Ctrl+A + Del.
So clean.
I’ve heard that a Claude 4 model generating code for an infinite amount of time will eventually simulate a monkey typing out Shakespeare
It will have consumed the GigaWattHours capacity of a few suns and all the moisture in our solar system, but by Jeeves, we’ll get there!
…but it won’t be that impressive once we remember concepts like “monkey, typing, Shakespeare” were already embedded in the training data.
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Welp. Its actually very in line with the late stage capitalist system. All polish, no innovation.
Awwwww snap look at this limp dick future we got going on here.
Ai code is specifically annoying because it looks like it would work, but its just plausible bullshit.
Well I’ve got the name for my autobiography now.
This is where I got the term from: https://craphound.com/news/2023/09/17/plausible-sentence-generators/
“Specifically Annoying” or “Plausible Bullshit”? I’d buy the latter.
It needs good feedback. Agentic systems like Roo Code and Claude Code run compilers and tests until it works (just gotta make sure to tell it to leave the tests alone)
And that’s what happens when you spend a trillion dollars on an autocomplete: amazing at making things look like whatever it’s imitating, but with zero understanding of why the original looked that way.
I mean, there’s about a billion ways it’s been shown to have actual coherent originality at this point, and so it must have understanding of some kind. That’s how I know I and other humans have understanding, after all.
What it’s not is aligned to care about anything other than making plausible-looking text.
Coherent originality does not point to the machine’s understanding; the human is the one capable of finding a result coherent and weighting their program to produce more results in that vein.
Your brain does not function in the same way as an artificial neural network, nor are they even in the same neighborhood of capability. John Carmack estimates the brain to be four orders of magnitude more efficient in its thinking; Andrej Karpathy says six.
And none of these tech companies even pretend that they’ve invented a caring machine that they just haven’t inspired yet. Don’t ascribe further moral and intellectual capabilities to server racks than do the people who advertise them.
Coherent originality does not point to the machine’s understanding; the human is the one capable of finding a result coherent and weighting their program to produce more results in that vein.
You got the “originality” part there, right? I’m talking about tasks that never came close to being in the training data. Would you like me to link some of the research?
Your brain does not function in the same way as an artificial neural network, nor are they even in the same neighborhood of capability. John Carmack estimates the brain to be four orders of magnitude more efficient in its thinking; Andrej Karpathy says six.
Given that both biological and computer neural nets very by orders of magnitude in size, that means pretty little. It’s true that one is based on continuous floats and the other is dynamic peaks, but the end result is often remarkably similar in function and behavior.
If you would like to link some abstracts you find in a DuckDuckGo search that’s fine.
I actually was going to link the same one I always do, which I think I heard about through a blog or talk. If that’s not good enough, it’s easy to devise your own test and put it to an LLM. The way you phrased that makes it sound like you’re more interested in ignoring any empirical evidence, though.
That’s unreal. No, you cannot come up with your own scientific test to determine a language model’s capacity for understanding. You don’t even have access to the “thinking” side of the LLM.
It’s true that one is based on continuous floats and the other is dynamic peaks
Can you please explain what you’re trying to say here?
Both have neurons with synapses linking them to other neurons. In the artificial case, synapse activation can be any floating point number, and outgoing synapses are calculated from incoming synapses all at once (there’s no notion of time, it’s not dynamic). Biological neurons are binary, they either fire or do not fire, during a firing cycle they ramp up to a peak potential and then drop down in a predictable fashion. But, it’s dynamic; they can peak at any time and downstream neurons can begin to fire “early”.
They do seem to be equivalent in some way, although AFAIK it’s unclear how at this point, and the exact activation function of each brain neuron is a bit mysterious.
Ok, thanks for that clarification. I guess I’m a bit confused as to why a comparison is being drawn between neurons in a neural network and neurons in a biological brain though.
In a neural network, the neuron receives an input, performs a mathematical formula, and returns an output right?
Like you said we have no understanding of what exactly a neuron in the brain is actually doing when it’s fired, and that’s not considering the chemical component of the brain.
I understand why terminology was reused when experts were designing an architecture that was meant to replicate the architecture of the brain. Unfortunately, I feel like that reuse of terminology is making it harder for laypeople to understand what a neural network is and what it is not now that those networks are a part of the zeitgeist thanks to the explosion of LLM’s and stuff.
well, it only took 2 years to go from the cursed will smith eating spaghetti video to veo3 which can make completely lifelike videos with audio. so who knows what the future holds
Hot take, today’s AI videos are cursed. Bring back will smith spaghetti. Those were the good old days
cursed will smith eating spaghetti video
The cursed Will Smith eating spaghetti wasn’t the best video AI model available at the time, just what was available for consumers to run on their own hardware at the time. So while the rate of improvement in AI image/video generation is incredible, it’s not quite as incredible as that viral video would suggest
But wouldn’t you point still be true today that the best AI video models today would be the onces that are not available for consumers?
Probably is still true, but I’ve not been paying close attention to the AI market in the last couple of years. But the point I was trying to make was that it’s an apples to oranges comparison
There actually isn’t really any doubt that AI (especially AGI) will surpass humans on all thinking tasks unless we have a mass extinction event first. But current LLMs are nowhere close to actual human intelligence.
This has beeny experience as well. It keeps emphasizing “beauty” and keeps missing “correctness”
It generates an answer that looks correct. Actual correctness is accidental. That’s how you wind up with documents with references that don’t exist, it just knows what references look like.
It doesn’t ‘know’ anything. It is glorified text autocomplete.
The current AI is intelligent like how Hoverboards hover.
This is a philosophical discussion and I doubt you are educated or experienced enough to contribute anything worthwhile to it.
I asked ChatDVP for a response to your post and it said you weren’t funny.
I can tell you’re a member of the next generation.
Gonna ignore you now.
At first I thought that might be a Pepsi reference, but you are probably too young to know about that.
A 3 day old account being a dick on Lemmy?
I’m shocked.
Insulting, but also correct. What “knowing” something even means has a long philosophical history.
Trying to treat the discussion as a philisophical one is giving more nuance to ‘knowing’ than it deserves. An LLM can spit out a sentence that looks like it knows something, but it is just pattern matching frequency of word associations which is mimicry, not knowledge.
I’ll preface by saying I agree that AI doesn’t really “know” anything and is just a randomised Chinese Room. However…
Acting like the entire history of the philosophy of knowledge is just some attempt make “knowing” seem more nuanced is extremely arrogant. The question of what knowledge is is not just relevant to the discussion of AI, but is fundamental in understanding how our own minds work. When you form arguments about how AI doesn’t know things, you’re basing it purely on the human experience of knowing things. But that calls into question how you can be sure you even know anything at all. We can’t just take it for granted that our perceptions are a perfect example of knowledge, we have to interrogate that and see what it is that we can do that AIs can’t- or worse, discover that our assumptions about knowledge, and perhaps even of our own abilities, are flawed.
Acting like the entire history of the philosophy of knowledge is just some attempt make “knowing” seem more nuanced is extremely arrogant.
That is not what I said. In fact, it is the opposite of what I said.
I said that treating the discussion of LLMs as a philosophical one is giving ‘knowing’ in the discussion of LLMs more nuance than it deserves.
Dude… the point is I don’t have to be. I just have to be human and use it. If it sucks, I am gonna say that.
Llms are the smartest thing ever on subjects you have no fucking clue on. On subjects you have at least 1 year experience with it suddenly becomes the dumbest shit youve ever seen.
Semantics 😴
Sementics 💦
Not even remotely.
You could claim that it knows the pattern of how references are formatted, depending on what you mean by the word know. Therefore, 100% uninteresting discussion of semantics.
The theory of knowledge (epistemology) is a distinct and storied area of philosophy, not a debate about semantics.
There remains to this day strong philosophical debate on how we can be sure we really “know” anything at all, and thought experiments such as the Chinese Room illustrate that “knowing” is far, far more complex than we might believe.
For instance, is it simply following a set path like a river in a gorge? Is it ever actually “considering” anything, or just doing what it’s told?
No one cares about the definition of knowledge to this extent except for philosophers. The person who originally used the word “know” most definitely didn’t give a single shit about the philosophical perspective. Therefore, you shitting yourself a word not being used exactly as you’d like instead of understanding the usage in the context is very much semantics.
llms are systems that output human-readable natural language answers, not true answers
And a good part of the time, the answers can often have a… subtly loose relationship with truth
So its 50% better than my code?
If the code cannot uphold correctness, it is 0% better than your code.
cant wait to see “we use AI agents to generate well structured non-functioning code” with off centered everything and non working embeds on the website
Honest question: I haven’t used AI much. Are there any AIs or IDEs that can reliably rename a variable across all instances in a medium sized Python project? I don’t mean easy stuff that an editor can do (e.g. rename QQQ in all instances and get lucky that there are no conflicts). I mean be able to differentiate between local and/or library variables so it doesn’t change them, only the correct versions.
Find and Replace?
that will catch too many false positives
Itellij is actually pretty good at this. Besides that, cursor or windsurf should be able to. I was using cursor for a while and when I needed to reactor something, it was pretty good at picking that up. It kept crashing on me though, so I am now trying windsurf and some other options. I am missing the auto complete features in cursor though as I would use this all the time to fill out boilerplate stuff as I write.
The one key difference in cursor and windsurf when compared to other products is that it will look at the entire context again for any changes or at least a little bit of it. You make a change, it looks if it needs to make changes elsewhere.
I still don’t trust AI to do much though, but it’s an excellent helper
Okay, I realize I’m that person, but for those interested:
tree,catandsedget the job done nicely.And… it’s my nap time, now. Please keep the Internet working, while I’m napping. I have grown fond of parts of it. Goodnight.
most IDEs are pretty decent at it if you configure them correctly. I used intelliJ and it knows the difference. use the refactor feature and it’ll crawl references, not just rename all instances.
I’m going to laugh in Java, where this has always been possible and reliable. Not like ai reliable, but expert reliable. Because of static types.
For the most part “Rename symbol” in VSCode will work well. But it’s limited by scope.
Yeah, I’m looking for something that would understand the operation (? insert correct term here) of the language well enough to rename intelligently.
Not reliably, no. Python is too dynamic to do that kind of thing without solving general program equivalence which is undecidable.
Use a static language, problem solved.
I use pycharm for this and in general it does a great job. At work we’ve got some massive repos and it’ll handle it fine.
The “find” tab shows where it’ll make changes and you can click “don’t change anything in this directory”
Yes, all of JetBrains’ tools handle project-wide renames practically perfectly, even in weirder things like Angular projects where templates may reference variables.
Just be carerul when refactoring variable names in doc comments, I’ve seen some weird stuff happen there
IntelliJ IDEA, if it knows it is the same variable, it will rename it. Usually works in a non fucked up codebase that uses
evalor some obscure constructs like saving a variable name into a variable as a string and dynamically invoking it.
Code that does not work is just text.
No the spell just fizzled. In my experience it happens far less often if you start with an Abra kabara and end it with an Alakazam!
Zojak Quapaj!
Yeah, the Abra kabara init and Alakazam cleanup are an important part, specially until you have become good enough to configure your own init.
There is an alternative init, Abra Kadabra, which automatically adds a cleanup and some general fixes when it detects the end of the spell.
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Conversely, code that works is also text
But not just text
Also that’s not converse to what the parent comment said
But working code can be made into numbers.
But text is also numbers
But numbers are also text
Code that works is also just text.
It is text, but not just text
Text that’s not code might also work.
I’ve never thought of it that way. I’m going to add copy writer to my resume.
Maybe fiction writer as well
This made me laugh so hard one of the dogs came to check in on me.
Oh my goodness, that’s adorable and sweet of your dog! Also, I’m so glad you had such a big laugh. I love when that happens.
He’s a sweet guy. … Mostly. Very much in need of a lot of attention. Sometimes he just sits next to you on the couch and puts his paw on you if you’re not giving him enough attention.
Here he is posing with his sister as a prop:
Oh my goodness, he sounds precious! I’ve had a sweet and needy dog like that in the past, too. It can be a lot, but I loved it (and miss it,) haha.
Both your dogs are very cute! You and your pups gave me a much-needed smile. Thank you for that. :) Please give them some pets from me!
Big Beautiful Code
My uncle. Very smart very neuronal. He knows the entire Internet, can you imagine? the entire internet. Like the mails of Crooked Hillary Clinton, that crook. You know what stands in that Mails? my uncle knows. He makes the best code. The most beautiful code. No one has ever seen code like it, but for him, he’s a genius, like i am, i have inherited all his genius genes. It is very easy. He makes the best code. Sometimes he calls me and asks me: you are even smarter than i am. Can you look at my code?
Thanks, I hate it.
All people say it. Tremendous code. All the experts said “No, generating formatted random text is not working code” but we did it.
Its like having a junior developer with a world of confidence just change shit and spend hours breaking things and trying to fix them, while we pay big tech for the privilege of watching the chaos.
I asked chat gpt to give me a simple squid proxy config today that blocks everything except https. It confidently gave me one but of course it didnt work. It let through http and despite many attempts to get a working config that did that, it just failed.
So yeah in the end i have to learn squid syntax anyway, which i guess is fine, but I spent hours trying to get a working config because we pay for chat gpt to do exactly that…
Man, I can’t wait to try out generative AI to generate config files for mission critical stuff! Imagine paying all of us devops wankers when my idiot boss can just ask Chat GPT to sort all this legacy mess we’re juggling with on the daily!
It confidently gave me one
IMO, that’s one of the biggest “sins” of the current LLMs, they’re trained to generate words that make them sound confident.
Sycophants.
They aren’t explicitly trained to sound confident, that’s just how users tend to talk. You don’t often see “I don’t know but you can give this a shot” on Stack Overflow, for instance. Even the incorrect answers coming from users are presented confidently.
Funnily enough, lack of confidence in response is something I don’t think LLMs are currently capable of, since it would require contextual understanding of both the question, and the answer being given.
SO answers and questions are usually edited multiple times to sound professional, confident, and be correct.
No, I’m sure you’re wrong. There’s a certain cheerful confidence that you get from every LLM response. It’s this upbeat “can do attitude” brimming with confidence mixed with subservience that is definitely not the standard way people communicate on the Internet, let alone Stack Overflow. Sure, sometimes people answering questions are overconfident, but it’s often an arrogant kind of confidence, not a subservient kind of confidence you get from LLMs.
I don’t think an LLM can sound like it lacks in confidence for the right reasons, but it can definitely pull off lack of confidence if it’s prompted correctly. To actually lack confidence it would have to have an understanding of the situation. But, to imitate lack of confidence all it would need to do is draw on all the training data it has where the response to a question is one where someone lacks confidence.
Similarly, it’s not like it actually has confidence normally. It’s just been trained / meta-prompted to emit an answer in a style that mimics confidence.
ChatGPT went through a phase of overly bubbly upbeat responses, they chilled it out tho. Not sure if that’s what you saw.
One thing is for sure with all of them, they never say “I don’t know” because such responses aren’t likely to be found in any training data!
It’s probably part of some system level prompt guidance too, like you say, to be confident.
I think “I don’t know” might sometimes be found in the training data. But, I’m sure they optimize the meta-prompts so that it never shows up in a response to people. While it might be the “honest” answer a lot of the time, the makers of these LLMs seem to believe that people would prefer confident bullshit that’s wrong over “I don’t know”.
I have a friend who swears by llms, he sais it helps him a lot. I once watched him do it, and the experience was exactly the same you described. He wasted couple of hours fighting with bullshit generator just to do everything himself anyway. I asked him wouldn’t it be better to not waste the time, but he didn’t really saw the problem, he gaslit himself that fighting with the idiot machine helped.
Laugh it up while you can.
We’re in the “haha it can’t draw hands!” phase of coding.
AI bad. But also, video AI started with will Will Smith eating spaghetti just a couple years ago.
We keep talking about AI doing complex tasks right now and it’s limitations, then extrapolating its development linearly. It’s not linear and it’s not in one direction. It’s a exponential and rhizomatic process. Humans always over-estimate (ignoring hard limits) and under-estimate (thinking linearly) how these things go. With rocketships, with internet/social media, and now with AI.
someone drank the koolaid.
LLMs will never code for two reasons.
one, because they only regurgitate facsimiles of code. this is because the models are trained to ingest content and provide an interpretation of the collection of their content.
software development is more than that and requires strategic thought and conceptualization, both of which are decades away from AI at best.
two, because the prevalence of LLM generated code is destroying the training data used to build models. think of it like making a copy of a copy of a copy, et cetera.
the more popular it becomes the worse the training data becomes. the worse the training data becomes the weaker the model. the weaker the model, the less likely it will see any real use.
so yeah. we’re about 100 years from the whole “it can’t draw its hands” stage because it doesn’t even know what hands are.
This is just your ego talking. You can’t stand the idea that a computer could be better than you at something you devoted your life to. You’re not special. Coding is not special. It happened to artists, chess players, etc. It’ll happen to us too.
I’ll listen to experts who study the topic over an internet rando. AI model capabilities as yet show no signs of slowing their exponential growth.
you’re a fool. chess has rules and is boxed into those rules. of course it’s prime for AI.
art is subjective, I don’t see the appeal personally, but I’m more of a baroque or renaissance fan.
I doubt you will but if you believe in what you say then this will only prove you right and me wrong.
what is this?
once you classify it, why did you classify it that way? is it because you personally have one? did you have to rule out what it isn’t before you could identify what it could be? did you compare it to other instances of similar subjects?
now, try to classify it as someone who doesn’t have these. someone who has never seen one before. someone who hasn’t any idea what it could be used for. how would you identify what it is? how it’s used? are there more than one?
now, how does AI classify it? does it comprehend what it is, even though it lacks a physical body? can it understand what it’s used for? how it feels to have one?
my point is, AI is at least 100 years away from instinctively knowing what a hand is. I doubt you had to even think about it and your brain automatically identified it as a hand, the most basic and fundamentally important features of being a human.
if AI cannot even instinctively identify a hand as a hand, it’s not possible for it to write software, because writing is based on human cognition and is entirely driven on instinct.
like a master sculptor, we carve out the words from the ether to perform tasks that not only are required, but unseen requirements that lay beneath the surface that are only known through nuance. just like the sculptor that has to follow the veins within the marble.
the AI you know today cannot do that, and frankly the hardware of today can’t even support AI in achieving that goal, and it never will because of people like you promoting a half baked toy as a tool to replace nuanced human skills. only for this toy to poison pill the only training data available, that’s been created through nuanced human skills.
I’ll just add, I may be an internet rando to you but you and your source are just randos to me. I’m speaking from my personal experience in writing software for over 25 years along with cleaning up all this AI code bullshit for at least two years.
AI cannot code. AI writes regurgitated facsimiles of software based on it’s limited dataset. it’s impossible for it to make decisions based on human nuance and can only make calculated assumptions based on the available dataset.
I don’t know how much clearer I have to be at how limited AI is.
LMFAO. He’s right about your ego.
thank you for your input obvious troll account.
Coding isn’t special you are right, but it’s a thinking task and LLMs (including reasoning models) don’t know how to think. LLMs are knowledgeable because they remembered a lot of the data and patterns of the training data, but they didn’t learn to think from that. That’s why LLMs can’t replace humans.
That does certainly not mean that software can’t be smarter than humans. It will and it’s just a matter of time, but to get there we likely have AGI first.
To show you that LLMs can’t think, try to play ASCII tic tac toe (XXO) against all those models. They are completely dumb even though it “saw” the entire Wikipedia article on how xxo works during training, that it’s a solved game, different strategies and how to consistently draw - but still it can’t do it. It loses most games against my four year old niece and she doesn’t even play good/perfect xxo.
I wouldn’t trust anything, which is claimed to do thinking tasks, that can’t even beat my niece in xxo, with writing firmware for cars or airplanes.
LLMs are great if used like search engines or interactive versions of Wikipedia/Stack overflow. But they certainly can’t think. For now, but likely we’ll need different architectures for real thinking models than LLMs have.
To be fair, if I wrote 3000 new lines of code in one shot, it probably wouldn’t run either.
LLMs are good for simple bits of logic under around 200 lines of code, or things that are strictly boilerplate. People who are trying to force it to do things beyond that are just being silly.
You managed to get an ai to do 200 lines of code and it actually compiled?
4o has been able to do this for months.
Play ASCII tic tac toe against 4o a few times. A model that can’t even draw a tic tac toe game consistently shouldn’t write production code.
I tried, it can’t get trough four lines without messing up. Unless I give it tasks that are so stupendously simple that I’m faster typing them myself while watching tv
Four lines? Let’s have realistic discussions, you’re just intentionally arguing in bad faith or extremely bad at prompting AI.
You can prove your point easily: show us a prompt that gives us a decent amount of code that isn’t stupidly simple or sufficiently common that I don’t just copy paste the first google result
I have nothing to prove to you if you wish to keep doing everything by hand that’s fine.
But there are plenty of engineers l3 and beyond including myself using this to lighten their workload daily and acting like that isn’t the case is just arguing in bad faith or you don’t work in the industry.
I do use it, it’s handy for some sloppy css for example. Emphasis on sloppy. I was kinda hoping you actually had something there
Uh yeah, like all the time. Anyone who says otherwise really hasn’t tried recently. I know it’s a meme that AI can’t code (and still in many cases that’s true, eg. I don’t have the AI do anything with OpenCV or complex math) but it’s very routine these days for common use cases like web development.
I recently tried it for scripting simple things in python for a game. Yaknow, change char’s color if they are targetted. It output a shitton of word salad and code about my specific use case in the specific scripting jargon for the game.
It all based on “Misc.changeHue(player)”. A function that doesn’t exist and never has, because the game is unable to color other mobs / players like that for scripting.
Anything I tried with AI ends up the same way. Broken code in 10 lines of a script, halucinations and bullshit spewed as the absolute truth. Anything out of the ordinary is met with “yes this can totally be done, this is how” and “how” doesn’t work, and after sifting forums / asking devs you find out “sadly that’s impossible” or “we dont actually use cpython so libraries don’t work like that” etc.
Well yeah, it’s working from an incomplete knowledge of the code base. If you asked a human to do the same they would struggle.
LLMs work only if they can fit the whole context into their memory, and that means working only in highly limited environments.
No, a human would just find an API that is publically available. And the fact that it knew the static class “Misc” means it knows the api. It just halucinated and responded with bullcrap. The entire concept can be summarized with “I want to color a player’s model in GAME using python and SCRIPTING ENGINE”.
It’s possible the library you’re using doesn’t have enough training data attached to it.
I use AI with python for hundreds line data engineering tasks and it nails it frequently.
You must be a big fan of boilerplate
Not sure what you mean, boilerplate code is one of the things AI is good at.
Take a straightforward Django project for example. Given a models.py file, AI can easily write the corresponding admin file, or a RESTful API file. That’s generally just tedious boilerplate work that requires no decision making - perfect for an AI.
More than that and you are probably babysitting the AI so hard that it is faster to just write it yourself.
They have been pretty good on popular technologies like python & web development.
I tried to do Kotlin for Android, and they kept tripping over themselves; it’s hilarious and frustrating at the same time.
I use ChatGPT for Go programming all the time and it rarely has problems, I think Go is more niche than Kotlin
I get a bit frustrated at it trying to replicate everyone else’s code in my code base. Once my project became large enough, I felt it necessary to implement my own error handling instead of go’s standard, which was not sufficient for me anymore. Copilot will respect that for a while, until I switch to a different file. At that point it will try to force standard go errors everywhere.
Yes, you can’t use Copilot to generate files in your code structure way if you start from scratch. I usually start by coding a skaffold and then use Copilot to complete the rest, which works quite good most of the time. Another possibility is to create comment templates that will give instructions to Copilot. So every new Go file starts with coding structure comments and Copilot will respect that. Junior Devs might also respect that, but I am not so sure about them
I am on you with this one. It is also very helpful in argument heavy libraries like plotly. If I ask a simple question like “in plotly how do I do this and that to the xaxis” etc it generally gives correct answers, saving me having to do internet research for 5-10 minutes or read documentations for functions with 1000 inputs. I even managed to get it to render a simple scene of cloud of points with some interactivity in 3js after about 30 minutes of back and forth. Not knowing much javascript, that would take me at least a couple hours. So yeah it can be useful as an assistant to someone who already knows coding (so the person can vet and debug the code).
Though if you weigh pros and cons of how LLMs are used (tons of fake internet garbage, tons of energy used, very convincing disinformation bots), I am not convinced benefits are worth the damages.
Why do you want AI to save you for learning and understanding the tools you use?
If you do it through AI you can still learn. After all I go through the code to understand what is going on. And for not so complex tasks LLMs are good at commenting the code (though it can bullshit from time to time so you have to approach it critically).
But anyways the stuff I ask LLMs are generally just one off tasks. If I need to use something more frequently, I do prefer reading stuff for more in depth understanding.
Perhaps 5 LOC. Maybe 3. And even then I’ll analyze every single character in wrote. And then I will in fact find bugs. Most often it hallucinates some functions that would be fantastic to use - if they existed.
My guess is what’s going on is there’s tons of psuedo code out there that looks like it’s a real language but has functions that don’t exist as placeholders and the LLM noticed the pattern to the point where it just makes up functions, not realizing they need to be implemented (because LLMs don’t realize things but just pattern match very complex patterns).
Practically all LLMs aren’t good for any logic. Try to play ASCII tic tac toe against it. All GPT models lost against my four year old niece and I wouldn’t trust her writing production code 🤣
Once a single model (doesn’t have to be a LLM) can beat Stockfish in chess, AlphaGo in Go, my niece in tic tac toe and can one-shot (on the surface, scratch-pad allowed) a Rust program that compiles and works, than we can start thinking about replacing engineers.
Just take a look at the dotnet runtime source code where Microsoft employees currently try to work with copilot, which writes PRs with errors like forgetting to add files to projects. Write code that doesn’t compile, fix symptoms instead of underlying problems, etc. (just take a look yourself).
I don’t say that AI (especially AGI) can’t replace humans. It definitely can and will, it’s just a matter of time, but state of the Art LLMs are basically just extremely good “search engines” or interactive versions of “stack overflow” but not good enough to do real “thinking tasks”.
Cherry picking the things it doesn’t do well is fine, but you shouldn’t ignore the fact that it DOES do some things easily also.
Like all tools, use them for what they’re good at.
I don’t think it’s cherry picking. Why would I trust a tool with way more complex logic, when it can’t even prevent three crosses in a row? Writing pretty much any software that does more than render a few buttons typically requires a lot of planning and thinking and those models clearly don’t have the capability to plan and think when they lose tic tac toe games.
Why would I trust a drill press when it can’t even cut a board in half?
A drill press (or the inventors) don’t claim that it can do that, but with LLMs they claim to replace humans on a lot of thinking tasks. They even brag with test benchmarks, claim Bachelor, Master and Phd level intelligence, call them “reasoning” models, but still fail to beat my niece in tic tac toe, which by the way doesn’t have a PhD in anything 🤣
LLMs are typically good in things that happened a lot during training. If you are writing software there certainly are things which the LLM saw a lot of during training. But this actually is the biggest problem, it will happily generate code that might look ok, even during PR review but might blow up in your face a few weeks later.
If they can’t handle things they even saw during training (but sparsely, like tic tac toe) it wouldn’t be able to produce code you should use in production. I wouldn’t trust any junior dev that doesn’t set their O right next to the two Xs.
Sure, the marketing of LLMs is wildly overstated. I would never argue otherwise. This is entirely a red herring, however.
I’m saying you should use the tools for what they’re good at, and don’t use them for what they’re bad at. I don’t see why this is controversial at all. You can personally decide that they are good for nothing. Great! Nobody is forcing you to use AI in your work. (Though if they are, you should find a new employer.)
Totally agree with that and I don’t think anybody would see that as controversial. LLMs are actually good in a lot of things, but not thinking and typically not if you are an expert. That’s why LLMs know more about the anatomy of humans than I do, but probably not more than most people with a medical degree.
It’s futile even trying to highlight the things LLMs do very well as Lemmy is incredibly biased against them.
I can’t speak for Lemmy but I’m personally not against LLMs and also use them on a regular basis. As Pennomi said (and I totally agree with that) LLMs are a tool and we should use that tool for things it’s good for. But “thinking” is not one of the things LLMs are good at. And software engineering requires a ton of thinking. Of course there are things (boilerplate, etc.) where no real thinking is required, but non-AI tools like code completion/intellisense, macros, code snippets/templates can help with that and never was I bottle-necked by my typing speed when writing software.
It was always the time I needed to plan the structure of the software, design good and correct abstractions and the overall architecture. Exactly the things LLMs can’t do.
Copilot even fails to stick to coding style from the same file, just because it saw a different style more often during training.
“I’m not again LLMs I just never say anything useful about them and constantly point out how I can’t use them.” The other guy is right and you just prove his point.
extremely good “search engines” or interactive versions of “stack overflow”
Which is such a decent use of them! I’ve used it on my own hardware a few times just to say “Hey give me a comparison of these things”, or “How would I write a function that does this?” Or “Please explain this more simply…more simply…more simply…”
I see it as a search engine that connects nodes of concepts together, basically.
And it’s great for that. And it’s impressive!
But all the hype monkeys out there are trying to pedestal it like some kind of techno-super-intelligence, completely ignoring what it is good for in favor of “It’ll replace all human coders” fever dreams.
Try to get one of these LLMs to update a package.json.
Define “update”
ones that can run cli tools do great, they just use npm
Did it try to blackmail him if he didn’t use the new code?
