mo

mononames – Over 850k brandable one-word domains at your fingertips

Hacker News

mononames – Over 850k brandable one-word domains at your fingertips

Hello HN, I'm often looking for short domain names for my various projects, and I thought it would be a good idea to publish my lists and make them available to others. At the time of writing, mononames offers more than 850,000 one-word domain names available in over 40 TLDs. The lists are in downloadable spreadsheet format, with grammatical classes and sentiments. Updated lists are sent by e-mail every week. An extract from one of the lists is freely available on the mononames website, along with a "surprise" for the .ing TLD. If you have any questions or comments, don't hesitate to reply to this thread. And if you have any ideas for additional TLDs, don't hesitate!

Visit product View on Hacker NewsApr 23, 2024

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Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released

72%72% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · 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

34%34% 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

13%13% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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