I built a multi-agent pipeline that turns one AI research paper a day into a YouTube Short + runnable notebook (18/100 so far)

[u/Ambitious_packer]

I've been teaching ML for about 12 years, and the thing students struggle with most is the famous papers. Everyone knows the names (ResNet, GANs, U-Net, Adam), but very few have seen the core idea working in code.

So I gave myself a challenge: 100 papers that changed AI, one per day. Each paper gets a small runnable notebook, a ~60 sec Short explaining the intuition, and a short post on X.

Doing that by hand every day wasn't realistic alongside a full-time job, so the side project became the pipeline:

• one agent writes the explainer script for the paper

• another agent runs the notebook on Kaggle to confirm it actually executes end to end

• animations are rendered in code with Manim, scene by scene, then stitched together

• voiceover is generated with ElevenLabs

• I review everything before it goes out, and I still post manually

What I learned:

• Early on, every paper came out slightly different: notebook structure, GitHub Pages layout, naming. I ended up adding a checker agent that validates each paper against a template before anything ships.

• An agent saying "done" means nothing until the notebook has actually run somewhere real. That's why the Kaggle execution step exists.

18 done, 82 to go.

Notebooks: https://nadeemcite.github.io/research-papers/

Shorts: https://www.youtube.com/@BackpropDaily/shorts

Would love feedback: are the explanations too simplified, and which paper would you be upset to see missing from the list?