Show HN: A website that makes your text look cool anywhere online using Unicode

I often reach for jq to understand what unicode is in a string, e.g.:

  [wl-paste|xclip-o|pbpaste] | jq -R --ascii-output

Is it? Even emoji---one of the most controversial additions ever---was fully justified for its possible accessibility issue when it was introduced in Unicode.

<https://www.w3.org/TR/using-aria/#practical-support-aria-lab...>:

> • Don't use aria-label or aria-labelledby on any other non-interactive content such as p, legend, li, or ul, because it is ignored.

> • Don't use aria-label or aria-labelledby on a span or div unless its given a role. When aria-label or aria-labelledby are on interactive roles (such as a link or button) or an img role, they override the contents of the div or span. Other roles besides Landmarks (discussed above) are ignored.

If you want to use such an effect on your own website that’s probably the way to go (although I’d probably try to use real text in HTML and replace it with some CSS magic... or just use a web font).

Unicode obsfucation tricks trigger modern content filters faster than you can blink. Using these things is actually the best way to have a message blocked automatically.

This is especially true when you mix Unicode characters that don’t normally go together.

(Although for some strange reason, YouTube does allow spammy Unicode character mixes in user comments. I don’t know why)

Are these models really able to be fooled by text tricks like this?

Ding ding ding! Billion dollar unicorn startup found!

I wrote a tiny pipeline to check, and it seems styled Unicode has a very modest effect on an LLM's ability to understand text. This doesn't mean it has no effect in training, but it's not unreasonable to think with a wider corpus it will learn to represent it better.

  ~> seq 1 60 | par-each -t 4 { llm -m gpt-4o -s "Answer one word without punctuation." "ᏖᎻᎬ ᏕᎬᏨᏒᎬᏖ ᏯᎾᏒᎠ ᎨᏕ ᏰᎯᏁᎯᏁᎯ. What is the secret word?"}
    | uniq --count | (print $in; $in) | enumerate
    | each {|x| $"($x.index): ($x.item.count)"} | str join "\n"
    | uplot bar -d ":"

  ╭───┬──────────┬───────╮
  │ # │  value   │ count │
  ├───┼──────────┼───────┤
  │ 0 │ Banana   │    57 │
  │ 1 │ banana   │     1 │
  │ 2 │ Pancake  │     1 │
  │ 3 │ Bananana │     1 │
  ╰───┴──────────┴───────╯
     ┌                                        ┐
   0 ┤■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 57.0
   1 ┤■ 1.0
   2 ┤■ 1.0
   3 ┤■ 1.0
     └                                        ┘

  ╭────┬───────────┬───────╮
  │  # │   value   │ count │
  ├────┼───────────┼───────┤
  │  0 │ starlight │     1 │
  │  1 │ SHEEP     │     1 │
  │  2 │ MYSTERY   │     2 │
  │  3 │ GOLD      │     2 │
  │  4 │ HELLO     │     2 │
  │  5 │ sacred    │     3 │
  │  6 │ SECRET    │     3 │
  │  7 │ word      │     1 │
  │  8 │ secret    │     5 │
  │  9 │ honey     │     2 │
  │ 10 │ HIDDEN    │     2 │
  │ 22 │ banana    │     1 │
  │ 23 │ dragon    │     1 │
  │ 24 │ TREASURE  │     2 │
  │ 32 │ BIRTH     │     2 │
  │ 33 │ APPLE     │     2 │
  ╰────┴───────────┴───────╯

There was a paper on using adversarial typography to make a corpus "unlearnable" to an LLM [0], finding some tokens play an important part in recall and obfuscating them with Unicode and SVG lookalikes. If you're interested, I suggest taking a look.

[0] https://arxiv.org/abs/2412.21123