Sh

Ship Name AI Tool

Hacker News

Ship Name AI Tool

Hey HN! I just launched ShipName – a bilingual (English + Chinese) “ship-name” generator that blends two names into a catchy couple tag in one click. What it does • Takes two names and a style (cute / cool / meme / wedding) • Generates 8 short blended names, in English, Chinese or mixed • One-click download of a share card (PNG) for X, Discord, Weibo • Bilingual UI + Pinyin support – no more garbled Chinese outputs Stack Next.js 14 + Vercel Edge Functions phi-3-mini-128k via OpenRouter for fallback creativity Satori/Resvg for server-side card rendering Cloudflare R2 for blob storage, Upstash Redis KV for edge caching PostHog for analytics Why build it? Fans, couples and wedding planners keep googling “ship name generator”, but most tools break on non-Latin characters or hide behind paywalls. I wanted a zero-friction, bilingual version that also spits out share-ready images. Roadmap (next 4 weeks) • Trending ships widget (scrapes Twitter/X hot pairs) • Anonymous favourites (localStorage) → optional login sync • Public REST API + Discord bot Source isn’t open yet, but I’m happy to share parts if that helps. Would love feedback on: * Output quality – any weird or cringy blends? * Performance on low-end mobile (FCP/LCP) * Other features you’d like before calling it “good enough” Demo: https://shipname.net/ Thanks for reading!

Visit product View on Hacker NewsJun 30, 2025

Actual performance

2points

Did not reach leaderboard

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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.

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

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: open · Missing: mac, agents, macos

33%33% 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

31%31% 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

27%27% 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

16%16% 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.

Correct prediction on native model

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