Showing 4 open source projects for "tiny-workflow"

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    HARPOON 2

    HARPOON 2

    Getting you where you want with the fewest keystrokes

    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 marked files and offers commands to add, remove, or navigate to those files with minimal keystrokes. ...
    Downloads: 1 This Week
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  • 2
    [OFFICIAL] tinyfiledialogs C C++ v3.21.3

    [OFFICIAL] tinyfiledialogs C C++ v3.21.3

    inputbox password WIN OSX GTK QT Console VCPKG C# Lua R Fortran Pascal

    one C/C++ file & header ==> 8 functions: - beep - tray notify popup - message & question - input & password - save file - open file(s) - select folder - color picker complements OpenGL Vulkan GLFW GLUT GLUI VTK SFML TGUI SDL Ogre Unity3d ION OpenCV CeGui MathGL GLM CPW GLOW Open3D ImGui MyGui GLT NGL STB Nuklear Fenster MicroUi & head-less programs NO INIT NO MAIN LOOP NO LINKING NO INCLUDE win (XP to 11) ASCII MBCS UTF-8 UTF-16 (wchar_t) - native dialogs osx/unix...
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    Downloads: 91 This Week
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  • 3
    PECS

    PECS

    Entity Component System (ECS) for PICO-8 & Picotron

    PECS is a lightweight Entity Component System (ECS) framework tailored for PICO-8 and Picotron, implemented in just 567 tokens. Based on KatrinaKitten's Tiny ECS Framework v1.1, PECS allows developers to manage game entities and their behaviors efficiently within the token limitations of PICO-8.
    Downloads: 0 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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