HNRelevant – Explore Related HN Discussions in an Integrated Sidebar

Hacker News

I've noticed that whenever I'm on HN, I feel intrigued and want to explore more discussions. I needed an easier workflow to explore related submissions instead of googling and going back and forth. 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. I made this browser extension that adds a section for related submissions automatically. The results are displayed in a sidebar right in the page's layout using HN native style just like other elements. I paid special attentions to UX to make it intuitive and seamless and now the extension is just part of the HN experience for me. Implementation-wise, it integrates HN algolia search 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. I shared it earlier last year when it was barely a prototype, at the time wasn't yet published and was only for chrome (hence the update): 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 24, 2024

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

79%79% 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

59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: users · 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 · 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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