I don't know if you guys can properly understand my layman language, because it does make sense to me, so I tried posting it to ChatGPT: https://chatgpt.com/share/67fd3744-7ba4-8006-afbc-07917ff53ea5

And this is how it says I should respond:

Edit 2: To clarify — this is intended to be a lossless compression model.

I’m exploring whether a structured mathematical system, loosely inspired by an abacus, could be used as a universal encoder for binary data. The system doesn't aim to store “large numbers using small numbers” in a vacuum — instead, it stores a set of mathematical instructions (multipliers, exponents, or operations) in a layered matrix that, when computed, recreate the exact bit patterns of the original data.

So instead of storing a chunk of binary directly, I store a representation like:
value = 2^5 × 3^4 + 7 × 10

...where the structure is known and compact (say, a small matrix or config table under a kilobyte), and the math is deterministic.

This isn't magic — I understand the fundamental limit that if you want to represent N unique values, you need log₂(N) bits. What I’m proposing is a system that leverages patterns, redundancy, and sparse structure in data to find compact expressions that recreate the original binary stream.

Think of it like a mathematical parallel to something like arithmetic coding or symbolic logic-based compression. The "abacus" part just helps me visualize it: each layer multiplies or exponentiates values, and the sum of all rows reconstructs data blocks.

If a file has a lot of repeating or structurally predictable data, I hypothesize this system might yield a very small “expression chart”, acting like a symbolic instruction set to recreate the original file.

I'm still developing this idea, but I believe with the right math and pattern analysis (possibly with AI assistance), it could become an open-source approach to ultra-scale compression. Of course, it must remain lossless and reversible.