[znpy]

I had similar experiences:

1. Aasked ChatGPT to write a simple echo server in C but with this twist: use io_uring rather than the classic sendmsg/recvmsg. The code it spat out wouldn't compile, let alone work. It was wrong on many points. It was clearly pieces of who-knows-what cut and pasted together. However after having banged my head on the docs for a while I could clearly determine from which sources the code io_uring code segments were coming. The code barely made any sense and it was completely incorrect both syntactically and semantically.

2. Asked another LLM to write an AWS IAM policy according to some specifications. It hallucinated and used predicates that do not exist at all. I mean, I could have done it myself if I just could have made predicates up.

> But for anything even mildly complex, LLMs are still not suited.

Agreed, and I'm not sure we are any close to them being.

[mattgreenrocks]

Yep. LLMs don’t really reason about code, which turns out to not be a problem for a lot of programming nowadays. I think devs don’t even realize that the substrate they build on requires this sort of reasoning.

This is probably why there’s such a divide when you try to talk about software dev online. One camp believes that it boils down to duct taping as many ready made components together all in pursuit of impact and business value. Another wants to really understand all the moving parts to ensure it doesn’t fall apart.

[typedef_struct]

My test is to take a sized chunk of memory containing a TrueType/OpenType font and output a map of glyphs to curves. Bot is nowhere close.