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StackOverflow for Research Papers

Hacker News

StackOverflow for Research Papers

Hi there, Rafael here, creator of gotit.pub, a project that I've been working on for the last six months. Gotit.pub is an online platform where anyone can discuss research papers, as a layer directly on top of the paper itself. I built it in large part to scratch my own itch and I couldn't find any other place on the web that's doing this. As part of my regular job I read plenty of research papers (Vision, Graphics, NeRFs, ML etc..) and often I find myself puzzled after studying. I'm sure the authors or community could answer questions easily but where to start? Open a ticket on Github? Write an email to the authors? Ask on SO? None of these are great because the context of the paper is lost and it's (ab)using the platforms for tasks they were not designed to do. I should mention that I have no PhD or ever worked in a research lab. Maybe (likely?) there's already an obvious solution to the discussion/feedback problem that I am not seeing. In that case I'm still happy to have built it because I know it'll be useful for me, personally :) I hope though that many of you will find it useful too (let me have your feedback in the comments!). Cheers! PS: You can actually discuss any research paper, not just ones on arXiv.org! PPS: For those interested, the whole site is built with Svelte / SvelteKit and a lot of PDF.js hackery. More bells and whistles to come, it's just MVP stage at this point. The quirky name is from another unrelated side-project of mine which builds on top of git but so far hasn't seen the light of day. I couldn't think of a good name for this one so I just adopted the name. Still fits, I think!

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72%72% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
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47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
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0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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