That seems as likely as anything, but that just moves the problem to counting, doesn't it? The model gets the duration, but I was under the impression LLMs struggled with the arithmetic that would make that useful.
Idk about Qwen 2.5 Coder that this is based on, but modern models don't struggle with arithmetic. I see Qwen 3.8 do long-hand math in thinking traces (ie. it outputs 17+35+12=64 in the thinking trace) so I would imagine this is something they train into the model specifically. Sometimes it creates and runs a simple python script to check its work.
It's still strange to me that an ability to perform arithmetic (and not just memorize a giant table of results) could emerge from training on language.
In principle a large enough neural network can provably approximate any continuous function. Since arithmetic can be represented with a concrete grammar, it's not surprising to me that an LLM could contain an abstract representation if trained correctly.
It would be nice to freeze the terminal preview after it generates the text for when you want to flip between color modes and color vision settings. Otherwise it gets annoying to wait for each one to complete the animation.
Time is an input too. If nothing is pressed, the model gets a WAIT token with a duration instead of a key, which is how snake keeps moving on its own.