I built a site to help new dog owners pick a boy dog name fast

Hacker News

Hi HN, I built https://boydognames.net/ to solve a tiny but real moment: new dog owners stuck on choosing a boy dog name. *Why:* - I’m experimenting with “single-keyword micro sites” for SEO + fast product validation. - Friends kept asking for name ideas and I saw “boy dog names” trending. *What it does:* - 40k+ names grouped by style (classic, funny, Japanese, etc.), searchable and filterable. - AI-assisted suggestions based on your dog’s story/photo (coming next). - Users can vote/favorite, so the list evolves. *Tech stack:* - Next.js, Supabase (data), Replicate/OpenRouter (AI gen), simple server-side rendering for speed. - Deployed on Vercel. No heavy backend; mostly static + on-demand API. *Ask / Feedback:* - Is this too niche or just niche enough? - Any UX you’d improve (mobile first)? - Ideas to make the generator smarter without being “AI-flavored spam”? - SEO/content pitfalls you see? Happy to share traffic/SEO results as I go. Thanks for reading!

Visit product View on Hacker NewsJul 23, 2025

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

87%87% 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: user, new, single · Missing: mac, agents, macos

83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

41%41% predicted probability of success on TrustMRR, 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

25%25% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

23%23% 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.

Correct prediction on native model

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