Mk

MkSlides – Markdown to slides with a similar workflow to MkDocs

Hacker News

MkSlides – Markdown to slides with a similar workflow to MkDocs

As a teacher, we keep our slides as markdown files in git repos and want to build these automatically so they can be viewed online (or offline if needed). To achieve this, I have created MkSlides. This tool converts all markdown in a folder to slides generated with Reveal.js. The workflow is very similar to MkDocs. Install: `pip install mkslides` Building slides: `mkslides build` Live preview during editing: `mkslides serve` Comparison with other tools like marp, slidev, ...: - This tool is a single command and easy to integrate in CI/CD pipelines. - It only needs Python. - The workflow is also very similar to MkDocs, which makes it easy to combine the two in a single GitHub/GitLab repo. - Generates an index landing page for multiple slideshows in a folder which is really convenient if you have e.g. a slideshow per chapter. - It is lightweight. - Everything is IaC.

Visit product View on Hacker NewsNov 27, 2025

Actual performance

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

68%68% 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 HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting

47%47% 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, pipe · Missing: https docs, excited, just released

46%46% 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

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

34%34% 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

13%13% 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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