Ship
Pending review
Downsorter v0.2.0 Release
A lightweight CLI tool to organize messy folders by sorting files into category folders based on their extension!!
in a nutshell:
- Organizes files into folders by type (Images, Documents, PDFs, Code, Audio, Video, Archives, etc.)
- Safely previews changes before moving anything
- Handles file name collisions automatically
- Filters files by minimum age to avoid moving in-use files
Install:
it is a pip now , fully functional , so you can just :
pip install downsorter
running it on terminal :
Preview what will happen can be gotten by :
downsorter –folder “C:\Users\YourName\Downloads”/“path of the folder you wish to sorter ( absolute path )”
Actually move the files by :
downsorter –folder “C:\Users\YourName\Downloads”/“path of the folder you wish to sorter ( absolute path )” –apply
Key features:
- ✅ Preview mode (safe by default)
- ✅ 11 file categories automatically sorted
- ✅ Duplicate file handling with auto-rename
- ✅ Cross-platform (Windows, macOS, Linux)
- ✅ No external dependencies
**Project links:**
- GitHub: https://github.com/bhuvan-rai/Downsorter
Clean up your Downloads folder in seconds!!
usage example : attached below , release v0.2.0 ( do NOT ask what happened with the first release 🤧🤧🤧💔💔)
Fixed what the review lacked , now the project comes in exe file !
Completed building the basic idea ->
csv for logs , python for everything else lol
decided the formats and types to include into this , will start with the built on 14/15.
keeping the github repo private until i can make the first working version
Ship
update : 12/06/2026 ( few hrs after the previous devlog )
i updated the sample size to 200+ , my goal is to make the default dataset json file in github near about 1000 , 100/digit , so its even better , for now i am satisfied and will see tomorrow for improvements/data training!
update : 12/06/2026
i was working with the mnist dataset and the tensorflow method to create this , and my model trained well ( 0.91 accurancy 0->1 ) , but it never worked during testing , so i gave up the mnist set
INSTEAD… i tried to make something new with the help of codex and power of json !
introducing the NEW and IMPROVED visual digits !
( github+FULL source code for PROPER info ( you won’t regret if ur interested )
in a nutshell tho :
now you can train it yourself , it currently has 10 samples / digit but you can now train it on your own system and even show it to me through github if your training dataset is superior !
it might be weaker than the CNN pathway but its simpler in working so yeah !
code is complete , i took help from readymade projects in this same field , im not an expert in AI since i never learnt it in school , just basic python so i needed alot of help from resources regarding this project.
10 epoches left a lot of room of error , im currently training my model with 20 epoch so its more accurate , will see if it needs more training soon.
( excuse if step times are slow my laptop is very mediocre , had to close everything for optimal ram usage )
my slack bot is functioning , but i still cannot get it to run 24/7 . once i figure it out , i will dive deeper into the slackbot and see the stuff i can create and improve !
