> 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?
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.
Are we doomed to spend the rest of our professional and personal lives reading AI output?
TLDR: they implemented case folding with a lot more SIMD via autovectorization.
> 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.
> 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?
This is good technical content, but it's obvious that an AI wrote it.
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.
Are we doomed to spend the rest of our professional and personal lives reading AI output?
Yes.
> This is genuinely interesting
Are you sure you're not an LLM yourself?
I thought I wasn't, but you're making me second-guess myself.
TLDR: they implemented case folding with a lot more SIMD via autovectorization.
> 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.