Thank you! I'm a bit wary of giving big hints to how the filters work, but I was inspired by:

- npm packages like redact-pii, bad-words (and numerous other "bad word" lists)

- askismet's open source spam code

- regex unicode categories

I also wrote a variety of checks for word counts, combinations of words, known "bad patterns", etc. You may not realize it, but the text input forms block a lot of potential inputs.

It's not perfect, but that's why I'm manually handling things that surface. Happy to answer any follow-ups.