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Name Capy – Find a domain name you're happy with

Hacker News

Name Capy – Find a domain name you're happy with

Names should be short and memorable. But naming things is, hard. Finding a good name? Tough. Getting one with a domain? Well ... It feels down right impossible. Especially, if you have trouble naming variables like me. Hi hackers, I'm Dian. This all started about a month ago, when I finally started my own company! It's been long overdue, so I filed the paperworks and got a domain. But. When my friend asked me about it at Starbucks, I had to lower my head, sip some matcha, and try to hide the embarrassment of a name. That's when I knew, I done goofed. As it turns out, I'm not alone in my misery, and naming is a skill. Steven Paul Jobs, yup, Steve Jobs, wanted to call the iMac ... the 'MacMan'. Issy Sharp wanted to create the 'Thunderbird Inn'. But that was taken. So he named it Four Seasons. And Phil Knight, at the very last minute, before his shoes goes into production, decided to call it Nike. Oh, what was the name he himself came up with? Dimension Six. Imagine. Running in a pair of Dimension Six. Working on a MacMan. Or vacationing at a Thunderbird. Yuck. So why are people bad at naming? I wanted to know how other people came up with names. Then I came across PG's essay https://paulgraham.com/name.html Two things: lack of imagination, and identity (I think its the endowment effect). And that gave me an idea, to build a tool just for naming. Name Capy. Find a domain name you're happy with. It's completely free! Well, until my OpenAI credits run out lol. You can try it out here: https://namecapy.com/ And yes, I used it to name itself. It combined the service I'm providing, 'naming', and my love of 'capybaras'. Let me know what you think.

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
90%90% 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: ide, io · Missing: https docs, excited, just released
63%63% 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 HuntOn track for Day 1 leaderboard · Strong signals: mac, new, openai · Missing: agents, macos, agent
50%50% 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
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
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.

Incorrect prediction on native model

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