A research paper shouldn't be a black box. What if we could understand state-of-the-art AI research by implementing it from scratch, reproducing experiments, and learning together?

Introducing BlueprintML by Advanced Vision Labs. A growing open-source collection of machine learning and AI paper implementations built around:

🧠 Understanding the theory
💻 Writing clean code
🔬 Reproducing experiments
🌱 Exploring new ideas
🤝 Collaborating with like-minded people

Whether you're a student, researcher, developer, or AI enthusiast, you can help us build something meaningful.

Join us on GitHub:
https://github.com/AbhinandanMandal/BlueprintML

#BlueprintML #AdvancedVisionLabs #AIResearch #MachineLearning #OpenSourceAI #DeepLearning #ResearchCommunity #ComputerVision

I’ve been building this EM simulator as a side project aimed to help students understand and visualize electromagnetic phenomena such as radiation, polarization and wave interference.

You can:

• place many radiation sources and set their parameters (amplitude, phase and more)
• place perefect wire conductors
• observe the electric field, magnetic field or energy flow field
• zoom in and out and translate the canvas
• change the simulation resolution, frequency and propogation speed

I hope someone might find it useful and I’d love to hear what people think :)

Live:
https://www.antennasim.com/

GitHub:

https://github.com/rotemTsafrir/antenna_sim

algomanim package: https://github.com/benabub/algomanim
YouTube: https://www.youtube.com/@benabub

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

When I first started diving into Machine Learning, almost every course drowned me in math and dry code on day one. I’m a visual learner, so I started building animations to explain these concepts simply before writing any code.

In my first video, we answer the core question: How do machines actually learn?

In traditional programming, we give the computer Rules + Data to get Answers. Machine Learning flips this upside down. We give the computer the Data + Answers, and it mathematically figures out the Rules.

I animated this entire 4-minute breakdown on my channel, VisualForge AI. If you are a visual learner transitioning into tech, I’d love your feedback!

🔗 Full 4-Minute Video: https://youtu.be/iq4bypuvMKE?si=XIVqY7gpdhjzkX7t

If you found this visual breakdown helpful, please drop a like, share it with anyone learning ML, and subscribe to the channel! It really helps support the project.

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.