Any paper.
Fully understood.
Drop in a research paper. Ela finds every concept it expects you to know and builds a course to fill the gaps. Visual, interactive, and built for that paper specifically.
Research papers do not stay in one field. FlashAttention assumes you know GPU memory architecture. AlphaFold 2 assumes protein chemistry, multiple sequence alignment, and transformer internals. None of it is explained in the paper. It is just expected.
So reading a paper you care about takes weeks of detours through lectures, textbooks, and blog posts, before you even reach the ideas you came for.
The tools people reach for today either tell you what a paper says or explain concepts in the abstract. Neither gets you to the point where you can think about the research.
They tell you what the paper says. They do not help you understand the concepts it is built on unless explicitly asked for it. Even then, we gfaced they lack structure that would help the reader get intuition and effectively learn.
Can't lie, you can find some excellent resources on YouTube and Coursera. These sites can help you learn the basics and then take you all the way to advanced levels. But, they usually don't cover topics that mix different fields unless those topics are popular. We aim to address this.
Ela reads the actual paper, finds every concept it assumes you know, and builds a course around exactly those gaps. With diagrams and interactive modules built for this paper.
Paste an arXiv link or upload a PDF. Ela reads the full text, figures, and appendices.
It finds every concept the paper expects you to know, traces their dependencies, and identifies what is missing from your background.
A sequence of visual modules from first principles to the paper's ideas. Nothing generic. Nothing you do not need.
We propose FlashAttention, an IO-aware exact attention algorithm that uses tiling to reduce the number of memory reads/writes between GPU high bandwidth memory (HBM) and on-chip SRAM. We prove that FlashAttention requires O(N²d/M) HBM reads/writes versus Θ(Nd + N²) for standard attention, where N is sequence length, d is head dimension, and M is SRAM size.
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