Showing 8 open source projects for "text file viewer"

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  • 1
    ecode

    ecode

    Lightweight multi-platform code editor designed for modern hardware

    ecode is a lightweight multi-platform code editor designed for modern hardware with a focus on responsiveness and performance. It has been developed with the hardware-accelerated eepp GUI, which provides the core technology for the editor. The project comes as the first serious project using the eepp GUI, and it's currently being developed to improve the eepp GUI library as part of one of its main objectives.
    Downloads: 11 This Week
    Last Update:
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  • 2
    Headlines.nvim

    Headlines.nvim

    This plugin adds horizontal highlights for text filetypes

    This plugin adds highlights for text file types, like markdown, orgmode, and neorg.
    Downloads: 0 This Week
    Last Update:
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  • 3
    Teal

    Teal

    The compiler for Teal, a typed dialect of Lua

    This is the repository of tl, the compiler for Teal, a typed dialect of Lua. The core compiler has no dependencies and is implemented as a single tl. lua file which you can load into your projects. Running tl. loader() will add Teal support to your package loader, meaning that require() will be able to run .tl files. Alternatively, you can find pre-compiled binaries for Linux x86_64 and Windows x86_64 at the releases page. The packages contain a stand-alone executable that can run Teal...
    Downloads: 0 This Week
    Last Update:
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  • 4
    MINI.NVIM

    MINI.NVIM

    Library of 40+ independent Lua modules improving overall Neovim

    Library of 40+ independent Lua modules improving overall Neovim (version 0.8 and higher) experience with minimal effort. They all share same configuration approaches and general design principles. Think about this project as "Swiss Army knife" among Neovim plugins: it has many different independent tools (modules) suitable for most common tasks. Each module can be used separately without any startup and usage overhead.
    Downloads: 0 This Week
    Last Update:
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  • 5
    LuaUnit

    LuaUnit

    LuaUnit is a popular unit-testing framework for Lua

    LuaUnit is a popular unit-testing framework for Lua, with an interface typical of xUnit libraries (Python unittest, Junit, NUnit, ...). It supports several output formats (Text, TAP, JUnit, etc.) that can be used directly or work with continuous integration platforms (Jenkins, Maven, etc.). LuaUnit may be installed as a rock or directly added to your project. For simplicity, LuaUnit is contained into a single file and has no external dependency. LuaUnit may also be used as an assertion library, to validate assertions inside a running program. ...
    Downloads: 0 This Week
    Last Update:
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  • 6
    myScite

    myScite

    The allRound pocket sized CodeEditor.

    Refurbished Scintilla.orgs/SciTE with some additional patches. -- Features -- - Full MinGW and GTK SDKs Autocomplete.(190+) - Do system scripting (bash, applescript, cmd, powershell, perl, j/vbscript, awk) - Examine all sorts of data files (sql, regedit, mib, xml, yaml, json, vcard ...) - Review difference and patch files - Create makefiles (gnu make / cmake) - Edit html, css and config files (with calltips) - Describe circuits in vhdl and spice. ... - And finally; read & write...
    Downloads: 8 This Week
    Last Update:
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  • 7
    Highlight Code Converter

    Highlight Code Converter

    Source code to formatted text converter

    Highlight is a source code to formatted text converter. It generates HTML, XHTML, RTF, ODT, LaTeX, TeX, SVG, BBCode and terminal escape sequences with coloured syntax highlighting. Language definitions and colour themes are Lua scripts and support plugins
    Downloads: 2 This Week
    Last Update:
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  • 8
    char-rnn

    char-rnn

    Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN)

    char-rnn is a classic codebase for training multi-layer recurrent neural networks on raw text to build character-level language models that learn to predict the next character in a sequence. It supports common recurrent architectures including vanilla RNNs as well as LSTM and GRU variants, letting users compare behavior and output quality across model types. It is straightforward: you provide a single text file, train the model to minimize next-character prediction loss, then sample from the trained network to generate new text one character at a time in the style of the dataset. ...
    Downloads: 0 This Week
    Last Update:
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