DotComBuddy – Generate and check domains with AI in one go

Hacker News

I tend to use AI to brainstorm domain names, but I kept wasting time pasting each idea into Namecheap to see if they were available. So I built DotComBuddy - it generates ideas (using DeepSeek V3 - switched from GPT-4o after realizing quality was just as good) and checks availability in real time via Name.com’s API. Built with Next.js and Vercel’s AI SDK, the app gives you a table of available domains with their price. 10 free searches to try it out, then $9/year (what I’d pay to never do this manually again). Sharing this for anyone else tired of switching tabs to check domains. Happy to answer any questions. Thanks!

Visit product View on Hacker NewsJan 27, 2025

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

77%77% 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.

Product HuntOn track for Day 1 leaderboard · Strong signals: using · Missing: mac, agents, macos

65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

40%40% predicted probability of success on AppSumo, 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

40%40% 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

23%23% 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 · Strong signals: real time · 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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