Technology

https://www.techrepublic.com/article/ai-generated-code-outages/

I use it as a time-saving device. The hardest part is spotting when it's not actually saving you time, but costing you time in back-and-forth over some little bug. I'm often better off fixing it myself when it gets stuck.

I find it's just like having another developer to bounce ideas off. I don't want it to produce 10k lines of code at a time, I want it to be digestible so I can tell if it's correct.

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.

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.

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.

Keep in mind that at the core of an LLM is it being a probability autocompletion mechanism using the vast training data is was fed. A fine tuned coding LLM would have data more in line to suit an output of coding solutions. So when you ask for generation of code for very specific purposes, it's much more likely to find a mesh of matches that will work well most of the time. Be more generic in your request, and you could get all sorts of things, some that even look good at first glance but have flaws that will break them. The LLM doesn't understand the code it gives you, nor can it reason if it will function.

Think of an analogy where you Googled a coding question and took the first twenty hits, and merged all the results together to give an answer. An LLM does a better job that this, but the idea is similar. If the data it was trained on was flawed from the beginning, such as what some of the hits you might find on Reddit or Stack Overflow, how can it possibly give you perfect results every time? The analogy is also why a much narrow query for coding may work more often - if you Google a niche question you will find more accurate, or at least more relevant results than if you just try a general search and past together anything that looks close.

Basically, if you can help the LLM hone in its probabilities on the better data from the start, you're more likely to get what may be good code.

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.

[NB: I'm no programmer. I can write some few lines of bash because Linux, I'm just relaying what I've read. I do use those bots but for something else - translation aid.]

The reasons that I've seen programmers complaining about LLM chatbots are:

1. concerns that AI will make human programmers obsolete
2. concerns that AI will reduce the market for human programmers
3. concerns about the copyright of the AI output
4. concerns about code quality (e.g. it assumes libraries and functions out of thin air)
5. concerns about the environmental impact of AI

In my opinion the first one is babble, the third one is complicated, but the other three are sensible.