Next.js GitHub Markdown Blog

Hacker News

This project allows you to easily add a nice looking, mobile-friendly blog to your existing NextJS app without a lot of heavy dependencies or complex stuff to configure. You just set up a repo on GitHub with some markdown files with some metadata at the top, and it will automatically retrieve and convert the markdown to rich html/css blog posts that works well with SEO. I made it for my own webapp because I wanted something simple and easy and didn't want to deal with a complex library/system that does tons of stuff I don't care about.

Visit product View on Hacker NewsNov 12, 2024

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

76%76% 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: friendly · Missing: plus, platform, intuitive

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing · Missing: https docs, excited, just released

51%51% 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

35%35% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · 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

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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