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Htmldocs – Typeset and generate pdfs with HTML/CSS

Hacker News

Htmldocs – Typeset and generate pdfs with HTML/CSS

htmldocs is an Overleaf-style editor for typesetting documents using HTML/CSS, which provides the same benefits as LaTeX while being more accessible, customizable, and familiar. I built this because I wanted to programatically generate invoices as well as automatically tailor my resume to jobs but had no good way of generating well-formatted PDFs. I ended up building a templating engine to Chromium rendering pipeline to generate PDFs, and due to the amount of engineering effort, turned it into a tool for others that might want to do the same. There's a built-in API ( https://htmldocs.com/docs/documents ) that you can call to turn JSON into PDFs in a single call. htmldocs is different from other tools like Wkhtmltopdf and Weasyprint in that it uses Chromium to generate PDFs, meaning that it supports the most modern CSS features and there's minimal drift between the rendered HTML document and PDF. Will also consider open sourcing if there's enough interest in the project!

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Actual performance

226points
89comments
Made the leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: ide, pipe · 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
81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: single, using, open · Missing: mac, agents, macos
55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
51%51% predicted probability of success on AppSumo, 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.

Correct prediction on native model

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