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Mdxish – Write high-quality, interactive blog posts in Markdown, live

Hacker News

Mdxish – Write high-quality, interactive blog posts in Markdown, live

Hi HN! I wanted an easier way to write technical interactive blog posts. So I built `mdxish`! You can write in markdown, mixing classic markdown, html, and javascript together, and see the output html live as you author it. Writing it in this way creates a really nice medium for the source (easy to read), which is just markdown mixed with code blocks. Also, many IDEs have support for syntax highlighting and intellisense / autocomplete features inside these code blocks. And because `marked` (one of the only dependencies, but does near all of the heavy lifting) works client side, we can even render markdown directly to html on the client side. For example, I created a gist of my falling sand blog post, rewritten in mdxish format: https://gist.github.com/jasonjmcghee/cba64163600fa90a3ea5d2e... (also threw in the css it used as other files in that gist) And now you or I or anyone can just replace "gist.github" with "mdxish". https://mdxish.com/jasonjmcghee/cba64163600fa90a3ea5d2ea099f... And after a slight delay, you get the full rendered post- and the author only needs to upload to gist. `mdxish` itself can do this client approach, the live server, or just one-off mdxish flavored md to html. In the live mode, you can store state that persists across updates, like "time" (very useful for iterating on animations). Anyway, hope others find this interesting / useful. If you'd like to contribute, please let me know! Try it out and tell me what you think. I've created a repo for doing GitHub Actions, the idea being, you should be able to just create a repo of mdxish flavored markdown and it'll take care of making it a full site for you. But not quite done with that yet! Note: I also added a short video with a voice-over if you'd like to see it in action.

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Product HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos
85%85% 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: created · Missing: supports, reddit linkedin, podcasting
81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
56%56% 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
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video, way · Missing: mobile apps, ios, personal
20%20% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · 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.

Incorrect prediction on native model

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