I use Elixir in production. So, it's already a pretty unique language with very low marketshare in the first place and not many developers are attracted to it because of that and the opportunities. And rarely, I do take on some existing codebases that would smell of code rot from vibe coding. Elixir is one of those languages where it's actually hard to make something look complex. But, vibe coders somehow manage to pull it off. There are usually all these modules all over the place - running Genservers instead of just using something simplified, lot of Javascript like patterns all over the place.

And so, I dug deeper, I tried Claude, GPT-5 and Gemini. While each have their own merit, they all seem to be flawed when it comes to keeping things simple. Having said that, there are a lot of times I'm stuck in these codebases and the AI knows exactly what's happening if you give it the right context (within VS Code). So, definitely you can just setup a small shop to "De-vibe" coding with AI. Ironically.

▲

rmoriz 9 hours ago | parent [-]

Have you instructed the agent to produce „simple“ solutions when possible?

▲

neya 6 hours ago | parent [-]

Yes, the quality of output varies greatly depending on the model. Mostly they will hallucinate. For example, today I ran into an issue with file uploads. It suggested me `async_consume_uploaded_entries` which doesn't even exist.

	▲	rmoriz an hour ago \| parent [-]
		I've made different experiences with Rovo Dev CLI (Sonnet 4). I think LLMs are designed to over-engineer because the learning data, typical open source projects, are over-engineered as well by design (they should provide universal services not tied to a single use case). I had to learn the hard way, since I steer the prompt to 1. limit external dependencies 2. don't introduce abstractions when not needed 3. focus on the main use path it works quite well. Since I also have tests and CI in place, hallucinations are caught very early. IMHO it's mandatory to be productive with the current state of coding agents.