HNRelevant – Explore Related Discussions on HN in an Integrated Sidebar

Hacker News

Reading submissions here, I often feel intrigued and want to explore more interconnected or similar stories. The process of googling and going back and forth didn't feel natural. So, I made this browser extension that adds a related submissions section for HN's layout. The results are displayed in a sidebar right in the page's layout using HN native style just like other elements. I enjoy being knee-deep in discussions. Many times you're learning about something new, where the topic is either unfamiliar, thought-provoking, or just could use some context that you don't even know about. If a submission is interesting enough for me to click on, chances are I'd like to dig deeper into this topic and there are more related discussions that offer new context or perspective. This is an extension that I always have on so I paid special attentions to UX to make it intuitive and seamless and now the extension has become just part of the HN experience for me. Implementation-wise, it integrates https://hn.algolia.com/ API and uses the submission title as its initial query with the ability to change the query and other options interactively. It's available on: Chrome, Firefox, and as a userscript. Chrome: https://chromewebstore.google.com/detail/hnrelevant/iajhnkei ... Firefox: https://addons.mozilla.org/en-US/firefox/addon/hnrelevant/ I shared it earlier last year when it was early in development, barely a prototype, at the time it wasn't yet published and could only be used on chrome by "load unpacked": https://news.ycombinator.com/item?id=36102610 . I've been using it daily since and I'm happy to share it now for you to give it a try.

Visit product View on Hacker NewsJan 27, 2024

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

74%74% 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.

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

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: intuitive, users · Missing: plus, platform, reviews

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

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

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Strong signals: active · 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 · Strong signals: paid · 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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