Part of the LLM-assisted work here is that Fable+Sol put together a test suite of over 400 cases that are bare shell scripts plus a harness that runs Kitty and Ghostty GUIs and captures their full pty stream AND screenshots (multiple, for animations). For the pty stream we assert byte equality. So Ghostty's success and error messages and field order of responses (its k=v) directly matches Kitty byte for byte. For screenshots, we can't do pixel-identical comparisons because the way Kitty and Ghostty calculate grids and do alpha blending doesn't match. But, LLMs are pretty good now at "do they look the same" PLUS I went through all the screenshots myself in the end. Super helpful AI assist. It allowed me to focus a lot of my brain energy and time on reading the spec, reading/writing the implementation, reviewing a lot of code, considering the right shape of things, the performance implications, etc. while I had a couple very good interns in the background doing work like this. I plan on open sourcing this validation set and harness, with the full disclaimer that it is 100% AI written. But, I think its a perfect example of something that SHOULD be. The way I had Fable+Sol work together here: 1. Sol put together the harness. 2. Fable + Sol (two separate agents) in parallel would write test cases and output them on disk in their own folders. These two are just in a ralph loop. 3. Sol + Fable (reversed) with an adversarial prompt would judge the others work by picking up the changes on disk that step 2 wrote. They would determine how accurate/worthwhile it is to keep. They'd put it in another folder. 4. Sol finally woke up for changes to this final folder and would determine if its a dup or not and then add it to the final repo. Then I'd pick up the bug reports, validate them myself, and either fix them myself or kick off new agents manually, just Codex app or Claude app. Finally, re-ran both agents once against the full test suite to verify what I saw myself: everything passed, all images look the same.