It gives a false sense of security to beginner programmers and doesn’t offer a more tailored solution that a more practiced programmer might create. This can lead to a reduction in code quality and can introduce bugs and security holes over time. If you don’t know the syntax of a language how do you know it didn’t offer you something dangerous? I have copilot at work and the only thing I actually accept its suggestions for now are writing log statements and populating argument lists. While those both still require review they are generally faster than me typing them out. Most of the rest of what it gives me is undesired: it’s either too verbose, too hard to read, or just does something else entirely.

Also if you are interested, here are those scripts I wrote with chatGPT

https://gitlab.com/cy_narrator/lukshelper

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

Sounds like it’s just another tool in a coding arsenal! As long as you take care to verify things like you did, I can’t see why it’d be a bad idea. It’s when you blindly trust that things go wrong.

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.

As a cybersecurity guy, it’s things like this study, which said:

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.

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.

My objections:

1. It doesn’t adequately indicate “confidence”. It could return “foo” or “!foo” just as easily, and if that’s one term in a nested structure, you could spend hours chasing it.
2. So many hallucinations-- inventing methods and fields from nowhere, even in an IDE where they’re tagged and searchable.

Instead of writing the code now, you end up having to review and debug it, which is more work IMO.

Lemmy is an outlier where anything “AI” immediately triggers the luddites to scream and rant (and occasionally send threats over PMs…) that it is bad because it is “AI” and so forth. So… massive grain of salt.

Speaking as (for simplicity’s sake) a software engineer who wears both a coder and a manager hat?

“AI” is incredibly useful for charlie work. Back in the day you would hire an intern or entry level staff to write your unit tests and documentation and utility functions. But, for well over a decade now, documentation and even many unit tests can be auto-generated by scripts for vim or plugins for an IDE. They aren’t necessarily great but… the stuff that Fred in Accounting’s son wrote was pretty dogshit too.

What LLMs+RAG do is step that up a few notches. You still aren’t going to have them write the critical path code. But you can farm off a LOT more charlie work to the point where you just need to do the equivalent of review an MR that came from a plugin rather than a kid who thinks we don’t know he reeks of weed.

And… that is good and bad. Good in that it means smaller companies/teams are capable of much bigger projects. And bad because it means a lot fewer entry level jobs to teach people how to code.

So that is the manager/mentor perspective. Let’s dig a bit deeper on your example:

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.

Honestly? That sounds to me like foundational issues. You already articulated what you need but you wanted to find an all in one guide rather than googing “bash function input example” or “bash function return example” or “strip trailing strash from directory path linux” and so forth. Also, I am pretty sure I very regularly find a guide that covers every one of those questions except for string processing every time I forget the syntax to a for loop in bash and need to google it.

And THAT is the problem with relying on these tools. I know plenty of people who fundamentally can’t write documentation because their IDE has always generated (completely worthless) doxygen for them. And it sounds like you don’t know how to self-educate on how to solve a problem.

Which is why, generally speaking:

I still prefer to offload the charlie work to newbies because it helps them learn (and it lets me justify their paycheck). And usually what I do is tell them I want to “walk you through our SDLC. it is kind of annoying” to watch over their shoulder and make sure they CAN do this by hand. Then… whatever. I don’t care if they pass everything through whatever our IT/Cybersecurity departments deem legit.

Which… personally? I generally still prefer “dumb” scripts to generate the boilerplate for myself. And when I do ask chatgpt or a “local” setup: I ask general questions. I don’t paste our codebase in. I say “Hey chatgpt, give me an example of setting the number of replicas of a pod based upon specific metrics collected with prometheus”. And I adapt that. Partially to make sure I understand what we are adding to our codebase and mostly because I still don’t trust those companies with my codebase and prompts. Which… is probably going to mean moving away from VSCode within the next year (yay Copilot) but… yeah.

My workplace of 5 employees and 2 owners have embraced it as an additional tool.

We have Copilot inside Visual studio professional and it’s a great time saver. We have a lot of boiler plate code that it can learn from and why would i want to waste valuable time writing the same things over and over. If every list page follows the same pattern then it’s boring we are paid to solve problems not just write the same things.

We even have a tool powered by AI made by the owner which we can type commands and it will scaffold all our boiler plate. Or it can watch the project and if I update a model it will do the mutations and queries in c# set up the graphql layer and then implement some views in react typescript.

Lots of good comments here. I think there’s many reasons, but AI in general is being quite hated on. It’s sad to me - pre-GPT I literally researched how AI can be used to help people be more creative and support human workflows, but our pipelines around the AI are lacking right now. As for the hate, here’s a few perspectives:

• Training data is questionable/debatable ethics,
• Amateur programmers don’t build up the same “code muscle memory”,
• It’s being treated as a sole author (generate all of this code for me) instead of like a ping-pong pair programmer,
• The time saved writing code isn’t being used to review and test the code more carefully than it was before,
• The AI is being used for problem solving, where it’s not ideal, as opposed to code-from-spec where it’s much better,
• Non-Local AI is scraping your (often confidential) data,
• Environmental impact of the use of massive remote LLMs,
• Can be used (according to execs, anyways) to replace entry level developers,
• Devs can have too much faith in the output because they have weak code review skills compared to their code writing skills,
• New programmers can bypass their learning and get an unrealistic perspective of their understanding; this one is most egregious to me as a CS professor, where students and new programmers often think the final answer is what’s important and don’t see the skills they strengthen along the way to the answer.

I like coding with local LLMs and asking occasional questions to larger ones, but the code on larger code bases (with these small, local models) is often pretty non-sensical, but improves with the right approach. Provide it documented functions, examples of a strong and consistent code style, write your test cases in advance so you can verify the outputs, use it as an extension of IDE capabilities (like generating repetitive lines) rather than replacing your problem solving.

I think there is a lot of reasons to hate on it, but I think it’s because the reasons to use it effectively are still being figured out.

Some of my academic colleagues still hate IDEs because tab completion, fast compilers, in-line documentation, and automated code linting (to them) means you don’t really need to know anything or follow any good practices, your editor will do it all for you, so you should just use vim or notepad. It’ll take time to adopt and adapt.

I don’t think that the current approaches being used by generative AIs are sufficient to reliably produce correct code; I think that they’re more-amenable to human-consumable output (and even there, I’m much more enthusiastic about their use for images than text, as things stand). A human needs approximately-correct material to cue their brain; CPUs are more particular.

We’ll probably get there, in the same sense that we can ultimately produce human-level AI for anything, but I think that it’ll entail higher-level reasoning about a problem, which present generative text approaches don’t do.

I did start with internet search…I could not find how to pass values into the function and return from a function easily,

So, now, this I have a hard time with.

When I search for “pass value function bash”, this is the first page I get, which clearly shows an example:

https://stackoverflow.com/questions/6212219/passing-parameters-to-a-bash-function

This isn’t where I’d consider generative AI to be a useful example; it’s something that there will be existing material already readily-available via a search.

The other issue with using generative AI for coding is that for taking pre-existing code for common tasks and using it in multiple programs, we already have an approach: use libraries. That way code gets maintained and such, but doesn’t need to be reimplemented by humans over-and-over.

Say someone says “I need linked-list code”. Okay, I mean, that’s a pretty common, plain Jane thing to need.

But if you use a library, and there’s a bug in that code, and it gets fixed, then the bugfix propagates when you update to a newer library. If you generate a linked-list implementation, even if you wind up with working linked-list code at the end, then that isn’t gonna happen.

It doesn’t pass judgment. It just knows what “looks” correct. You need a trained person to discern that. It’s like describing symptoms to WebMD. If you had a junior doctor using WebMD, how comfortable would you be with their assessment?

It’s a tool just like everything else, but people are just now sobering up after all the hype that it’s incredibly wrong a lot.

A lot of people are very reactionary when it comes to LLMs and any of the other “AI” technologies.

For myself, I definitely roll my eyes at some of the “let’s shoehorn ‘AI’ into this!” marketing, and I definitely have reservations about some datasets stealing/profiting from user data, and part of me worries about the other knock-on effects of AI (e.g. recently it was found that some foraging books on Amazon were AI generated and, if followed, would’ve led to people being poisoned. That’s pretty fucking bad).

…but it can also be a great tool, too. My sister is blind, and honestly, AI-assisted screen readers will be a game changer. AI describing images online that haven’t been properly tagged for blind people (most of them, btw!) is huge too. This is a thing that is making my little sister’s life better in a massive way.

It’s been useful for me in terms of translation (Google translate is bad), in terms of making templates that take a lot of the tedious legwork out of programming, effortlessly clearing up some audio clarity issues for some voluntary voice acting “work” I’ve done for a huge game mod, and for quickly spotting programming or grammar mistakes that a human could easily miss.

I wish people could just have rational, adult discussions about AI tech without it just descending into some kind of almost religious shouting match.