monohacks – 170k+ available domain hacks

Hacker News

Hello! A few months ago, I shared with you the launch of mononames, a service that provides access to lists of one-word domains available from various top-level domains. TL;DR of the previous post: it's not easy to find brandable one-word domains, and mononames was created to simplify this process. Today, I'm excited to announce a special new addition to mononames: monohacks. monohacks is an additional list specialising in domain hacks. Examples of domain hacks? ‘instagr.am’, ‘itun.es’... A few more details: - Stand out from the crowd with this extensive new list - Based on English words + others for diverse options - Over 440 TLDs covered - Alphabetically sorted in a spreadsheet format for easy sorting by word, domain name, or TLD - Receive weekly updates straight to your inbox - Included with other mononames lists This new list is designed to provide even more value and options for finding the perfect domain name. If you have any questions, comments, or ideas for additional TLDs, feel free to reply to this post!

Visit product View on Hacker NewsMay 23, 2024

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

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

best fitHighest predicted score across all platforms for this description.

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

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

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

58%58% 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: new, inbox · Missing: mac, agents, macos

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

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

38%38% 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

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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