Ar

ArTok is TikTok for research papers

Hacker News

ArTok is TikTok for research papers

Hello everyone! I always found it hard to find new research papers outside of my usual bubble. I thought a random feed (with no recommendation algorithms) might be a fun way to explore. But I also didn’t want to waste time on completely unrelated stuff — so the idea of a fast, swipeable format came to mind. Wikitok was a real inspiration! But Arxiv and Open Review APIs weren’t as robust as Wikipedia, so I pulled the papers into a Postgres backend. Right now, I’ve indexed papers from a few recent ML conferences to see if this might be useful for others too. No signups required and it’s totally free. You can mark your favorites and add text annotations, which are saved on your device. Would love to hear your feedback!

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, apis, open · Missing: mac, agents, macos
81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
26%26% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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