ʕ☞ᴥ ☜ʔ Kix Panganiban's blog

Two things LLM coding agents are still bad at

I’ve been trying to slowly ease into using LLMs for coding help again lately (after quitting cold turkey), but something always feels off -- like we’re not quite on the same wavelength. Call it vibe coding or vibe engineering, but I think I’ve finally pinned down two big reasons why their approach to code feels so awkward.

  1. LLMs don’t copy-paste (or cut and paste) code. For instance, when you ask them to refactor a big file into smaller ones, they’ll "remember" a block or slice of code, use a delete tool on the old file, and then a write tool to spit out the extracted code from memory. There are no real cut or paste tools. Every tweak is just them emitting write commands from memory. This feels weird because, as humans, we lean on copy-paste all the time. It’s how we know the code we moved is exactly the same as where we copied it from. I've only seen Codex go against the grain here, sometimes I'd see it issue sed and awk to try and replicate that copy-paste interaction, but it doesn't always work.
  2. And it’s not just how they handle code movement -- their whole approach to problem-solving feels alien too. LLMs are terrible at asking questions. They just make a bunch of assumptions and brute-force something based on those guesses. Good human developers always pause to ask before making big changes or when they’re unsure (hence the mantra of "there are no bad questions"). But LLMs? They keep trying to make it work until they hit a wall -- and then they just keep banging their head against it. Sure, you can overengineer your prompt to try get them to ask more questions (Roo for example, does a decent job at this) -- but it's very likely still won't. Maybe the companies building these LLMs do their RF based on making writing code "faster".

These quirks are why I contest the idea that LLMs are replacing human devs -- they’re still more like weird, overconfident interns. I can’t fully vibe with them yet.

#ai #coding #musings