Typical CEO thinking number of lines of code is the same as productivity. What was the functionality of those 250k lines? Do arithmetic ops between two ints? Compute if an int is even?
To circumvent a peculiar bottleneck that randomly sprouted up just after their new hire arrived, it’s just 250k lines marking specific numbers odd or even.
int lollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollolloollollollollollollollollollollollollollollollollollollollollollollollollollollollollol = 0;
I’ve been playing with cursor a bit at work (non-dev here - I’m a mechanical engineer writing custom tools for me & my team).
it might just be the way I use it, because my AI-positive boss seems to be more successful than me, but it’s… not great. cursor itself seems to have many problems following instructions. and then the code it comes up with… I’ve seen it make a complete replica of the original code when I asked it to add another scenario to the four already there, and mixed and matched between the two copies to access functions etc so that you couldn’t just remove one.
it’s definitely helped me a bit when I got stuck with a particular issue and didn’t know what API calls to be using, but in general… idk. I don’t have enough time with it yet, nor am I skilled enough to judge it properly, I think.
So far, the only time AI seems to works well - and only sometimes - is as autocomplete for a single line. It does such a terrible job at generating larger chunks of code that you will spend more time correcting the problems than if you had written it yourself or used the template-based features of a half-decent IDE. It doesn’t matter which LLM you use, they are all bad. Everything an AI outputs is a hallucination, even when it’s correct. The system is not capable of reasoning or thinking, it can’t apply logic to problems. As a result, you can’t trust any code it gives you in the least.
I can assure you LLMs are, in general, not great at extracting concepts and work upon it, like a human mind is. LLMs are statistical parrots, that have learned to associate queries with certain output patterns like code chunks, or text chunks, etc. They are not really intelligent, certainly not like a human is. They cannot follow instructions like a human does, because of this. Problem is, they seem just intelligent enough that they can fool someone wanting to believe them to be intelligent even though there is no intelligence, by any measure, behind their replies.
It also doesn’t help that you have AGI evangelists like Yarvin and Musk who keep saying that the techno-singularity/techno-god is the ONLY WAY TO SAVE US, and that we’re RIGHT ON THE EDGE, so a lot of dumb fucks see that and go “well obviously this is like querying an average human mind which has access to all of human knowledge for the problem if superhuman jntelligence is right around the corner.”
I’m a dev and I think my experience is mostly similar to yours. Where AI seems to work well, is when the boundaries of the problem are very well defined. For example : “Take this C++ implementation of the LCS algorithm and convert it to JS”. That would have taken me a few hours at least, but AI appears to have nailed it. However, anything where a large amount of context is needed and it starts to fail fast, and suggest absolutely insane things. I have turned of copilot on my IDE because it slows me down, but I will still ask questions to chatgpt when I have a specific problem I think it can help me with. I also will ask pointed questions when I review other dev’s code, and my expectation is the author can explain why they wrote it.
Typical CEO thinking number of lines of code is the same as productivity. What was the functionality of those 250k lines? Do arithmetic ops between two ints? Compute if an int is even?
To circumvent a peculiar bottleneck that randomly sprouted up just after their new hire arrived, it’s just 250k lines marking specific numbers odd or even.
Breaks as soon as they get to 250k+1
and thinks 0 is an even number.Why shouldn’t it think that 0 is an even number? It’s divisible by 2.
Damn, I was thinking of the primeness of 1 and trying to be clever, which demonstrated I wasn’t.
Actually, it’s just the first 250k prime numbers
int lollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollollolloollollollollollollollollollollollollollollollollollollollollollollollollollollollollol = 0;
I’ve been playing with cursor a bit at work (non-dev here - I’m a mechanical engineer writing custom tools for me & my team).
it might just be the way I use it, because my AI-positive boss seems to be more successful than me, but it’s… not great. cursor itself seems to have many problems following instructions. and then the code it comes up with… I’ve seen it make a complete replica of the original code when I asked it to add another scenario to the four already there, and mixed and matched between the two copies to access functions etc so that you couldn’t just remove one.
it’s definitely helped me a bit when I got stuck with a particular issue and didn’t know what API calls to be using, but in general… idk. I don’t have enough time with it yet, nor am I skilled enough to judge it properly, I think.
It’s not just you. Cursor is horrible.
So far, the only time AI seems to works well - and only sometimes - is as autocomplete for a single line. It does such a terrible job at generating larger chunks of code that you will spend more time correcting the problems than if you had written it yourself or used the template-based features of a half-decent IDE. It doesn’t matter which LLM you use, they are all bad. Everything an AI outputs is a hallucination, even when it’s correct. The system is not capable of reasoning or thinking, it can’t apply logic to problems. As a result, you can’t trust any code it gives you in the least.
It’s exactly as bad as you describe.
I can assure you LLMs are, in general, not great at extracting concepts and work upon it, like a human mind is. LLMs are statistical parrots, that have learned to associate queries with certain output patterns like code chunks, or text chunks, etc. They are not really intelligent, certainly not like a human is. They cannot follow instructions like a human does, because of this. Problem is, they seem just intelligent enough that they can fool someone wanting to believe them to be intelligent even though there is no intelligence, by any measure, behind their replies.
It also doesn’t help that you have AGI evangelists like Yarvin and Musk who keep saying that the techno-singularity/techno-god is the ONLY WAY TO SAVE US, and that we’re RIGHT ON THE EDGE, so a lot of dumb fucks see that and go “well obviously this is like querying an average human mind which has access to all of human knowledge for the problem if superhuman jntelligence is right around the corner.”
I’m a dev and I think my experience is mostly similar to yours. Where AI seems to work well, is when the boundaries of the problem are very well defined. For example : “Take this C++ implementation of the LCS algorithm and convert it to JS”. That would have taken me a few hours at least, but AI appears to have nailed it. However, anything where a large amount of context is needed and it starts to fail fast, and suggest absolutely insane things. I have turned of copilot on my IDE because it slows me down, but I will still ask questions to chatgpt when I have a specific problem I think it can help me with. I also will ask pointed questions when I review other dev’s code, and my expectation is the author can explain why they wrote it.
if n% 2 == 0: print("Even") else: print("Odd") if (n+1)%2 == 0: print("Even") else: print("Odd") . . .Whatever the language’s equivalent of x86 assembly “NOP” is (forgive me - been decades since I programmed, so I’m not up on the modem languages).