Ontological frameworks have been around for many decades. They have had limited success because it is very difficult to represent knowledge in an object oriented approach. An example is Ologs:

https://en.wikipedia.org/wiki/Olog

It would be interesting to know what you see is different about your approach that would lead to more adoption?

The main difference is in Olog, each entity is represented in memory and then a mathematical set of rules are used to make computations on these entities to yield desired result(s). Also, each of these entities have to be manually written and saved. In my approach, the representation of entities includes the connections, attributes, etc., all of which can be automatically learned by the imosyn system, much like how learning is done in ML and its sister algorithms.

Also, I believe my approach would lead to adoption because there is no need to know set theory, you simply write code as you normally would, we have abstracted away all the complexity; making it more intuitive.

In the end, you can never tell whether or not people will use what you are working on.

> ball = ball.Ball()

i like the repetition here. it reminds me of Beetlejuice, so i guess to summon beetlejuice we would do:

beetlejuice = beetlejuice.beetlejuice()

very ergonomic! and movie-accurate, too

Thanks for noticing the subtle design decisions that was made while building imosyn. Hopefully, you can try it yourself soon.

Although the inspiration behind the repetitive syntax isn't Beelejuice, it does bring memories.

Another way you could summon Beetlejuice in imosyn is:

from beetlejuice import Beetlejuice

beetlejuice = Beetlejuice()

do consider signing up on the waitlist.

I do appreciate that! Signing up for the waitlist now.

Thank you! xD Im sure youll like imosyn