Would love your thoughts on Open Paper Digest. It’s a mobile feed that let’s you “doomscroll” through summaries of popular papers that were published recently.
Backstory There’s a combination of factors lead me to build this:
1. Quality of content social media apps has decreased, but I still notice that it is harder than ever for me to stay away from these apps. 2. I’ve been saying for a while now that I should start reading papers to keep up with what’s going on in AI-world.
Initially, I set out to build something solely for point 2. This version was more search-focussed, and focussed on simplifying the whole text of a paper, not summarizing. Still, I wasn’t using it. After yet another 30 min doomscroll on a bus last month, point 1 came into the picture and I changed how Open Paper Digest worked. That’s what you can see today!
How it works It is checking Huggingface Trending Papers and the large research labs daily to find papers to add to the index. The PDFs gets converted to markdown using Mistral OCR, this is then given to Gemini 2.5 to create a 5 minute summary.
I notice that I am now going to the site daily, so that’s a good sign. I’m curious what you all think, and what feedback you might have.
Cheers, Arthur
pentaphobe•2mo ago
If there was one of these for non-AI papers I'd easily lose hours each day
Totally off topic, but come to think of it, I'd love to see more feeds support anti-bubbling (show me _less_ of what I've frequently consumed)
davailan•2mo ago
What topics are you interested in?
Two different directions I'm thinking of for Open Paper Digest:
- either some recommendation algorithm that figures out which topics you are interested in and serves you papers based on that. Would need a good way to get signals though. That's why I'm now bootstrapping the process with Huggingface Trending Papers, but that immediately constrains the topics.
- or more search driven, where you type "I'd like to read about X" and it starts your feed
With regards to anti-bubbling: interesting thought, a "reverse" recommendation algorithm...
pentaphobe•2mo ago
That's just it - any list I give would probably miss the mark. I guess it all ties back to computational thinking in some way? (physics, neuroscience, rendering algorithms, medicine, linguistics, category theory)
Perhaps if recommendation algorithms could be that generalised it would scratch most of the desire for a good anti-bubble..
But still misses that special sauce of discovering papers/topics I didn't know I was interested in.
Libraries and stumbling into random university lectures did this very well (or newsagents, video shops, etc..) -- broadening rather than narrowing
LLMs / vector space seem well placed to automate this kind of expansive/lateral matching -- but it does seem we (or marketers) tend to build recommenders around the assumption that individuals' interests are a singularity to zero in on.. (and so likely train our models for same)
Anyway - end rant - thanks again, really cool project! Clearly got me inspired :)
stym06•2mo ago
for filters, create a set of pre-defined tags and let the LLM choose one of your pre-defined tags from the paper's summary.