Embedded spaces inspired on gravity [P][D]

https://preview.redd.it/x164c3ol59kh1.png?width=895&format=png&auto=webp&s=96fe040f824799f2175e526a0512e81367e04b0f

tl;dr: I have a project that I think is really cool and as a non-researcher, I don't know what to do next and need advice.

Project Overview

[Me, my context]

For the past 1 year or so I have been working in this project by myself in my spare time, sometimes maybe overworking haha
I am a software developer by accident, but I really love to study CC, language, neuroscience, phisycs and biology, but I haven't had the oportinity to formally study any of that (life in Brazil is not exactly easy).
Anyhow, that is just that I don't have much oportunity to publish or even talk about my projects, I don't have people to share, to evaluate or guide me, and this project was no different, though, I had many AI's helping me throughout it, and it is not yet done, of course, but I don't really know what to do right now and I need advice.
Basically I don't have energy nor the competence to continue with the project alone, I can't publish papers, I am afraid sharing the mess the project is right now and burning away any good idea hidden there.

[The project]

Mixing all these areas of interest I have, I had the realization that "maybe the next token is exactly the right one". In a sense, information organizes itself, that is why we have things like the " Jennifer Aniston Neuron" (https://en.wikipedia.org/wiki/Grandmother_cell), this is the part of the brain that optimizes the retrieval of information about the concept of " Jennifer Aniston" .

Another project I have try to treat gravity as an emergent property of an informational universe (similar to what Vopson is doing https://wikitia.com/wiki/Melvin_Vopson ). Assuming information organize itself, if we knew the mechanism of that, we would be able to create an embedding space without backpropagation.

The idea really ressambles what w2v have done, actually, there is nothing really new code or concept-wise, what I think my idea bring something new is how the pieces are put together.

I really like the way it solves MNIST and how the model can be incrementally trained without much forgeting.

What should I do next? Just forget about it?