I'm building an open source discussion forum for latest ArXiv papers

Hacker News

Hey HN! I built arXiv News ( https://arxiv-news.com ) – a discussion space for research papers. The problem: I browse arXiv constantly for new papers, but there's nowhere good to discuss them. Twitter is chaotic, Reddit is fragmented, and arXiv itself has no comments. So I cloned HN's design (using https://github.com/clintonwoo/hackernews-react-graphql ) and applied it to arXiv. Upvote papers, comment, sort by hot/new/discussed, filter by category, post ideas/papers you've found. A daily scraper pulls all new papers automatically. I'd love to hear peoples thoughts. Trying to iterate/improve upon what I currently have so I need feedback. https://github.com/peterdunson/arxiv-news Thanks!

Visit product View on Hacker NewsOct 21, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, using, 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.

Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released

76%76% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

30%30% 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

15%15% 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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