Mk

MkDocs in NextJS/Tailwind/Shadcn

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MkDocs in NextJS/Tailwind/Shadcn

Hey everyone. I wanted to share a sideproject I was working on for a while now: A drop-in replacement for mkdocs-material in nextjs. I had that in the back of my head for about 2 years now and finally pulled the trigger earlier this year with a crude prototype. I somehow kept going at it and magicly it reached a state where I feel (almost? - but will it ever be?) comfortable to share it to others as well (by far not feature-complete though). A bit about myself: I am not considering myself a nextjs pro, I just like the ecosystem somehow and grew into it by trial and error over the years. I have a large site built in mkdocs-material. I really like the style of mkdocs-material and how easy it is to start with something decently looking, however I always wanted to add more dynamic elements like member areas etc, which is a pain doing through the static website. With nextjs it isn't much of a problem. I came in late to Nextjs and I can barely keep up with the releases, but somehow, with a bit of help from aider and some common sense, I got something working I feel is ok. Of course, if you are a nextjs pro and feel like I'm doing it all wrong, please let me know. Help, criticism, PRs, issues, comments welcome, but keep in mind, its a side project. In general, I always felt code + content should be a separate thing. I love how easy it is to get started with mkdocs-material and I hope I can provide a similar experience. Certainly, the intention is to have something that gets up and running in minutes. Something hackable. Something fun. And something that looks good out of the box. Currently its nextjs 14, I failed miserably porting it to nextjs 15 in an afternoon :) but ok, we'll get there. I genuinely hope its not just xkcd 927 applied to docs, at least I haven't found a drop in replacement yet. Even if it goes nowhere, I learned a ton about remark, rehype and MDAST... Anyways, if you have any questions or comments, let me know.

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Product HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos
88%88% 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.
Indie HackersFits the IH revenue-focused audience · Strong signals: started, para · Missing: supports, reddit linkedin, podcasting
72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way, para · Missing: mobile apps, ios, personal
38%38% 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
36%36% 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
17%17% 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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