Search Results for "text editor software"

UI Component Library for Neovim.

refactoring.nvim is a Neovim plugin developed to bring powerful automated code refactoring capabilities to one of the most popular text editors among programmers, giving developers a suite of refactoring operations that streamline repetitive restructuring tasks inside the editor. 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. ...

snacks.nvim is a playful and minimal plugin for Neovim that shows little "snack" animations or messages in the command line during idle moments. It adds charm and personality to the editor without being intrusive, appealing to users who enjoy aesthetic feedback in their development environment. snacks.nvim is written in Lua and intended as a lighthearted UX enhancement.

HARPOON 2 is a highly popular Neovim plugin created by ThePrimeagen that enhances editor navigation by letting users mark and quickly jump to frequently used files and terminals within their Neovim workflow. Unlike traditional global marks in Vim, Harpoon’s marks are per-project and automatically updated, meaning they follow files even as content changes, which streamlines switching between contextually relevant files during development. It comes with an intuitive menu system that lists...

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.

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...