Glintflow – Commenting system with Medium-like claps for blogs/articles

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Hey HN, I'd like to share a project that I've been working on to try out Shadcn/ui: GlintFlow commenting system It's an early stage commenting system that has claps (like in Medium) instead of likes/dislikes, and people can give up to 50 claps. It's built with NextJS (the usual suspect) and Shadcn. Right now it's extremely simple, you can give claps (on article/page and comments) and add comments as guest (no login required). Planning to add more features like spam protection, user login to post, moderation, dashboard, analytics etc. if there's interest. I hope you find GlintFlow as engaging and useful as I do and would love to get feedback on it! You can check it out at https://glintflow.com , just scroll down to find a demo instance of it. ## Motivation ## Firstly, I wanted to try out shadcn on a simple-enough project. I've mainly been using Mantine.js which have been great, and looks great, but honestly Shadcn looks even more amazing; both have very functional components. But looks aside, I wanted to see how much ease of development I'd be trading off for it - quite a bit, but not enough to drop shadcn. Secondly, I personally the claps to be more fun/engaging than the plain like/dislike buttons but haven't really seen any third-party embeddable commenting system. So built something to see if anyone would find it useful. If you're interested in trying embedding an instance for yourself, ping me and I'll set something up for you. Not planning to charge anything atm and will try to keep it so permanently for early adopters as long as their usage is not ridiculous.

Visit product View on Hacker NewsDec 20, 2024

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

78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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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.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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.

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

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

14%14% 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.

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