I don’t really understand what values such design pattern can bring worth (that will outweigh its own complexities)
my experience working with DAGs programmatically (ie not as an abstraction (like in React) but actually handling the edges&nodes of a graph-based abstraction in code) is that it looks nice theoretically but in practice top-down (conceptual) approach like this often tends to over-complicate things
would love to see some real-world examples where an actual dev team, etc, find values in graph-theortical-based abstractions like this
Have a look at the node code. It's biblically simple. I would say it's much simpler than any comparable solution. You can forget about reducers, thunks, computed, even useState, useContext, useEffect and props are rare. I've stuck with graph terminology: nodes, predecessors etc, but you don't need to grok that stuff to use this. Once you've understood that a node is a value, some methods and a reup() function, that's it.
You don't need to define every possible state. A state machine can serve as a higher-level structure of some of the states (statuses, modes) of parts of your app, for ease of structuring logic. But you can also create a single-state state machine and represent non-finite data as "external state".
I'm not doing this for fun. Especially since I'm fundamentally lazy (and more of a practitioner than a comp-sci person). I'm looking at custom configurators for complex made-to-measure products with dozens of options, with many interdependencies between options.
my experience working with DAGs programmatically (ie not as an abstraction (like in React) but actually handling the edges&nodes of a graph-based abstraction in code) is that it looks nice theoretically but in practice top-down (conceptual) approach like this often tends to over-complicate things
would love to see some real-world examples where an actual dev team, etc, find values in graph-theortical-based abstractions like this