Showing 4 open source projects for "options"

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  • 1
    indent-blankline.nvim

    indent-blankline.nvim

    Indent guides for Neovim

    indent-blankline.nvim is a Neovim plugin that adds vertical indentation guides to visually indicate code structure. It supports modern syntax parsing through Treesitter and provides extensive configuration options for appearance, scope highlighting, and context awareness. This plugin is essential for users who want cleaner and more structured code visuals.
    Downloads: 0 This Week
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  • 2
    refactoring.nvim

    refactoring.nvim

    The Refactoring library based off the Refactoring book

    ...Built around an intuitive set of commands and a Lua API, the plugin allows users to extract and inline variables or functions, pull blocks of code into new files, and modify code structure without leaving the comfort of Neovim’s modal interface. It integrates with built-in Neovim selection modes and can work with third-party tools like Telescope to present refactoring options quickly, enabling rapid transformation of code patterns. Beyond pure refactor actions, the plugin includes debugging helpers that insert print statements or remove them, aiding developers in tracing program flow and inspecting variable states as part of iterative development.
    Downloads: 0 This Week
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  • 3
    json.lua

    json.lua

    A lightweight JSON library for Lua

    ...Its small size makes it practical for games, plugins, scripts, embedded Lua environments, and projects where dependency weight matters. It is implemented in pure Lua and is known for being fast among pure Lua JSON options while still offering useful error messages. Overall, it is a convenient choice when a Lua project needs dependable JSON support with minimal setup.
    Downloads: 2 This Week
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  • 4
    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...
    Downloads: 0 This Week
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