The tools have a learning curve. You can get extreme amounts of value out of them but you have to learn how to use them. Many tools are this way.

It might make sense to learn now, before everyone expects that you be an expert with this tool in order to do your job. On the other hand perhaps they will be easier to learn if you wait.

There is an incredible amount of nuance to be had in using LLMs and we reached the stage, probably only in the last few weeks where all the pieces have fallen into place such that if you are skilled at using the LLMs just about all of the previous problems with them can now be easily mitigated. If you learn how. There is a lot to learn.

Sadly there seems to be no point in arguing online though.

- You need to have a mental model to grow an idea

- When a pattern is known you can use a library!

- When a pattern is not known AI will make stuff up

No matter how "good" coding with an AI will become there is no way you get around learning, having someone understand what's going on and take responsibility.

I'm fine with formal checking and fuzzing or getting hints where a bug might come from - but these are iterations on tools we used before like a debugger, strict types, functional programming and well fuzzing.

I tried to have an AI iterate on a step i don't fully understand yet and i always regretted it. Highly interconnected projects will grind to a halt until you do learn.

You can check with people doing highly specced software in the medical domain that even with very good specs there is ambiguity in natural language thus it can never be the source of reliable and manageable software.

However - i read about some smart guys in the 40s or so encountering the need for a formal language to tell a computer what to do with 0 ambiguity and i think they called it a "programming language"

Right I feel like they just got to a point where they can reliably write individual functions with direction. Every time I've tried to use them consistently I feel like I just end up spinning wheels from them doing things that mostly work until it crashes and burns or just has straight up missing things or half truths. Skill issue or not it doesn't work for me.

Still glad I basically never have to debug a build issue or memory error again though

If the Code is bad, you can tell it to fix it to match your expectations —only at the edges, are you going to have to spell it out.

Lack of documentation used to be a running joke.

The goose_spec.txt in 2019 would be "goose is like c and rust only with syntax like array{;}[] btw it uses a small amount of stacks instead of malloc."0 10klocs.

Now, we make sure the AI leaves documentation so it can figure out what it was doing earlier.

A local model a few generations and many weights behind newest Claude can implement my novel algorithms and write the documentation I wouldn't.

Yes, many times it does dumb CS for kindergarten mistakes but it only has to work towards the optimum every once in a while.

That's like me with violins!

it's definitely true in real life. i think there is actually a massive skill gap in using them effectively.

I can see that you only work with true scotsmen, but the machine really does write most of the code now, at least from a pure numbers perspective.