Please remove it if unallowed
I see alot of people in here who get mad at AI generated code and I am wondering why. I wrote a couple of bash scripts with the help of chatGPT and if anything, I think its great.
Now, I obviously didnt tell it to write the entire code by itself. That would be a horrible idea, instead, I would ask it questions along the way and test its output before putting it in my scripts.
I am fairly competent in writing programs. I know how and when to use arrays, loops, functions, conditionals, etc. I just dont know anything about bash’s syntax. Now, I could have used any other languages I knew but chose bash because it made the most sense, that bash is shipped with most linux distros out of the box and one does not have to install another interpreter/compiler for another language. I dont like Bash because of its, dare I say weird syntax but it made the most sense for my purpose so I chose it. Also I have not written anything of this complexity before in Bash, just a bunch of commands in multiple seperate lines so that I dont have to type those one after another. But this one required many rather advanced features. I was not motivated to learn Bash, I just wanted to put my idea into action.
I did start with internet search. But guides I found were lacking. I could not find how to pass values into the function and return from a function easily, or removing trailing slash from directory path or how to loop over array or how to catch errors that occured in previous command or how to seperate letter and number from a string, etc.
That is where chatGPT helped greatly. I would ask chatGPT to write these pieces of code whenever I encountered them, then test its code with various input to see if it works as expected. If not, I would ask it again with what case failed and it would revise the code before I put it in my scripts.
Thanks to chatGPT, someone who has 0 knowledge about bash can write bash easily and quickly that is fairly advanced. I dont think it would take this quick to write what I wrote if I had to do it the old fashioned way, I would eventually write it but it would take far too long. Thanks to chatGPT I can just write all this quickly and forget about it. If I want to learn Bash and am motivated, I would certainly take time to learn it in a nice way.
What do you think? What negative experience do you have with AI chatbots that made you hate them?
People who use LLMs to write code (incorrectly) perceived their code to be more secure than code written by expert humans.
Lol.
We literally had an applicant use AI in an interview, failed the same step twice, and at the end we asked how confident they were in their code and they said “100%” (we were hoping they’d say they want time to write tests). Oh, and my coworker and I each found two different bugs just by reading the code. That candidate didn’t move on to the next round. We’ve had applicants write buggy code, but they at least said they’d want to write some test before they were confident, and they didn’t use AI at all.
I thought that was just a one-off, it’s sad if it’s actually more common.
OP was able to write a bash script that works… on his machine 🤷 that’s far from having to review and send code to production either in FOSS or private development.
I also noticed that they were talking about sending arguments to a custom function? That’s like a day-one lesson if you already program. But this was something they couldn’t find in regular search?
Maybe I misunderstood something.
Exactly. If you understand that functions are just commands, then it’s quite easy to extrapolate how to pass arguments to that function:
function my_func () { echo $1 $2 $3 # prints a b c } my_func a b cOnce you understand that core concept, a lot of Bash makes way more sense. Oh, and most of the syntax I provided above is completely unnecessary, because Bash…
Hmm, I’m having trouble understanding the syntax of your statement.
Is it
(People who use LLMs to write code incorrectly) (perceived their code to be more secure) (than code written by expert humans.)Or is it
(People who use LLMs to write code) (incorrectly perceived their code to be more secure) (than code written by expert humans.)The “statement” was taken from the study.
We conduct the first large-scale user study examining how users interact with an AI Code assistant to solve a variety of security related tasks across different programming languages. Overall, we find that participants who had access to an AI assistant based on OpenAI’s codex-davinci-002 model wrote significantly less secure code than those without access. Additionally, participants with access to an AI assistant were more likely to believe they wrote secure code than those without access to the AI assistant. Furthermore, we find that participants who trusted the AI less and engaged more with the language and format of their prompts (e.g. re-phrasing, adjusting temperature) provided code with fewer security vulnerabilities. Finally, in order to better inform the design of future AI-based Code assistants, we provide an in-depth analysis of participants’ language and interaction behavior, as well as release our user interface as an instrument to conduct similar studies in the future.
I agree AI is a godsend for non coders and amateur programmers who need a quick and dirty script. As a professional, the quality of code is oftentimes 💩 and I can write it myself in less time than it takes to describe it to an AI.
i love it when the AI declares and sets important sounding variables it then never uses 🙄
AI is a godsend for non coders and amateur programmers who need a quick and dirty script.
Why?
I mean, it is such a cruel thing to say.
50% of these poor non coders and amateur programmers would end up with a non-functioning script. I find it so unfair!
You have not even tried to decide who deserves and gets the working solution and who gets the garbage script. You are soo evil…
I think you’ve hit the nail on the head. I am not a coder but using chatGPT I was able to take someone else’s simple program and modify for my own needs within just a few hours of work. It’s definitely not perfect and you still need to put in some work to get your program to run exactly the way you want it to but it’s using chatGPT is a good place to start for beginners, as long as they understand that it’s not a magic tool.
I think the process of explaining what your want to an AI can often be helpful. Especially given the number of times I’ve explained things to junior developer’s and they’ve said they understood completely, but then when I see what they wrote they clearly didn’t.
Explaining to an AI is a pretty good test of how well the stories and comments are written.
Many lazy programmers may just copy paste without thinking too much about the quality of generated code. The other group of person who oppose it are those who think it will kill the programmer job
Many lazy programmers may just copy paste without thinking too much about the quality of generated code
Tbf, they’ve been doing that LONG before AI came along
Sure, but if you’re copying from stack overflow or reddit and ignore the dozens of comments telling you why the code you’re copying is wrong for your use case, that’s on you.
An LLM on the other hand will confidently tell you that its garbage is perfect and will do exactly what you asked for, and leave you to figure out why it doesn’t by yourself, without any context.
An inexperienced programmer who’s willing to learn won’t fall for the first case and will actually learn from the comments and alternative answers, but will be completely lost if the hallucinating LLM is all they’ve got.
There is an enormous difference between:
rm -rf / path/filevs.
rm -rf /path/file
Also if you are interested, here are those scripts I wrote with chatGPT
Lol this comment is the opposite of the code it’s trying to describe, https://gitlab.com/cy_narrator/lukshelper/-/blob/main/luksCreate.sh?ref_type=heads#L107
Changed and added a feature to check if volume exists before override
deleted by creator
Now, I obviously didnt tell it to write the entire code by itself. […]
I am fairly competent in writing programs.
Go ahead using it. You are safe.
but chose bash because it made the most sense, that bash is shipped with most linux distros out of the box and one does not have to install another interpreter/compiler for another language.
Last time I checked (because I was writing Bash scripts based on the same assumption), Python was actually present on more Linux systems out of the box than Bash.
One point that stands out to me is that when you ask it for code it will give you an isolated block of code to do what you want.
In most real world use cases though you are plugging code into larger code bases with design patterns and paradigms throughout that need to be followed.
An experienced dev can take an isolated code block that does X and refactor it into something that fits in with the current code base etc, we already do this daily with Stackoverflow.
An inexperienced dev will just take the code block and try to ram it into the existing code in the easiest way possible without thinking about if the code could use existing dependencies, if its testable etc.
So anyway I don’t see a problem with the tool, it’s just like using Stackoverflow, but as we have seen businesses and inexperienced devs seem to think it’s more than this and can do their job for them.
I have a coworker who is essentially building a custom program in Sheets using AppScript, and has been using CGPT/Gemini the whole way.
While this person has a basic grasp of the fundamentals, there’s a lot of missing information that gets filled in by the bots. Ultimately after enough fiddling, it will spit out usable code that works how it’s supposed to, but honestly it ends up taking significantly longer to guide the bot into making just the right solution for a given problem. Not to mention the code is just a mess - even though it works there’s no real consistency since it’s built across prompts.
I’m confident that in this case and likely in plenty of other cases like it, the amount of time it takes to learn how to ask the bot the right questions in totality would be better spent just reading the documentation for whatever language is being used. At that point it might be worth it to spit out simple code that can be easily debugged.
Ultimately, it just feels like you’re offloading complexity from one layer to the next, and in so doing quickly acquiring tech debt.
Exactly my experience as well. Using AI will take about the same amount of time as just doing it myself, but at least I’ll understand the code at the end if I do it myself. Even if AI was a little faster to get working code, writing it yourself will pay off in debugging later.
And honestly, I enjoy writing code more than chatting with a bot. So if the time spent is going to be similar, I’m going to lean toward DIY every time.
People are in denial. AI is going to take programmer’s jobs away, and programmers perceive AI as a natural enemy and a threat. That is why they want to discredit it in any way possible.
Honestly, I’ve used chatGPT for a hundred tasks, and it has always resulted in acceptable, good-quality work. I’ve never (!) encountered chatGPT making a grave or major error in any of the questions that I asked it (physics and material sciences).
Because most people on Lemmy have never actually had to write code professionally.
If the AI was trained on code that people permitted it to be freely shared then go ahead. Taking code and ignoring the software license is largely considered a dick-move, even by people who use AI.
Some people choose a copyleft software license to ensure users have software freedom, and this AI (a math process) circumvents that. [A copyleft license makes it so that you can use the code if you agree to use the same license for the rest of the program - therefore users get the same rights you did]
I hate big tech too, but I’m not really sure how the GPL or MIT licenses (for example) would apply. LLMs don’t really memorize stuff like a database would and there are certain (academic/research) domains that would almost certainly fall under fair use. LLMs aren’t really capable of storing the entire training set, though I admit there are almost certainly edge cases where stuff is taken verbatim.
I’m not advocating for OpenAI by any means, but I’m genuinely skeptical that most copyleft licenses have any stake in this. There’s no static linking or source code distribution happening. Many basic algorithms don’t follow under copyright, and, in practice, stack overflow code is copy/pasted all the time without that being released under any special license.
If your code is on GitHub, it really doesn’t matter what license you provide in the repository – you’ve already agreed to allowing any user to “fork” it for any reason whatsoever.
Be it a complicated neural network or database matters not. It output portions of the code used as input by design.
If you can take GPL code and “not” distribute it via complicated maths then that circumvents it. That won’t do, friendo.
For example, if I ask it to produce python code for addition, which GPL’d library is it drawing from?
I think it’s clear that the fair use doctrine no longer applies when OpenAI turns it into a commercial code assistant, but then it gets a bit trickier when used for research or education purposes, right?
I’m not trying to be obtuse-- I’m an AI researcher who is highly skeptical of AI. I just think the imperfect compression that neural networks use to “store” data is a bit less clear than copy/pasting code wholesale.
would you agree that somebody reading source code and then reimplenting it (assuming no reverse engineering or proprietary source code) would not violate the GPL?
If so, then the argument that these models infringe on right holders seems to hinge on the verbatim argument that their exact work was used without attribution/license requirements. This surely happens sometimes, but is not, in general, a thing these models are capable of since they’re using loss-y compression to “learn” the model parameters. As an additional point, it would be straightforward to then comply with DMCA requests using any number of published “forced forgetting” methods.
Then, that raises a further question.
If I as an academic researcher wanted to make a model that writes code using GPL’d training data, would I be in compliance if I listed the training data and licensed my resulting model under the GPL?
I work for a university and hate big tech as much as anyone on Lemmy. I am just not entirely sure GPL makes sense here. GPL 3 was written because GPL 2 had loopholes that Microsoft exploited and I suspect their lawyers are pretty informed on the topic.
The corresponding training data is the best bet to see what code an input might be copied from. This can apply to humans too. To avoid lawsuits reverse engineering projects use a clean room strategy: requiring contributors to have never seen the original code. This is to argue they can’t possibility be copying, even from memory (an imperfect compression too.
If it doesn’t include GPL code then that can’t violate the GPL. However, OpenAI argue they have to use copyrighted works to make specific AIs (if I recall correctly). Even if legal, that’s still a problem to me.
My understanding is AI generated media can’t be copyrighted as it wasn’t a person being creative - like the monkey selfie copyright dispute.
Yeah. I’m thinking more along the lines of research and open models than anything to do with OpenAI. Fair use, above all else, generally requires that the derivative work not threaten the economic viability of the original and that’s categorically untrue of ChatGPT/Copilot which are marketed and sold as products meant to replace human workers.
The clean room development analogy is definitely an analogy I can get behind, but raises further questions since LLMs are multi stage. Technically, only the tokenization stage will “see” the source code, which is a bit like a “clean room” from the perspective of subsequent stages. When does something stop being just a list of technical requirements and veer into infringement? I’m not sure that line is so clear.
I don’t think the generative copyright thing is so straightforward since the model requires a human agent to generate the input even if the output is deterministic. I know, for example, Microsoft’s Image Generator says that the images fall under creative Commons, which is distinct from public domain given that some rights are withheld. Maybe that won’t hold up in court forever, but Microsoft’s lawyers seem to think it’s a bit more nuanced than “this output can’t be copyrighted”. If it’s not subject to copyright, then what product are they selling? Maybe the court agrees that LLMs and monkeys are the same, but I’m skeptical that that will happen considering how much money these tech companies have poured into it and how much the United States seems to bend over backwards to accommodate tech monopolies and their human rights violations.
Again, I think it’s clear that commerical entities using their market position to eliminate the need for artists and writers is clearly against the spirit of copyright and intellectual property, but I also think there are genuinely interesting questions when it comes to models that are themselves open source or non-commercial.
As someone who just delved into a related but unfamiliar language for a small project, it was relatively correct and easy to use.
There were a few times it got itself into a weird “loop” where it insisted on doing things in a ridiculous way, but prior knowledge of programming was enough for me to reword and “suggest” different, simpler, solutions.
Would I have ever got to the end of that project without knowledge of programming and my suggestions? Likely, but it would have taken a long time and been worse off code.
The irony is, without help from copilot, I’d have taken at least three times as long.
I have worked with somewhat large codebases before using LLMs. You can ask the LLM to point a specific problem and give it the context. I honestly don’t see myself as capable without a LLM. And it is a good teacher. I learn much from using LLMs. No free advertisement for any of the suppliers here, but they are just useful.
You get access to information you can’t find on any place of the Web. There is a large structural bad reaction to it, but it is useful.
(Edit) Also, I would like to add that people who said that questions won’t be asked anymore seemingly never tried getting answers online in a discussion forum - people are viciously ill-tempered when answering.
With a LLM, you can just bother it endlessly and learn more about the world while you do it.
We built a Durable task workflow engine to manage infrastructure and we asked a new hire to add a small feature to it.
I checked on them later and they expressed they were stuck on an aspect of the change.
I could tell the code was ChatGPT. I asked “you wrote this with ChatGPT didn’t you?” And they asked how I could tell.
I explained that ChatGPT doesn’t have the full context and will send you on tangents like it has here.
I gave them the docs to the engine and to the integration point and said "try using only these and ask me questions if you’re stuck for more than 40min.
They went on to become a very strong contributor and no longer uses ChatGPT or copilot.
I’ve tried it myself and it gives me the wrong answers 90% of the time. It could be useful though. If they changed ChatGPT to find and link you docs it finds relevant I would love it but it never does even when asked.
Phind is better about linking sources. I’ve found that generated code sometimes points me in the right direction, but other times it leads me down a rabbit hole of obsolete syntax or other problems.
Ironically, if you already are familiar with the code then you can easily tell where the LLM went wrong and adapt their generated code.
But I don’t use it much because its almost more trouble than its worth.
Personally, I’ve found AI is wrong about 80% of the time for questions I ask it.
It’s essentially just a search engine with cleverbot. If the problem you’re dealing with is esoteric and therefore not easily searchable, AI won’t fare any better.
I think AI would be a lot more useful if it gave a percentage indicating how confident it is in its answers, too. It’s very useless to have it constantly give wrong information as though it is correct.
