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Downsorter : Deterministic File Management Framework

#DownsorterDownsorter is a lightweight, low-overhead system utility designed to address un-indexed file growth and directory entropy. By evaluating metadata payloads and extension patterns at the local OS layer, Downsorter automatically categorizes incoming files into isolated storage partitions using their extensions and types , ensuring optimal file access latency and zero-clutter file-system architecture. Unlike Other CLI-based sorting tools , Downsorter is customizable using CLI commands , ensuring maximum user control #FEATURES/FUNCTIONS --folder: Folder to organize. Defaults to the current user's Downloads folder. --apply: Actually move files. Without this flag, the script only previews the planned moves. --edit-config : Open the user config file in the default text editor to manually edit the config file --add-ext : Add extension(s) to an existing category. Use with --to ( eg downsorter --add-ext .ext --to CATAGORYNAME ) --to : Specify existing category to add extensions to (used with --add-ext) --remove-ext : Remove extension(s) from an existing category. Use with --from ( eg downsorter --remove-ext .txt --from CATAGORYNAME ) --from : existing category to remove extensions from (used with --remove-ext) --min-age-days: Only move files that have not been modified for at least this many days. --config-path : Print the path to the user config file #INSTALLATION   - run this inside powershell: python -m pip install downsorter

tixOS: Modular Operating System Based Website

# tixos An Investigation into Retro-Thematic Micro-Kernel Architectures inside the field of personal websites !! ( buttons themed off MacOS ) ## Current Features ( as of 13/08/2026 ): - Functioning NOTEPAD that saves your text in a .txt file - pixel art app that allows you to draw art on a working canvas and save it as a .png file - spritesheet - animation based DESKTOP PET inspired website widget in the bottom right    - Welcome app ( brief introduction ) - Links app ( social links + project links )      

Visual Digits :TRAINABLE K-NN based ML thinking model

#Visual Digits A webapp made in vscode that guesses cursorwritten digits from 0-9 using a user-trainable K-NN model  The app uses a simple manually trained dataset anyone can update locally by training their K-NN model , of which new training data is stored in localstorage of the browser itself !!  ## Features - Draw digits with your cursor- Guess digits from 0 to 9- Manually train the webapp by approving the correct digit- Saves training data locally in the browser's localstorage ## Training Your Dataset- after drawing and guessing , you can click one of the digits from 0 to 9 below the "train as" header - on clicking , the guessed drawing is stored under that digit as sample- the more data the better ## How It Works - Your drawing is converted into a 28×28 grid of pixel values. When you click Guess, the app compares it to every saved example using Euclidean distance and picks the closest match (k-NN with k=3). The pre-trained dataset (217 samples) is embedded in the js file from my python project. Any new drawings you train are saved to your browser's localStorage and persist across page loads.