Culink – A social platform for curating and sharing link collections

Hacker News

Hi HN! I'm a solo developer from Korea, and I’ve been building Culink, a social platform for curating and sharing link collections — kind of like Pinterest for links (articles, tools, videos, resources, etc). Why I built this: I kept losing interesting links across scattered bookmarks. GitHub “awesome lists” are great, but they’re static — I wanted something more social, searchable, and collaborative. What you can do: - Create themed collections (AI tools, dev resources, startup guides, etc) - Collaborate and share collections with others - Follow curators you trust - Discover quality links through your network - Enjoy a simple, clean interface Tech stack: Next.js 15, NestJS, PostgreSQL, Azure Container Apps Current status: Just launched the English version on US servers, with example collections so you can explore right away. Try it: https://www.culink.io/discover Would love your feedback on: - Potential use cases I might be missing - Features you'd find useful - UX or onboarding improvements Thanks for checking it out — happy to answer any questions!

Visit product View on Hacker NewsOct 28, 2025

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

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

AppSumoStrong fit for a featured deal · Strong signals: platform, interface · Missing: plus, intuitive, reviews

58%58% 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

48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

27%27% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

26%26% 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

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: collaborate · 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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