Don't stop early: Case-folding source code at memory speed
39 points by sbulaev 5 days ago | 9 comments
claudetard 3 hours ago
This is good technical content, but it's obvious that an AI wrote it.
replyagency 2 hours ago
Agreed. This is genuinely interesting content, but there is no doubt in my mind that "The two operations diverge on real characters—ß, İ, final sigma—which is why lowercasing as a stand-in silently produces wrong matches." is LLM output.
replyAre we doomed to spend the rest of our professional and personal lives reading AI output?
inigyou 2 hours ago
TLDR: they implemented case folding with a lot more SIMD via autovectorization.
reply> almost every fold preserves the UTF-8 length or shrinks it, but two outliers grow—U+023A (Ⱥ) and U+023E (Ɀ) are 2 bytes each yet fold to 3-byte characters (ⱥ, ɀ)
Fix this by reversing it. Fold ⱥ to Ⱥ instead of the other way around. The search index won't only consist of lowercase characters any more, but that never mattered.
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?
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.