You're completely correct, that's fair criticism. The excitement made me skip the basics. Here's a quick breakdown:
What it does: It's a new optimization algorithm that finds exceptionally good solutions to the MAX-CUT problem (and others) very quickly.
What is MAX-CUT: It's a classic NP-hard problem where you split a graph's nodes into two groups to maximize the number of edges between the groups. It's fundamental in computer science and has applications in circuit design, statistical physics, and machine learning.
How it works (The "Grav" part): It treats parameters like particles in a gravitational field. The "loss" creates an attractive force, but I've added a quantum potential that creates a repulsive force, preventing collapse into local minima. The adaptive engine balances these forces dynamically.
Comparison: The script in the post beats the 0.878... approximation guarantee of the famous Goemans-Williamson algorithm on small, dense graphs. It's not just another gradient optimizer; it's designed for complex, noisy landscapes where Adam and others plateau.
I've updated the README with a "Technical Background" section. Thanks for the push—it's much better now.
Clippy only helped with very specific products, and was compensating for really odd UI/UX design decisions.
LLM's are a product that want to data collect and get trained by a huge amount of inputs, with upvotes and downvotes to calibrate their quality of output, with the hope that they will eventually become good enough to replace the very people they trained them.
The best part is, we're conditioned to treat those products as if they are forces of nature. An inevitability that, like a tornado, is approaching us. As if they're not the byproduct of humans.
If we consider that, then we the users get the shorter end of the stick, and we only keep moving forward with it because we've been sold to the idea that whatever lies at the peak is a net positive for everyone.
That, or we just don't care about the end result. Both are bad in their own way.
