> What I am reading from you is that basically the only useful part of the ML paper is the abstract. If the repo/blog had a good abstract there would be no poin

> What I am reading from you is that basically the only useful part of the ML paper is the abstract. If the repo/blog had a good abstract there would be no point to the paper. The whole point of a blog post is vulgarization (which is definitely needed as well): explaining an idea without assuming that the reader has a lot of prior knowledge in the domain. Which goes in the opposite direction of what researchers look for: the most efficient way to understand what is being proposed. Most of ML papers abstract would be pretty horrible on a blog post in term of vulgarization. Plus, papers are all very well centralized in Arxiv. How am I supposed to find blog posts? I should just hope that what I am interested in will blowup on twitter or /r/machinelearning and I will notice it? This sounds extremely sub-optimal. > This sounds like a strawman, but yea I do think the code is the "actual" method while the math is some idealized cleaned-up translation. Do you also think that biologist should stop writing papers and just upload video of their actual experiments? I work in an ML research lab with 30 or so researchers, research never begins with some code snippets. It always begins on the whiteboard. Even between us, where we have the code and data readily available to share with each others, when someone presents it's work, they will never show code, but always equations or model diagrams. It's just an order of magnitude easier to visualize. Take this paper for example: https://arxiv.org/pdf/1805.11604v3.pdf https://arxiv.org/pdf/1805.11604v3.pdf how would you even begin to communicate something like that through code?