It’s not in the thread line I’m replying to, to get to that I would have had to read another reply, and all of the replies to that to spot yours.

If the work you do can be fully specified in a Jira ticket, you’re a code monkey and not a software engineer, of course you can use LLMs to do your job since you can be replaced by an LLM.

And it’s not true that agents can’t help with edge cases, they can. If you know which points to look at, you task to analyze the specific interaction and watch which parts of the code would be mentioned.

You’re missing my point entirely, it’s not that it can’t help with, it’s that the solution it writes will not take them into account unless you tell it to, and to explain every edge case in enough details to be unambiguous about all of them is essentially the same as writing code directly. Not to mention that you can’t possibly know all of the edge cases of the solution it will write without seeing it, so you can’t directly tell it to watch for edge cases without knowing what code it will write.

I do write way less amount of symbols to LLM than I would when I write code.

Maybe, but then you have to review everything it wrote so you waste more time. Give me one concrete example of something that you can prompt an LLM to give you code that is advanced enough to be worth it (i.e. writing the prompt and reviewing the code it wrote would be faster than writing the code myself) and not generic enough that I would be able to find the answer in stack overflow.

Those symbols don’t have to be structured

If you don’t structure them the LLM might misinterpret what you meant. Structure in a language is required to make things unambiguous, this reminds me of the stupid joke of “go to the store and bring 1L of milk, if they have eggs bring 6” and the programmer coming back with 6L of milk because they had eggs. Of course that’s a stupid example, but anything complex enough to be worth using an LLM would be hard to describe unambiguously and covering all edge cases in normal human speak.

and they can even have typos, so I can focus my brain activity on things that actually matter.

Typos are very easy to correct, most editors will highlight them for you, and some can even autocorrect them but more likely you avoid most of them by using tab completion anyways. I don’t waste any brain activity on that, I’m thinking on the solution and structuring it in an unambiguous way, that is what writing code is, it’s not some cryptic art of writing the proper runes to make the machine do your will like you seem to be implying, it’s just structured thought.

Plus, copilot is shit.

Might be, wouldn’t know any other as that’s the one I have available to use, but sincerely I doubt others are that much better to make a difference.

I rate your post as a skill issue.

Yup, I have absolutely no skill in using LLMs, nor will I waste my time with it. Don’t get me wrong, it’s a neat tool for auto completing small snippets like we used to do with an actual snippet library a couple of years ago, it is also a decent tool to navigate unknown code bases asking it where certain parts are or how to achieve something in the. I would say that 60% of the time it gives you some good pointers, but 90% of the time most of the code it writes is wrong, but at least it points you in the right direction of where to start investigating.

I don’t expect you to understand this since from what I’m reading here you probably never worked on anything big enough, but a software engineer job is not to write code, that’s just a side-effect, our job is to solve problems, so either you’re trying to get the LLM to solve the problem for you, or wasting lots of time explaining your solution in English, reading the generated code, understanding it, analyzing it, fixing any issues and testing it, possibly multiple times instead of explaining your solution once in code and testing it.