> Probably the most damning fact about LLMs is just how poorly written their parent companies' systems are.

This seems like a popular take, but I think it's the other way around. Them dogfooding cc with cc is proof that it can work, and that "code quality" doesn't really matter in the end.

Before cc claude.ai (equivalent of chatgpt) was meh. They were behind in features, behind in users, behind in mindshare. cc took them from "weirdos who use AI for coding" to "wait, you're NOT using cc? you freak" in ~1 year. And cc is a very big part of them reaching 1-2B$ monthly revenue.

Yes, it's buggy. Yes, the code is a mess (as per the leak, etc). But they're also the most used coding harness. And, on a technical side, having had cc as early as they did, helped them immensely on having users, having real-usage data, real-usage signals and so on. They trained the models on that data, and trained the models in sync with the harness. And it shows, their models are consistently the highest ranked both on benchmarks and on "vibes" from coders. Had they not have that, they would have lacked that real-world data.

And if you look at the competition it's even more clear. Goog is kidna nowhere with their gemini-cli, is all over the place with their antigravity-ex-windsurf, and while having really good generalist models, the general mindshare is just not there for coding. Same for oAI. They have an open-source, rust-based, "solid" cli, they have solid models (esp in code review, planning, architecture, bug fixing, etc) but they are not #1. Claude is with their cc.

So yeah, I think it's really the other way around. Having a vibe-coded, buggy, bad code solution, but being the first to have it, the first to push it, and the first to keep iterating on it is really what sets them apart. Food for thought on the future, and where coding is headed.