Upvote RSS – Generate rich RSS feeds from aggregation websites like HN

Hacker News

Upvote RSS is a self-hosted project I've been working on that generates rich RSS feeds from social aggregation websites like Hacker News, Reddit, Lemmy, and more. I prefer to do as much reading and browsing of these communities as I can in my RSS reader; with that in mind, Upvote RSS filters to only the top posts (with filters you specify) and generates rich feeds with article content, post media (videos, images, galleries), AI summaries, and post comments. Where this differs from something like hnrss.org: - You can choose between filtering posts by score, an average score threshold, or a specified number of posts per day (the filter I find most useful). - The RSS feed contains the text the post author provided, optional linked article content, AI summaries from your provider of choice, and a specified number of top comments from the community post. Everything in your RSS reader. - Self-host your own instance, or use the public one. - Supports subreddits, Hacker News, Lemmy communities, Mbin, PieFed, Lobsters, and trending new GitHub repositories. - Estimated reading time, score, and permalinks to the original post. - NSFW filtering/blurring (Reddit only) and custom Reddit domain. Here's the GitHub link if you'd like to give it a spin: https://github.com/johnwarne/upvote-rss

Visit product View on Hacker NewsSep 23, 2025

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

69%69% 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 · Strong signals: supports · Missing: reddit linkedin, podcasting, created

68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos

61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

40%40% 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

17%17% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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