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?
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.
Because despite how easy it is to dupe people into thinking your methods are altruistic- AI exists to save money by eradicating jobs.
AI is the enemy. No matter how you frame 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.
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.
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.
I use ai, but whenever I do I have to modify it, whether it’s because it gives me errors, is slow, doesn’t fit my current implementation or is going off the wrong foot.
- issues with model training sources
- business sending their whole codebase to third party (copilot etc.) instead of local models
- time gain is not that substantial in most case, as the actual “writing code” part is not the part that takes most time, thinking and checking it is
- “chatting” in natural language to describe something that have a precise spec is less efficient than just writing code for most tasks as long as you’re half-competent. We’ve known that since customer/developer meetings have existed.
- the dev have to actually be competent enough to review the changes/output. In a way, “peer reviewing” becomes mandatory; it’s long, can be fastidious, and generated code really needs to be double checked at every corner (talking from experience here; even a generated one-liner can have issues)
- some business thinking that LLM outputs are “good enough”, firing/moving away people that can actually do said review, leading to more issues down the line
- actual debugging of non-trivial problems ends up sending me in a lot of directions, getting a useful output is unreliable at best
- making new things will sometimes confuse LLM, making them a time loss at best, and producing even worst code sometimes
- using code chatbot to help with common, menial tasks is irrelevant, as these tasks have already been done and sort of “optimized out” in library and reusable code. At best you could pull some of this in your own codebase, making it worst to maintain in the long term
Those are the downside I can think of on the top of my head, for having used AI coding assistance (mostly local solutions for privacy reasons). There are upsides too:
- sometimes, it does produce useful output in which I only have to edit a few parts to make it works
- local autocomplete is sometimes almost as useful as the regular contextual autocomplete
- the chatbot turning short code into longer “natural language” explanations can sometimes act as a rubber duck in aiding for debugging
Note the “sometimes”. I don’t have actual numbers because tracking that would be like, hell, but the times it does something actually impressive are rare enough that I still bother my coworker with it when it happens. For most of the downside, it’s not even a matter of the tool becoming better, it’s the usefulness to begin with that’s uncertain. It does, however, come at a large cost (money, privacy in some cases, time, and apparently ecological too) that is not at all outweighed by the rare “gains”.
a lot of your issues are effeciency related which i think can realistically be solved given some time for development cycles to take hold on ai. if they were better all around to whatever standard you think is sufficiently useful, would you then think it would be useful? the other side related thing too is that if it can get that level of competence in coding then it most likely can get just as competant in a variety of other domains too.
When it comes to writing code, there is a huge difference between code that works and code that works *well." Lets say you’re tasked with writing a function that takes an array of RGB values and converts them to grayscale. ChatGPT is probably going to give you two nested loops that iterate over the X and Y values, applying a grayscale transformation to each pixel. This will get the job done, but it’s slow, inefficient, and generally not well-suited for production code. An experienced programmer is going to take into account possible edge cases (what if a color is out of the 0-255 bounds), apply SIMD functions and parallel algorithms, factor in memory management (do we need a new array or can we write back to the input array), etc.
ChatGPT is great for experienced programmers to get new ideas; I use it as a modern version of “rubber ducky” debugging. The problem is that corporations think that LLMs can replace experienced programmers, and that’s just not true. Sure, ChatGPT can produce code that “works,” but it will fail at edge cases and will generally be inefficient and slow.
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5 bucks says the same outages would have happened with human written code.
All right, I guess I’m here to collect then. We doin’ paypal or what?
People who use LLMs to write code incorrectly perceived their code to be more secure than code written by expert humans.
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.)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.
- AI Code suggestions will guide you to making less secure code, not to mention often being lower quality in other ways.
- AI code is designed to look like it fits, not be correct. Sometimes it is correct. Sometimes it’s close but has small errors. Sometimes it looks right but is significantly wrong. Personally I’ve never gotten ChatGPT to write code without significant errors for more than trivially small test cases.
- You aren’t learning as much when you have ChatGPT do it for you, and what you do learn is “this is what chat gpt did and it worked last time” and not “this is what the problem is and last time this is the solution I came up with and this is why that worked”. In the second case you are far better equipped to tackle future problems, which won’t be exactly the same.
All that being said, I do think there is a place for chat GPT in simple queries like asking about syntax for a language you don’t know. But take every answer it gives you with a grain of salt. And if you can find documentation I’d trust that a lot more.
AI Code suggestions will guide you to making less secure code, not to mention often being lower quality in other ways.
This is a PR post from a company selling software.
All that being said, I do think there is a place for chat GPT in simple queries like asking about syntax for a language you don’t know.
I am also weary regarding AI and coding but this is actually the first time I used ChatGpt to programm something for a small home project in python, since I never used it. I was positively surprised in how much it could help me getting started. I also learned quite a bit since I always asked for comparison with Java, which I know, and for reasonings why it is that way. I simply also wanted to understand what it puts out. I also only asked for single lines of code rather than generating a whole method, e.g. I want to move a file from X to Y.
The thought of people blindly copying the produced code scares me.
Yes, I completely forget how to solve that problem 5 minutes after chatGPT writes its solution. So I whole heartedely believe AI is bad for learning
A lot of the criticism comes with AI results being wrong a lot of the time, while sounding convincingly correct. In software, things that appear to be correct but are subtly wrong leads to errors that can be difficult to decipher.
Imagine that your AI was trained on StackOverflow results. It learns from the questions as well as the answers, but the questions will often include snippets of code that just don’t work.
The workflow of using AI resembles something like the relationship between a junior and senior developer. The junior/AI generates code from a spec/prompt, and then the senior/prompter inspects the code for errors. If we remove the junior from the equation to replace with AI, then entry level developer jobs are slashed, and at the same time people aren’t getting the experience required to get to the senior level.
Generally speaking, programmers like to program (many do it just for fun), and many dislike review. AI removes the programming from the equation in favour of review.
Another argument would be that if I generate code that I have to take time to review and figure out what might be wrong with it, it might just be quicker and easier to write it correctly the first time
Business often doesn’t understand these subtleties. There’s a ton of money being shovelled into AI right now. Not only for developing new models, but for marketing AI as a solution to business problems. A greedy executive that’s only looking at the bottom line and doesn’t understand the solution might be eager to implement AI in order to cut jobs. Everyone suffers when jobs are eliminated this way, and the product rarely improves.
Generally speaking, programmers like to program (many do it just for fun), and many dislike review. AI removes the programming from the equation in favour of review.
This really resonated with me and is an excellent point. I’m going to have to remember that one.
A developer who is afraid of peer review is not a developer at all imo, but more or less an artist who fears exposing how the sausage was made.
I’m not saying a junior who is nervous is not a dev, I’m talking about someone who has been at this for some time, and still can’t handle feedback productively.
They’re saying developers dislike having to review other code that’s unfamiliar to them, not having their code reviewed.
As am I, it’s a two way street. You need to review the code, and have it reviewed.
Nowhere did you say this.
I did, and I stand by what I said.
Review is both taken and given. Peer review does not occur in a single direction, it is a conversation with multiple parties. I can understand if someone misunderstood what I meant though.
Your reply refers to a “junior who is nervous” and “how the sausage is made”, which makes no sense in the context of someone who just has to review code
For me it’s because if the AI does all the work the person “coding” won’t learn anything. Thus when a problem does arise (i.e. the AI not being able to fix a simple mistake it made) no one involved has the means of fixing it.
I have seen my friend in this situation
But I don’t want to learn. I want the machine to free me from tedious tasks I already know how to do. There’s no learning experience in creating a Wordpress plugin or a shell script.
If you’re a seasoned developer who’s using it to boilerplate / template something and you’re confident you can go in after it and fix anything wrong with it, it’s fine.
The problem is it’s used often by beginners or people who aren’t experienced in whatever language they’re writing, to the point that they won’t even understand what’s wrong with it.
If you’re trying to learn to code or code in a new language, would you try to learn from somebody who has only half a clue what he’s doing and will confidently tell you things that are objectively wrong? Thats much worse than just learning to do it properly yourself.
I’m a seasoned dev and I was at a launch event when an edge case failure reared its head.
In less than a half an hour after pulling out my laptop to fix it myself, I’d used Cursor + Claude 3.5 Sonnet to:
- Automatically add logging statements to help identify where the issue was occurring
- Told it the issue once identified and had it update with a fix
- Had it remove the logging statements, and pushed the update
I never typed a single line of code and never left the chat box.
My job is increasingly becoming Henry Ford drawing the ‘X’ and not sitting on the assembly line, and I’m all for it.
And this would only have been possible in just the last few months.
We’re already well past the scaffolding stage. That’s old news.
Developing has never been easier or more plain old fun, and it’s getting better literally by the week.
Edit: I agree about junior devs not blindly trusting them though. They don’t yet know where to draw the X.
Edit: I agree about junior devs not blindly trusting them though. They don’t yet know where to draw the X.
The problem (one of the problems) is that people do lean too heavily on the AI tools when they’re inexperienced and never learn for themselves “where to draw the X”.
If I’m hiring a dev for my team, I want them to be able to think for themselves, and not be completely reliant on some LLM or other crutch.
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.
The human brain is compartmentised: you can damage a part and lose the ability to recognizes faces, or name tools. Presumably it can be seen as multi-stage too but would that be a defense? All we can do is look for evidence of copyright infringement in the output, or circumstantial evidence in the input.
I’m not sure the creativity of writing a prompt means you were creative for creating the output. Even if it appears your position is legal you can still lose in court. I think Microsoft is hedging their bets that there will be president to validate their claim of copyright.
There are a few Creative Commons licenses but most actually don’t prevent commercial use (the ShareAlike is like the copyleft in GPL for code). Even if the media output was public domain and others are free to copy/redistribute that doesn’t prevent an author selling public domain works (just harder). Code that is public domain isn’t easily copied as the software is usually shared without it as a binary file.
