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TeX Open Source · Awesome GitHub Repositories

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Explore 3 awesome TeX GitHub repositories. AI-ranked by relevance — refine with filters, or browse the highest-voted projects in the community.

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  • lib-pku/libpku

    lib-pku/libpku

    33,313View on GitHub↗

    This project is an open-source textbook and academic resource repository designed to support collaborative learning and scholarly research. It functions as a digital platform for organizing and distributing instructional content, allowing students and researchers to contribute to a shared knowledge base. The repository utilizes a typesetting pipeline that transforms structured markup into professional-grade academic documents. By leveraging a distributed version control system, the project maintains a complete history of revisions and facilitates collaborative contributions from multiple auth

    TeX
    33,313View on GitHub↗
  • posquit0/Awesome-CV

    posquit0/Awesome-CV

    26,774View on GitHub↗

    Awesome-CV is a specialized LaTeX document class designed for the creation of professional résumés and cover letters. It functions as a static document generator that transforms structured, plain-text source files into high-quality, print-ready portable document format files. The project utilizes a modular, macro-driven layout engine that separates raw content from visual presentation. By employing a declarative markup abstraction, it allows users to manage career data independently of the final document styling, ensuring consistent formatting across various professional and academic material

    TeXawesomecoverlettercv
    26,774View on GitHub↗
  • HarisIqbal88/PlotNeuralNet

    HarisIqbal88/PlotNeuralNet

    24,431View on GitHub↗

    PlotNeuralNet is a programmatic tool designed to generate high-quality visual representations of neural network architectures. It functions as a declarative visualization framework that converts structural definitions into professional-grade graphical output, specifically tailored for technical documentation and academic research papers. The project distinguishes itself by utilizing a layer-centric procedural modeling approach, which applies standardized geometric templates to network components to ensure consistent visual styling. By leveraging a domain-specific macro language and a LaTeX-ba

    TeXdeep-neural-networkslatex
    24,431View on GitHub↗