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I wrote my own lightweight markup language for mathy blogs

Hacker News

I wrote my own lightweight markup language for mathy blogs

I wrote my own lightweight markup language, similar to Markdown, that compiles to both HTML5 and LaTeX, supports LaTeX equations (through svgtex, i.e. serverside MathJax), references, captioned figures with semantically correct <figure> tag in html, syntax highlighted code blocks, and so on. http://www.dllu.net/programming/dllup/ See the text file written in dllup markup language: http://www.dllu.net/programming/dllup/index.dllu and the PDF and LaTeX versions: http://www.dllu.net/programming/dllup/index.pdf http://www.dllu.net/programming/dllup/index_dllu.tex The code is very hacky (I wrote it for my own use after all) and might crash if the syntax is not exactly right. I also have experimental vim syntax highlighting support: http://i.imgur.com/imx6XCY.png The HTML output of dllup is valid HTML5 (http://validator.w3.org/check?uri=www.dllu.net/programming/dllup/&charset=(detect+automatically)&doctype=Inline&group=0) and should be sufficiently semantic to be usable in lynx: http://i.imgur.com/ugSXX90.png

Visit product View on Hacker NewsMar 12, 2015

Actual performance

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

82%82% 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 HackersFits the IH revenue-focused audience · Strong signals: supports, latex · Missing: reddit linkedin, podcasting, created

69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: code · Missing: mac, agents, macos

31%31% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

27%27% 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

22%22% 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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