Discorss – RSS Feeds for Discord

Hacker News

I made my own Discord bot for managing RSS feeds, called Discorss. Discorss is open source, has AI summarization, automatic paywall skips, and can be entirely managed with slash commands. Best of all – self hosting is very cheap and trivial with Railway, so you can add your own customizations to how the RSS bot works. I've been using a personal Discord server as my RSS hub – it's convenient and I can setup different channels for different newsfeeds (i.e. an HN channel for the frontpage). I was using https://monitorss.xyz – but it's barely had any changes, and needing to manage the external UI has been annoying. Website: https://discorss.fldr.zip/ Github: https://github.com/mergd/discorss

Visit product View on Hacker NewsApr 29, 2025

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

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs

58%58% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

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

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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

11%11% 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

8%8% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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