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I made a tool for finding memorable, available domains

Hacker News

I made a tool for finding memorable, available domains

I was sick of all the noise on domain sites trying to upsell me to premium and auction domains for small dev projects, so I built a tool that shows only available, non-premium domains. It automatically scores and ranks options based on six metrics (spellability, pronounceability etc). By default, it suggests a wide range of "dot-something" domains, but you can restrict it to .com, .ai, .io. The tool also learns from your likes and dislikes, helping you hone in on names that match your taste. Would love feedback from others who have faced the same problem!

Visit product View on Hacker NewsApr 28, 2025

Actual performance

2points

5comments

Did not reach leaderboard

Launch Intel predictions

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AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

66%66% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

50%50% predicted probability of success on Indie Hackers, 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

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

nativeThis product was originally launched on this platform.

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

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

29%29% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

12%12% 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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