[wruza]

No one includes complete detail at saying it’s useful and life-changing too, so that’s fair. It might turn out that what works for those for whom it works is trivial “code” not worth the ssd blocks it occupies. This is actually my current theory, cause LLMs (all of them, yes we tried all of them) are capable of what I tend to not think about as programming but as industry nonsense which should have been automated/abstracted/libraried away ages ago.

Maybe show us the successful code it built and we’ll see what type it is, cause recording failures is only useful in hindsight. I have no logs of lenghty struggling with llm stupidity.

getting perfect code out of the LLM is a non-goal

It stops being a goal after just a few tries, naturally. The problem is usually not that it isn’t perfect, the problem is it doesn’t understand the problem at all and tends to some resembling mediocrity instead. You can’t just fix it and move on.

[handzhiev]

"No one includes complete detail at saying it’s useful and life-changing too"

There are at least 3 posts in this very discussion sharing details and githup repos with code written mostly by LLM.

[wruza]

I see a weather.com class, a parser and a react gui boilerplate boilerplate tsx folder. All three are textbook and trivial areas. We are only missing an ad hoc crud orm here.

In my opinion that is not code, it’s not a business logic. What is presented is (not to offend anyone, I look at code not people) useless github-code carcasses that it contains in abundance. Real code solves problems, this code solves nothing, it just exists. A parser, a ui, an http query - it’s a boilerplate boilerplate boilerplate. You aren’t coding at writing it. It’s “my arduino is blinking leds” level of programming.

I think that’s the difference in our perception. I won’t share my current code purely for technical reasons, but for an overview, it fuzzy-detects elements on virtual displays and plays simple games with dynamic objects on screen, behaving completely human input-wise, all based on a complex statistical schedule. It uses a stack of tech and ideas that llms fails at miserably. Llms are completely useless at anything in there, because there’s basically no boilerplate and no “prior art”. I probably could offload around 15% to an llm, but through the pain of explaining what it’s supposed to assist with.

Maybe it’s me, but I think that most of the jobs that involve trivial things like “show fields with status” or “read/write a string format” are not programming jobs, but an artefact of a stupid industry that created them out of mud-level baseline it allowed to persist. These should have been removed long ago regardless of AI. People just had way too much money (for a while) to paycheck all that nonsense.

Edit: I mean not just removed, but replaced with instruments to free these jobs from existing. AI is an utterly sarcastic answer to this problem, as it automates and creates more of that absurdity rather than less.