81–90 of 229 posts
Earlier quoted context omitted.
The over-hype is causing it to look under-hyped. 48% of the code being freaking correct from 0% about 5 years ago is huge. With everyone and all the news talking about it constantly inevitably people are going to start rolling their eyes. This is bias. 48% is half way to 100%. Once it reaches 100% you don't have a job. You realize that right? It's halfway their to taking your job and you're underwhelmed. The bias is…
100-62=38%
Earlier quoted context omitted.
The over-hype is causing it to look under-hyped. 48% of the code being freaking correct from 0% about 5 years ago is huge. With everyone and all the news talking about it constantly inevitably people are going to start rolling their eyes. This is bias. 48% is half way to 100%. Once it reaches 100% you don't have a job. You realize that right? It's halfway their to taking your job and you're underwhelmed. The bias is…
38%. And 38% is not "a third way to 100%". I'm sure you know what diminishing return is.
Earlier quoted context omitted.
The over-hype is causing it to look under-hyped. 48% of the code being freaking correct from 0% about 5 years ago is huge. With everyone and all the news talking about it constantly inevitably people are going to start rolling their eyes. This is bias. 48% is half way to 100%. Once it reaches 100% you don't have a job. You realize that right? It's halfway their to taking your job and you're underwhelmed. The bias is…
100% - 62% = 38%
Earlier quoted context omitted.
The over-hype is causing it to look under-hyped. 48% of the code being freaking correct from 0% about 5 years ago is huge. With everyone and all the news talking about it constantly inevitably people are going to start rolling their eyes. This is bias. 48% is half way to 100%. Once it reaches 100% you don't have a job. You realize that right? It's halfway their to taking your job and you're underwhelmed. The bias is…
> Once it reaches 100% you don't have a job. You realize that right? It's halfway their to taking your job and you're underwhelmed. The bias is on your side. What this study looked at was feeding StackOverflow questions to LLMs and then looking at the quality of the code. If you think a programmer's job is just turning an english-language description of a function into isolated code that never gets modified, I don't…
I feel like AI for programming has been so overhyped. I've attempted to use ChatGPT for programming so many times, and almost every time it's just wasted my time. Giving me outright lies or generating stuff that looks right but doesn't work. And just throwing out it's answer and starting from scratch is faster than fixing its output. I've only found it useful for explaining basic terminology or concepts for a topic I…
The over-hype is causing it to look under-hyped. 48% of the code being freaking correct from 0% about 5 years ago is huge. With everyone and all the news talking about it constantly inevitably people are going to start rolling their eyes. This is bias. 48% is half way to 100%. Once it reaches 100% you don't have a job. You realize that right? It's halfway their to taking your job and you're underwhelmed. The bias is…
You’re implying 100% reliability is actually attainable. If that were the case, wouldn’t that mean the halting problem would have been solved by AI? I’m not an expert but I’ve heard that’s like one of those fundamental laws of information theory that really can’t be broken.
> It's typical. It's like an indie band is only popular when not many people know about it. Once everybody starts talking about it all the time it loses it's popularity.
I think applying the sociology of hipster band fans to LLMs is a mistake and I’m not sure how the vogueness of the technology correlates with the correctness of the actual models. Sometimes an early technology seems docile or useless at first but eventually reaches ubiquity and in retrospect the utility is obvious. But sometimes (more often than not) standard adoption curves don’t make sense to apply to a new technology because it isn’t useful enough to go through an adoption cycle. I think there’s a temptation to apply the analogy of something like the early internet or smartphone to LLMs but those are networked products. LLMs don’t really improve with the various applications built on top of them if the LLM is itself fundamentally broken or faulty to the point that it is unsafe to use in practice. Furthermore given the massive amount of premature hype due to the AI zeitgeist, you can safely assume enough user hours have been spent messing with LLMs to get a verdict on their utility. Unlike the feeble technology that takes a longtime to reach a scale to know its utility, we don’t have to wait 10 years for 100m people to try LLMs, it happened in a week. My only point being, I think we should be apprehensive about trying to draw analogies of other adoption cycles that structurally are very very different to this one.
Obviously, I, like probably everyone else on this website would love LLMs to be reliable to a high degree. Just this morning I had such a good use for an LLM that I was seriously considering building (and admittedly still am pondering) but the second I started to think through the LLM faultiness, I had to consider the complexity of the safe guards and weigh if it was really better to use GPT or just write a nasty regex script and constrain the problem. I’m leaning heavily towards the latter but I’d much prefer a silver bullet if it really killed vampires. Until then (if that day ever comes), it’s lead bullets for me.
Earlier quoted context omitted.
The over-hype is causing it to look under-hyped. 48% of the code being freaking correct from 0% about 5 years ago is huge. With everyone and all the news talking about it constantly inevitably people are going to start rolling their eyes. This is bias. 48% is half way to 100%. Once it reaches 100% you don't have a job. You realize that right? It's halfway their to taking your job and you're underwhelmed. The bias is…
Except the eternal problem with all "AI" so far is edge cases. A product that is 90% correct may as well be useless, honestly.
It works but only edge cases are a problem. Next step is to fix the edge cases.
I feel like AI for programming has been so overhyped. I've attempted to use ChatGPT for programming so many times, and almost every time it's just wasted my time. Giving me outright lies or generating stuff that looks right but doesn't work. And just throwing out it's answer and starting from scratch is faster than fixing its output. I've only found it useful for explaining basic terminology or concepts for a topic I…
There's a big gap between people using it to write greenfield applications and/or smaller tools, and people working on large codebases. I am in the latter group, and I don't find it all that helpful. There simply aren't any tools that can plug into a massive codebase with millions of lines. I never just work on one specific repository either - a change generally involves multiple repositories. On the other hand, if y…
Run the code it writes, if it gives an error, paste it into the chat and 90% of the time the LLM can fix the issue. People are missing the point here - it's not about writing code in one-shot. An LLM-enabled loop can generate the code and test and refine it until it works
Someone posted a transcript of having chatgpt write them a bash script with the comment "see how much easier this was than figuring it out" and it took like a dozen tries of pasting error messages back in. I was infuriated just reading it. I cannot imagine trying to develop this way. But if people like it, whatever blows your hair back. Allegedly the benefit is it will do the boring stuff like write the error handlin…
I agree; who am I to comment on how you write the code, as long as it gets written, right? That said, I've already had a "this is really odd code you committed last week, what's up with that?" and the reply was "oh, dunno, that's just what ChatGPT gave me". Meh. Actually writing things yourself does increase your understanding, so it's not the same, not really. It's when they told you to take notes at school: I didn't believe my teachers it'll help retain knowledge on account of being a stubborn little idiot, but they were right!
For people who have already done this programming thing for a while I guess it'll work out, but my main worry is the effect it will have on more junior people who will "grow up" on ChatGPT. "Figuring it out" yourself has a lot of value.
Just because the code is free of syntax errors doesn't mean it's free of bugs. Shell scripting is a classic case where it's hard to make something work but also buggy. You need to actually understand the code and reason about it. Trail-and-error programming rarely leads to good code.
I also fear we'll end up with hard to read overly verbose/repetitive code, "because ChatGPT/copilot will just generate it".
I feel like AI for programming has been so overhyped. I've attempted to use ChatGPT for programming so many times, and almost every time it's just wasted my time. Giving me outright lies or generating stuff that looks right but doesn't work. And just throwing out it's answer and starting from scratch is faster than fixing its output. I've only found it useful for explaining basic terminology or concepts for a topic I…
The over-hype is causing it to look under-hyped. 48% of the code being freaking correct from 0% about 5 years ago is huge. With everyone and all the news talking about it constantly inevitably people are going to start rolling their eyes. This is bias. 48% is half way to 100%. Once it reaches 100% you don't have a job. You realize that right? It's halfway their to taking your job and you're underwhelmed. The bias is…
It's a great assistant. I use it. It's not taking anybody's job.