most "AI motion" tools make you choose: let the AI generate it, or hand-edit it. your edits die on the next regen.
so i made edits into stable addresses. regenerate the base, they survive. every render byte-identical.
whole thing is one data file. 41s:
Spent the afternoon adding a live "liquid glass" backdrop to reframe, my declarative motion engine.
Different angle from the WebGL demos: you write the glass as data, and it renders deterministically to mp4 and runs live in the browser. Frosted-backdrop tier, not full 3D refraction
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The part that matters: it didn't overwrite my work. I got a before/after diff to accept or reject, and any tweak I'd made by hand survived the regeneration. That's the whole point.
Turn any paper into running code.
Just swap arxiv โ autoarxiv in the paper url.
That hands the paper to an AI agent from alphaXiv. It reads the abstract, the claims, and the linked GitHub repo, then clones the codebase and works through the usual setup pain like dependencies, broken paths, environment config, and hardware assumptions.
From there it designs a minimal reproduction. That means a smaller model, fewer steps, and a single GPU instead of a cluster, scaled down just enough to test whether the headline claim holds.
The whole run is live and fully logged. Loss curves, metrics, and training progress are all observable as it happens.
What comes back is a clean signal on whether the minimal run matches the paper's reported result, plus an estimate of what a full replication would cost in compute and time.
A lot of research code dies in setup before anyone verifies a single number. This moves reproduction from a weekend of debugging to a url change.
Pick a paper and try it now.
video credits: @askalphaxiv
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