PlusWhois – a visual WHOIS tool that adds context

Hacker News

I built PlusWhois because I was frustrated with how raw and hard to read most whois tools are. They give you plain json data. Try it here - https://pluswhois.com/whois/ycombinator.com I build this whois site to make it more useful by: - structuring domain registry DATE into human readable format - make links clickable, show if a site is live, logos etc - adding basic dns and hosting info - pulled some metadata to show social links, tech stacks whenever possible. - quick toggle and you can also see a basic DNS info. No need for an account - just type a domain and get a fast overview instantly.

Visit product View on Hacker NewsApr 30, 2025

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Actual performance

1points

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Launch Intel predictions

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

70%70% predicted probability of success on AppSumo, 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

68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

45%45% 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 HuntUnlikely to reach the leaderboard · Strong signals: context, visual, plain · Missing: mac, agents, macos

34%34% 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

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

15%15% 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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