> “Suppose […] they type straße and you’ve stored STRASSE. To make these count as matches, you need […]”
Really bad example, because as the article says later on, this casefold crate won’t match those two strings because the ß → ss conversion isn’t done.
> “[str::to_lowercase and case folding] diverge on real characters—ß, İ, final sigma”
The main point is true (case folding is different from lowercasing), but two of the three examples are wrong. The casefold operation that they use maps ß to itself, as does str::to_lowercase. The casefold operation maps İ to U+0069 U+0307 regardless of locale, as does str::to_lowercase.
When I’m reading an article, these kind of mistakes in the introduction make me doubt the accuracy of the whole article. Which is a shame, because again, it’s an interesting write-up. The mistakes also make the article harder to follow, since the examples imply ß is folded to ss.
What I should have said is that in the case of İ/i/I/ı, using str::to_lowercase for string matching wouldn’t be any less correct than using their locale-independent casefold.
The third character in the list, final sigma, is a good example that illustrates why using str::to_lowercase for string matching isn’t good.
Or if you really want to do it, your case-folding needs to map certain lowercase characters to to other lowercase characters (lowercase ß to ss, lowercase ı to i, etc), losing some of the meaning
s/case-folding/lowercasing/
Proper Unicode case-folding absolutely does map ß to ss, ς to σ, etc. Moreover, there are some scripts (IIRC Georgian) where for historical reasons case-folding yields uppercase letters, not lowercase ones. The case-folding mapping is specifically designed in concert with the comparison rules to yield the same result, that’s why it’s a separate operation from lowercasing.
(I believe the thing described in TFA is supposed to be proper case-folding in that sense, but given TFA is AI-written I wouldn’t trust its descriptions either way.)
That said, if you want to match the sort order customary in a specific language, you need to use language-specific rules for producing collation keys rather than generic case-folding. There’s no way out of this because different users of e.g. the Latin alphabet want contradictory results. And if you think you do want generic casefolding, then you probably actually want NFKC_Casefold instead unless your input is pre-normalized.
> The hot operation isn’t really “fold this character,” it’s “does this character fold?” Almost always no.
Really that's distracting. If you must use LLMs, also do a rewrite pass that removes most LLMisms
Are we doomed to spend the rest of our professional and personal lives reading AI output?
> This is genuinely interesting
Are you sure you're not an LLM yourself?
> 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.
This isn't the case anyway. Unicode case-folding has a few lowercase-to-uppercase mappings, e.g. Cherokee
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.
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.
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...
I was talking with a junior at the office today about LLM output and mentioned em dashes, to which responded “oh, I thought that was just where formatting for hyphens was going, I guess I learned something from the AI writing instead of the other way around” and god, his acceptance of it was just deprrsssing.