If someone says it’s working well for them, I’m going to now assume they are too ignorant to understand what’s broken.
It works well when you use it for small (or repetitive) and explicit tasks. That you can easily check.
It works well for recalling something you already know, whether it be computer or human language. What’s a word for… what’s a command/function that does…
According to OpenAis internal test suite and system card, hallucination rate is about 50% and the newer the model the worse it gets.
And that fact remains unchanged on other LLM models.
For words, it’s pretty good. For code, it often invents a reasonable-sounding function or model name that doesn’t exist.
It’s not even good for words. AI just writes the same stories over and over and over and over and over and over. It’s the same problem as coding. It can’t think of anything novel. Hell it can’t even think. I’d argue the best and only real use for an llm is to help be a rough draft editor and correct punctuation and grammar. We’ve gone way way way too far with the scope of what it’s actually capable of
I use it for things that are simple and monotonous to write. This way I’m able to deliver results to tasks I couldn’t have been arsed to do. I’m a data analyst and mostly use mysql and power query
What’s your preferred Hello world language? I’m gunna test this out. The more complex the code you need, the more they suck, but I’ll be amazed if it doesn’t work first try to simply print hello world.
Malbolge is a fun one
Edit: Funny enough, ChatGPT fails to get this right, even with the answer right there on Wikipedia. When I tried running ChatGPT’s output the first few characters were correct but it errors with invalid char at 37
Cheeky, I love it.
Got correct code first try. Failed creating working docker first try. Second try worked.
tmp="$(mktemp)"; cat >"$tmp" <<'MBEOF' ('&%:9]!~}|z2Vxwv-,POqponl$Hjig%eB@@>}=<M:9wv6WsU2T|nm-,jcL(I&%$#" `CB]V?Tx<uVtT`Rpo3NlF.Jh++FdbCBA@?]!~|4XzyTT43Qsqq(Lnmkj"Fhg${z@> MBEOF docker run --rm -v "$tmp":/code/hello.mb:ro esolang/malbolge malbolge /code/hello.mb; rm "$tmp"Output: Hello World!
Why the fuck does this language exist lol
I’m actually slightly impressed it got both a working program, and a different one than Wikipedia. The Wikipedia one prints “Hello, world.”
I guess there must be another program floating around the web with “Hello World!”, since there’s no chance the LLM figured it out on its own (it kinda requires specialized algorithms to do anything)
I’d never even heard of that language, so it was fun to play with.
Definitely agree that the LLM didn’t actually figure anything out, but at least it’s not completely useless
It’s like having a lightning-fast junior developer at your disposal. If you’re vague, he’ll go on shitty side-quests. If you overspecify he’ll get overwhelmed. You need to break down tasks into manageable chunks. You’ll need to ask follow-up questions about every corner case.
A real junior developer will have improved a lot in a year. Your AI agent won’t have improved.
This is the real thing. You can absolutely get good code out of AI, but it requires a lot of hand holding. It helps me speed some tasks, especially boring ones, but I don’t see it ever replacing me. It makes far too many errors, and requires me to point them out, and to point in the direction of the solution.
They are great at churning out massive amounts of code. They’re also great at completely missing the point. And the massive amount of code needs to be checked and reviewed. Personally I’d rather write the code and have the AI review it. That’s a much more pleasant way to work, and that way it actually enhances quality.
They are improving, and probably faster then junior devs. The models we had had 2 years ago would struggle with a simple black jack app. I don’t think the ceiling has been hit.
My jr developer will eventually be familiar with the entire codebase and can make decisions with that in mind without me reminding them about details at every turn.
LLMs would need massive context windows and/or custom training to compete with that. I’m sure we’ll get there eventually, but for now it seems far off. I think this bubble will have to burst and let hardware catch up with our ambitions. It’ll take a couple of decades.
Just a few trillion more dollars, bro. We’re almost there. Bro, if you give up a few showers, the AI datacenter will be able to work perfectly.
Bro.
The cost of the improvement doesn’t change the fact that it’s happening. I guess we could all play pretend instead if it makes you feel better about it. Don’t worry bro, the models are getting dumber!
And I ask you - if those same trillions of dollars were instead spent on materially improving the lives of average people, how much more progress would we make as a society? This is an absolutely absurd sum of money were talking about here.
It’s beside the point. I’m simply saying that AI will improve in the next year. The cost to do so or all the others things that money could be spent on doesn’t matter when it’s clearly going to be spent on AI. I’m not in charge of monetary policies anywhere, I have no say in the matter. I’m just pushing back on the fantasies. I’m hoping the open source scene survives so we don’t end up in some ugly dystopia where all AI is controlled by a handful of companies.
Don’t worry bro, the models are getting dumber!
That would be pretty impressive when they already lack any intelligence at all.
They might. The amount of money they’re pumping into this is absolutely staggering. I don’t see how they’re going to make all of that money back, unless they manage to replace nearly all employees.
Either way it’s going to be a disaster: mass unemployment or the largest companies in the world collapsing.
Almost as if it was made to simulate human output but without the ability to scrutinize itself.
To be fair most humans don’t scrutinize themselves either.
(Fuck AI though. Planet burning trash)
The number of times I have received an un-proofread two sentence email is too damn high.
And then the follow up email because they didn’t actually finish a complete thought
I do this with texts/DMs, but I’d never do that with an email. I double or triple check everything, make sure my formatting is good, and that the email itself is complete. I’ll DM someone 4 or 5 times in 30 seconds though, it feels like a completely different medium ¯\_(ツ)_/¯
(Fuck AI though. Planet burning trash)
It’s humans burning the planet, not the spicy Linear Algebra.
Blaming AI for burning the planet is like blaming crack for robbing your house.
How about I blame the humans that use and promote AI. The humans that defend it in arguments using stupid analogies to soften the damage it causes?
Would that make more sense?
Blaming AI is in general criticising everything encompassing it, which includes how bad data centers are for the environment. It’s like also recognizing that the crack the crackhead smoked before robbing your house is also bad.
And even worse, it doesn’t realise it and can’t fix the errors.
But you see. That’s the solution. Now you pay foreigners to clean up the generated code by offshoring the engineers. At 1/100 the cost.
A computer is a machine that makes human errors at the speed of electricity.
I think one of the big issues is it often makes nonhuman errors. Sometimes I forget a semicolon or there’s a typo, but I’m well equipped to handle that. In fact, most programs can actually catch that kind of issue already. AI is more likely to generate code that’s hard to follow and therefore harder to check. It makes debugging more difficult.
Also seems like it’d be a lot harder to modify or extend later
AI is more likely to generate code that’s hard to follow and therefore harder to check.
Sure. It’s making the errors faster and at a far higher volume than any team of humans could do in twice the time. The technology behind inference is literally an iterative process of turning gibberish into something that resembles human text. So its sort of a speed run from baby babble into college level software design by trial, evaluation, and correction over and over and over again.
But because the baseline comparison code is, itself, full of errors, the estimation you get at the end of the process is going to be scattering errant semicolons (and far more esoteric coding errors) through the body of the program at a frequency equivalent to humans making similar errors over a much longer timeline.
I’ve been coding for a while. I did an honest eager attempt at making a real functioning thing with all code written by AI. A breakout clone using SDL2 with music.
The game should look good, play good, have cool effects, and be balanced. It should have an attractor screen, scoring, a win state and a lose state.
I also required the code to be maintainable. Meaning I should be able to look at every single line and understand it enough to defend its existence.
I did make it work. And honestly Claude did better than expected. The game ran well and was fun.
But: The process was shit.
I spent 2 days and several hundred dollars to babysit the AI, to get something I could have done in 1 day including learning SDL2.
Everything that turned out well, turned out well because I brought years of skill to the table, and could see when Claude was coding itself into a corner and tell it to break up code in modules, collate globals, remove duplication, pull out abstractions, etc. I had to detect all that and instruct on how to fix it. Until I did it was adding and re-adding bugs because it had made so much shittily structured code it was confusing itself.
TLDR; LLM can write maintainable code if given full constant attention by a skilled coder, at 40% of the coder’s speed.
It depends on the subject area and your workflow. I am not an AI fanboy by any stretch of the imagination, but I have found the chatbot interface to be a better substitute for the “search for how to do X with library/language Y” loop. Even though it’s wrong a lot, it gives me a better starting place faster than reading through years-old SO posts. Being able to talk to your search interface is great.
The agentic stuff is also really good when the subject is something that has been done a million times over. Most web UI areas are so well trodden that JS devs have already invented a thousand frameworks to do it. I’m not a UI dev, so being able to give the agent a prompt like, “make a configuration UI with a sidebar that uses the graphql API specified here” is quite nice.
AI is trash at anything it hasn’t been trained on in my experience though. Do anything niche or domain-specific, and it feels like flipping a coin with a bash script. It just throws shit at the wall and runs tests until the tests pass (or it sneakily changes the tests because the error stacktrace repeatedly indicates the same test line as the problem).
Yeah what you say makes sense to me. Having it make a “wrong start” in something new is useful, as it gives you a lot of the typical structure, introduces the terminology, maybe something sorta moving that you can see working before messing with it, etc.
It’s basically just for if you’re lazy and don’t want to write a bunch of boilerplate or hit your keyboard a bunch of times to move the cursor(s) around
It is great for boilerplate code. It can also explain code for you, or help with an unfamiliar library. It’s even helped me be productive when my brain wasn’t ready to really engage with the code.
But here’s the real danger: because I’ve got AI to do it for me, my brain doesn’t have to engage fully with the code anymore. I don’t really get into the flow where code just flows out of your hands like I used to. It’s becoming a barrier between me and the real magic of coding. And that sucks, because that’s what I love about this work. Instead, I’m becoming the AI’s manager. I never asked for that.
I’ve found the same thing. I’ve turned off the auto suggestions while tying because by the time I’m typing i already know what I’m going I’m to type and having mostly incorrect suggestions popping up every 2 seconds was distracting and counterproductive.
I generally agree with what you’ve said for sure. I think I’ve honestly started to use it for helping me to go pinpoint where to go look for issues in the spaghetti code of new code bases. I’ve also mostly tried to avoid using it in my personal coding time but I feel like it’s gotten harder and harder to get legitimately good search results nowadays which I realize is also because of ai. Given the choice I’d happily just erase it from existence I think. Spending hours sifting through reddit and stack overflow was way more fulfilling + I feel like people used to be slightly less prickly about answering stuff because that was how you had to get answers. It seems like lemmy could replace that space at least, I’ve genuinely gotten helpful comments and I’ve always felt downvotes on here have been productive versus what Reddit is now.
This was a very directed experiment at purely LLM written maintainable code.
Writing experiments and proof of concepts, even without skill, will give a different calculation and can make more sense.
Having it write a “starting point” and then take over, also is a different thing that can make more sense. This requires a coder with skill, you can’t skip that.
Which is funny because you should be able to just copy and paste And combine from maybe two maybe three GitHub pages pretty easily and you learn just as much
It would be really interesting to watch a video of this process. Though I’m certain it would be pretty difficult to pull off the editing.
You want to see someone using say, VS Code to write something using say, Claude Code?
There’s probably a thousand videos of that.
More interesting: I watched someone who was super cheap trying to use multiple AIs to code a project because he kept running out of free credits. Every now and again he’d switch accounts and use up those free credits.
That was an amazing dance, let me tell ya! Glorious!
I asked him which one he’d pay for if he had unlimited money and he said Claude Code. He has the $20/month plan but only uses it in special situations because he’ll run out of credits too fast. $20 really doesn’t get you much with Anthropic 🤷
That inspired me to try out all the code assist AIs and their respective plugins/CLI tools. He’s right: Claude Code was the best by a HUGE margin.
Gemini 3.0 is supposed to be nearly as good but I haven’t tried it yet so I dunno.
Now that I’ve said all that: I am severely disappointed in this article because it doesn’t say which AI models were used. In fact, the study authors don’t even know what AI models were used. So it’s 430 pull requests of random origin, made at some point in 2025.
For all we know, half of those could’ve been made with the Copilot gpt5-mini that everyone gets for free when they install the Copilot extension in VS Code.
It’s more I want to see the process of experienced coders explaining the coding mistakes that typical AI coding makes. I have very little experience and see it as a good learning experience. You’re probably right about there being tons of videos like that.
The mistakes it makes depends on the model and the language. GPT5 models can make horrific mistakes though where it randomly removes huge swaths of code for no reason. Every time it happens I’m like, “what the actual fuck?” Undoing the last change and trying usually fixes it though 🤷
They all make horrific security mistakes quite often. Though, that’s probably because they’re trained on human code that is *also" chock full of security mistakes (former security consultant, so I’m super biased on that front haha).
One of the first videos I watched about LLMs, was a journalist who didn’t know anything about programming used ChatGPT to build a javascript game in the browser. He’d just copy paste code and then paste the errors and ask for help debugging. It even had to walk him through setting of VS Code and a git repo.
He said it took him about 4 hours to get a playable platformer.
I think that’s an example of a unique capability of AI. It can let a non-programmer kinda program, it can let a non-Chinese speaker speak kinda Chinese, it’ll let a non-artist kinda produce art.
I don’t doubt that it’ll get better, but even now it’s very useful in some cases (nowhere near enough to justify the trillions of dollars being spent though).
Yeah, I’m not sure the way we allocate resources is justified either, in general. I guess ultimately the problem with AI is that it gives access to skills to capital that they would otherwise have to interact with laborers to get.
