rssc – RSS (atom and JSON too) customizer

Hacker News

I've used https://hnrss.github.io/ (HN rss) for a while, which provided some customization over queries; however, it was limited to OR predicates only. Sometimes I wished to have more extensible RSS preferences; some of the feed readers I used supported such advanced queries, but the majority didn't. Using this approach, customization is possible on any atom, RSS, or JSON feed using regex expressions to filter feed entities based on title, description, content, and authors. Examples are provided on the article link.

Visit product View on Hacker NewsAug 14, 2023

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Actual performance

4points

Did not reach leaderboard

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

59%59% 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

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: using · Missing: mac, agents, macos

40%40% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

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

31%31% 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

13%13% 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

6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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