I think Markov chains have transition probabilities, which this model is lacking. But it's the same idea, just with uniform transition probabilitis.

It seems like it has transition probabilities.

Depending on the type of the prior word, it randomly selects the next word from a list of compatible word types.

Am I misunderstanding?

It only has probability 0 or 1/n, where n is the number of compatible next words.

There are no numbers in https://github.com/adamjgrant/Tiny-Predictive-Text/blob/main...

A Markov chain could express probabilities like completing "the original" to -> "poster" (0.1), -> "McCoy" (0.2), -> "and best" (0.7) which I don't think this does. But I am tired and maybe also misunderstanding.

Is a Markov chain where every state has an equal probability not a Markov chain?

Kind of splitting hairs here I guess, but I genuinely don’t know.