> We deal mostly with source code, so the text we fold is overwhelmingly ASCII and making it run at memory speed is the single most important thing we can do. Everything else just has to keep the rare non-ASCII path from spoiling it.

Semi-on-topic: I've noticed that many LLMs via coding agents (ChatGPT and Claude at work with my CoPilot account, and DeepSeek 4 and ChatGPT in pi.dev at home) really seem to like using unicode / emoji characters for things like arrows (for things like test value ranges), crosses and ticks (for pass vs fail in test comments), instead of plain ASCII. Codebases are almost exclusively ASCII chars to my knowledge, although they're UTF-8 files.

I'm not yet using agents to write code (only do code reviews, write example prototypes I then copy bits of, and helping craft tests), but I'm likely to get there soon, and I'm sure it's possible to prompt them NOT to do this, but has anyone else noticed this? I wonder if that changes things over time for them if this is a common theme of increased non-ASCII output?

Perhaps there was heavy weighting of swift code [1] ;)

I would just paste the example, but HN code block display appears to think it's as unreasonable as I do.

[1] https://wolfmcnally.com/121/programming-with-fruit-using-emo...

A lot of good repos (CLIs, frameworks) had 'tree' unicode directory structure with like ├──, └──, and │ , as well as emojis for passing/failed tests and README docs maybe a unicode arrow or two, but LLMs absolutely overuse it.

I don't know why chatbots prefer → over -> so much. It's becoming a countersignal compared to the old terminal customization era, where arrow ligatures were a signal of effort.

Codebases written by native English speakers are almost exclusively ASCII, but codebases written by speakers of languages other than English frequently have non-ASCII content, even if only in the comments, but languages which support it often wind up with non-ASCII identifiers, too.

I do not believe that emoji like crosses and ticks are particularly common at all, for any language, but LLMs seem to have picked up heavy use of them from somewhere and inserted them into code (and everything else) they generate.

LLM training sets will very likely include the massive corpos of non-English open source code from sites like Gitee, but would be unlikely to generate responses heavily influenced by them unless you've done specific things to make that happen - prompt in Chinese, try to make use of a library only available with Chinese source and/or documentation, perhaps. I've not seen it happen, but I am a light user of LLMs.

Using emoji for status indicators on the console is a trend that pre-dates LLMs. First mainstream app I can recall doing it was Yarn.