HNRelevant – Add a "related" section to Hacker News

Hacker News

It's been 2 years since the initial release [here]( https://news.ycombinator.com/item?id=36102610 ). The initial version was a very basic prototype which was not available anywhere beyond the GitHub repo. You had to install it as a userscript or load the extension manually. Since then, it remained simple but better, new features include: - Improved accuracy by also using comments to help gauge the topic of discussion and the right keywords. - Published as plugin for more browsers: Chrome, Firefox (including android), and more recently Microsoft Edge. - Support for narrow screens and mobile devices. - Added preference controls. You're here because you love interesting HN discussions but they're often buried away like hidden gems, so give it a try and let me know what you think.

Visit product View on Hacker NewsMay 24, 2025

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122points

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

64%64% predicted probability of success on Indie Hackers, 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: hacker news, io, including · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

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

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

AppSumoMay struggle as an AppSumo deal · Strong signals: users · 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: users, way · Missing: mobile apps, ios, personal

31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

14%14% 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.

Correct prediction on native model

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