Built a Markdown to HTML Converter

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After two weeks of working on this project of building markdown to HTML converter/ compiler, It's finally done. It's called yamper (a python package). Supports most of the md elements like headings, ordered and unordered lists, code blocks, blockquotes, images and links as well as inline elements like bold, italic, strike-through and gfm emojis . Tables, nested lists, alerts etc. are not supported yet/ Will be working on them now. The package can be used in a different project or simply on command line. Just pass in the path of the md file like this: yamper '../path_to_md'. There is also support for choosing template (currently supports 3- standard-light, standard-dark and plain). The standard templates are pre-styles and code highlighted using prism[.]js cdn. Don't know how it'd be useful to you, but you can also generate tokens from the lexer for your md file. I struggled at building the lexer (actually at designing it) and posted on this r/Compilers. Someone pointed out to the commonmark spec, which made it designing much easier. After that, building the parser and renderer is quite straight forward. Skipped building the AST from the parser and instead directly converted it to HTML, though. For more detailed usage instructions and to explore the code, visit the GitHub repo: https://github.com/useDeep/yamper or just try for yourself using pip install yamper

Visit product View on Hacker NewsAug 14, 2024

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best fitHighest predicted score across all platforms for this description.

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

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47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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