Markdown HN profiles at {user}.at.hn

Hacker News

Very opportunistic toy project as I saw the domain was up for grabs: 'at.hn' is a little site where people can have their own subdomains for whatever their HN username is (opt-in only by adding a slug to your bio). It doesn't really do much. Just shows your HN bio rendered as markdown plus meta stuff. I'm thinking of adding an aggregated user listing on the homepage so people can explore profiles. There's a bunch of interesting people on HN but discoverability is a bit longwinded. I'm wondering what other features people want. Otherwise shall likely leave it as-is. I remember hnbadges was a thing for a while, but can't remember what happened to it. Did people like that? Anyway, at.hn's on github if people want to contribute. - https://github.com/padolsey/at.hn

Visit product View on Hacker NewsJun 10, 2024

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

81%81% 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.

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

67%67% 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: plus · Missing: platform, intuitive, reviews

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

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

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

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

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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.

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