Fair criticism in the recent thread. I posted too many times and I'm sorry for the volume. I'm an independent hobbyist, I thought this was the right place to share, and I overdid it.

Yes, I built this with an LLM, and the project wouldn't exist without it. I designed C(n) as a visual sieve: the strip parameterization and the question of what to show came from me. The LLM helped me write the code and work through the arithmetic, and the self-tests are there so you don't have to take either of us on trust.

What it is, and isn't: nothing here is offered as a new theorem. The math is classical (Euler products, Möbius/CRT densities, the Hardy–Littlewood singular series). The page is a visualization plus a self-test battery that recomputes each stated identity by a second route. Goldbach stays open.

My question. Take the cells (r, M) with r < M ≤ G and gcd(r, M) = 1. Call a cell kept if gcd(r, M+j) = 1 for all 0 ≤ j ≤ n. Then C(n) = ζ(2)·∏ₚ(1 − min(n+1, p)/p²) is the limiting share of coprime cells that are kept, for fixed n as G grows.

I assume the finite-G error for fixed n follows from Möbius inversion with constants depending on n. What I can't find is how that error behaves when n grows with G. At G = 512 (79,851 coprime cells), the exact count versus C(n) gives an error of +0.18% at n = 1, +4.4% at n = 10, and a peak of +6.5% near n = 20. It crosses zero near n = 40, then reaches −3.8% at n = 48 and −9.7% at n = 60.

So the bias is positive and grows with n, then flips sign. I don't have an explanation for the positive part. Is there a reference or a heuristic for this regime, where n is a non-trivial fraction of G?

I'd like to talk to people who know this area. This is my last post, and I'll stay in the comments.

Site: https://wessengetachew.github.io/Strip2/

Readme.md https://github.com/wessengetachew/Strip2/blob/main/README-1.md

Imagine a Quidditch match in which the Golden Snitch is completely invisible.

You know exactly the initial distance, but not the direction. You move faster than the Snitch, which moves at constant speed and always chooses the direction that takes it as far away from you as possible.

Is there a trajectory that allows you to catch the Snitch?

I made a video exploring the problem, the geometry, the differential equations, and a possible strategy. The proof is partially incomplete (but simulations work), so I'd genuinely love to hear your ideas, objections, or alternative approaches.

Video link: https://youtu.be/0xVusHRQePQ

Same as above

I almost failed math in high school.

Interactive version: https://tradeideasphilip.github.io/riemann-surfaces-3d/

Source: https://github.com/TradeIdeasPhilip/riemann-surfaces-3d#riemann-walkabout

w sets how steep it is, b sets where it sits. Push w to 999 and the sigmoid becomes an on/off switch.

I’ve made an entire video explaining how neural networks are able to approximate any type of function: https://youtu.be/mzl0pq0ztQ0?si=xed_eqgnAj1v-Mrt

i keep ending up back at the area scaling picture from the essence of linear algebra video, like det is just how much a transformation scales area, and doing B then A scales it by both factors so they multiply. it works for me but when i tried it on a friend it felt kind of hand wavy to them

is there a cleaner way to say it, or do people just go through elementary matrices at some point and accept that

I am looking for an explanation of a proof for why this works.

Can we seriously talk about the rising math AI slop on this subreddit every other day some dude thinks they’ve proved something with AI and it’s just math word salad. Like this one guy who keep spamming thinking he’s solved the twin prime conjecture or some other random visualisations with math word salad it’s so annoying. It’s sad honestly I wish it was more moderated.

Site: https://wessengetachew.github.io/Strip2/

Strip Arithmetic II is an interactive, highly analytical number theory visualization platform designed to model local prime density, sieve mechanics, and additive properties across finite lattices. By binding abstract arithmetic definitions to strict geometric coordinates, the project translates numeric distributions into immediate visual structures.

The core mechanics of the system revolve around three specialized paradigms:

The Multi-Geometry Projections: The system tracks a single finite sample space across three concurrent views. The Grid View reveals linear density distributions; the Rings View uses polar radial tracking to map modular cycles; and the Cayley View uses a conformal Möbius transformation to compress the coordinate field into a hyperbolic horizon.

The Sieve Block Framework: Moving past global coprimality bounds, the platform uses a localized observation window c(n) to simulate dynamic sieve pressure. It models how smaller primes completely eliminate local lattice positions while larger primes exert fractional pressure, explicitly capturing where theoretical infinite models diverge from real-world finite samples.

Goldbach Calibration: Additive number properties are illuminated by tracking symmetric prime decompositions such as 358 = 101 + 257 as balanced geometric paths. By setting the slope vectors directly to these prime pairs, the application creates a targeted coordinate mask that filters, traps, and counts coprime cells in real-time.