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Twinwords - fast two word domain name generator

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Twinwords - fast two word domain name generator

For a hobby project, I made a web domain name generator. It's always bugged me they aren't smarter. It algorithmically figures out similar words by calculating pair similarities for all the two word domain names in the .com zone file (about 60 million.) Because of the way it works, the 'synonyms' it generates are more like how people use the words in domain names instead of a thesaurus, and it works for lots of things that aren't dictionary words. (ex: http://twinwords.com/?cmd=suggest&first=reddit&second=html ) Tech buzzwords used if you have any questions: nodejs, C, mongo, bloomfilter, jaccard similarity. Try it out and tell me what you think! How it works & some examples: http://twinwords.com/about.html

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Actual performance

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Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
64%64% 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.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
39%39% 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
33%33% 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
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · 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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