That said, the edge alignment is, I believe, caused by the fact that LLMs are involved in the process. Because the LLMs never "see" the final visual representation that humans see. Their "view" of the world is text-based, and in the text file, those columns line up because they have the same number of UTF-8 codepoints in the row. So the LLMs do not realize that the right edges are misaligned visually. (And since the workflow described is for an LLM to take that text file as input and produce an output in React/Vue/Svelte/whatever, the visual alignment of the text file needs to stay LLM-oriented for it to work properly. I assume, of course, since I haven't tried this myself).
The number of codepoints never did correspond exactly to the number of fixed-width blocks a character should take up (U+00E9 é is the same as U+0065 e plus U+0301 COMBINING ACUTE ACCENT, so it should be rendered in a single block but it might be one or two codepoints depending on whether the text was composed or decomposed before reaching the rendering engine). But with emojis in play, the number of possibilities jumps dramatically, and it's no longer sufficient to just count base characters and ignore diacritics: you have to actually compute the renderings (or pre-calculate them in a good lookup table, which IIRC is what Ghostty does) of all those valid emoji combinations.
P.S. The Hacker News comments stripped out those emojis; fair enough. They were, in order:
- a US flag emoji (made up of two codepoints) - a heart-on-fire symbol (two distinct symbols combined into a single image, made up of four codepoints total) - a woman and a man with a heart between them (three distinct symbols combined into a single image, made up of six codepoints total)