DEV Community: Chandanpreet kaur

SlothSort: A Local AI File Organizer That Does the Thinking for You 🦥

Chandanpreet kaur — Wed, 21 Oct 2026 07:12:01 +0000

SlothSort: Let Local AI Tidy Your Files While You Relax 🦥

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady.

What I Built

Meet SlothSort, a local-first AI file organizer designed for people who keep saying, “I'll clean up my Downloads folder tomorrow.”

Instead of manually deciding where every file belongs, SlothSort uses a locally running AI model to suggest categories for filenames.

It can suggest categories such as Documents, Images, Code, Archives, Work, Personal, and Miscellaneous. The user can enter filenames into a simple webpage, click a button, and review the AI-generated suggestions.

The goal is to turn a tedious chore into a quick review instead of another task on the to-do list.

The sloth philosophy: work smarter, move less, and save your energy for something better.

Demo

SlothSort runs as a local webpage on my computer.

To try it locally, start the Python application and open http://localhost:8000 in your browser.

The prototype categorizes filenames and displays suggestions. It does not automatically move, rename, or delete files.

Code

The prototype uses Python, HTML, CSS, JavaScript, and Ollama's local API.

The application code is included below so the implementation can be inspected without a separate repository.

Project structure
slothsort/
└── app.py

How to run
Install Python and Ollama.
Download an open-weight model in Ollama if you haven't already.
Save the application code below as app.py.
Run python app.py.
Open http://localhost:8000 in your browser.
thanks!!!!!

LocalListing AI

Chandanpreet kaur — Tue, 13 Oct 2026 14:07:59 +0000

🛍️

I built LocalListing AI, a lightweight tool that helps small online sellers generate product listings using AI.

What it does: Users enter product details, choose a selling platform and language, and generate a product title, description, and hashtags.

Tech stack:

Python
Streamlit
Ollama
Llama 3.2 3B open-weight model
🛍️ LocalListing AI — Open-Source AI Side Hustle

I built LocalListing AI, a local AI tool that generates product descriptions for small businesses using Python, Streamlit, Ollama, and the Llama 3.2 3B open-weight model.

Project proof:

💻 Source code: [https://github.com/chandanpreetk853/locallisting-ai.git]
📸 Demo: Screenshots of the application and generated product listing.
🤖 AI model: Llama 3.2 3B running locally through Ollama.

The project explores how open-weight AI can help small sellers create product listings while keeping their data on their own computers.

My next goal is to test the tool with real sellers and learn which features they would pay for.

Why open-source AI?

LocalListing AI runs an open-weight model on the user's computer instead of relying on a paid external AI API for every request. This can help reduce recurring inference costs and keep product information private.

Using an open-weight model also gives me the flexibility to experiment with different models and customize prompts for small-business needs.

Business model: My proposed pricing is ₹199–₹499 per month, with potential future features such as bulk listing generation and custom brand voices.

What's next?

I'd like to test the tool with real sellers and improve it using their feedback.

Built with Python, Streamlit, Ollama, and Llama 3.2 3B. 🚀

🌿 I Built an AI That Tells You to Touch Grass — Locally with Gemma 3 1B

Chandanpreet kaur — Wed, 07 Oct 2026 17:12:56 +0000

🌿 TouchGrass AI — Use AI to Help You Touch Grass

For Hacktoberfest Week 1, I built TouchGrass AI — a local AI-powered outdoor adventure generator.

The idea is simple:

Use AI to help people spend less time on screens, not more.

Instead of generating another chatbot conversation, TouchGrass AI generates a small outdoor quest based on your available time, energy level, and location type.

🤖 How it works

The project runs AI locally using:

🐍 Python
🌐 HTML, CSS & JavaScript
🦙 Ollama
💎 Gemma 3 1B
💾 Browser LocalStorage

The flow is:

User
  ↓
TouchGrass AI Web App
  ↓
Python Backend
  ↓
Ollama
  ↓
Gemma 3 1B
  ↓
Outdoor Quest
  ↓
🌿 Go Outside

No cloud AI API is required for quest generation.

✨ Features

Generate personalized outdoor micro-adventures
Choose available time
Choose energy level
Choose location type
Built-in adventure timer
Save completed quests and reflections locally
Quest history
Lightweight local Python backend
PWA support
Local AI with Ollama + Gemma

🧠 Why I built it

AI is usually designed to keep us on our screens.

I wanted to try the opposite approach:

Use technology to help you step away from technology.

A quest can be something as simple as exploring your neighborhood, noticing interesting things around you, or taking a short outdoor walk.

🔒 Privacy

The AI model runs locally through Ollama.

Quest history and reflections are stored locally in the browser.

There is no need for a remote AI API key.

🚀 Run it locally

Clone the repository:

git clone https://github.com/chandanpreetk853/touchgrass-ai.git
cd touchgrass-ai

Make sure Ollama is running and Gemma 3 1B is installed:

ollama pull gemma3:1b

Then start the Python server:

python app.py

Open:

http://localhost:8000

📸 Project

The complete source code is available on GitHub:

https://github.com/chandanpreetk853/touchgrass-ai

🌱 What's next?

Some ideas I would like to explore:

Weather-aware quests
More outdoor activity categories
Quest difficulty levels
Achievement system
Better accessibility
More offline functionality
Location-based challenges

This is an early learning project, so feedback and contributions are welcome!

Built with local AI using Ollama + Gemma 3 1B. 🌿

hf26challenge #hacktoberfest #opensource #ai #localai #ollama #gemma #python #javascript

✦ LUMA — PRIVATE AI STUDY COMPANION

Chandanpreet kaur — Sun, 04 Oct 2026 12:55:57 +0000

hf26challenge

WHAT I BUILT

I built Luma, a private AI study companion designed to help students study more easily while keeping their study material local.

Luma has four main features:

• Explain difficult topics in simple language
• Generate beginner-friendly quizzes
• Simplify complicated study material
• Upload PDF notes and ask questions about them

The AI runs locally through Ollama using the open-weight Llama 3.2 model, so study questions and uploaded notes can be processed on the user's own computer instead of being sent to a closed cloud AI API.

I built Luma to solve a real student problem: getting useful AI assistance for studying while keeping personal study notes and educational material under the user's control.

DEMO VIDEO:

SOURCE CODE:

chandanpreetk853 / luma-private-ai-study-companion

A private AI study companion powered by local open-source AI with Ollama.

# ✦ Luma — Private AI Study Companion

Luma is a private AI study companion built with open-source AI and local inference.

It helps students understand difficult topics, create quizzes, simplify study material, and ask questions about their PDF notes — without sending their study content to a remote AI API.

## ✨ Features

- 📖 Explain difficult topics in simple language

- 📝 Generate beginner-friendly quizzes

- 🔄 Simplify complicated study material

- 📄 Upload PDF notes and ask questions about them

- 🔒 Local AI processing with Ollama

- 🤖 Powered by the open-weight Llama 3.2 model

- 🌙 Clean dark interface

## 🧠 How It Works

Luma uses:

- **Streamlit** — Web interface

- **Ollama** — Local AI inference

- **Llama 3.2** — Open-weight language model

- **PyPDF** — Extracts text from PDF study notes

- **Python** — Application logic

The basic flow is:


Student
&#x20;  ↓

…

View on GitHub

SHOW THE CODE

chandanpreetk853 / luma-private-ai-study-companion

A private AI study companion powered by local open-source AI with Ollama.

# ✦ Luma — Private AI Study Companion

Luma is a private AI study companion built with open-source AI and local inference.

It helps students understand difficult topics, create quizzes, simplify study material, and ask questions about their PDF notes — without sending their study content to a remote AI API.

## ✨ Features

- 📖 Explain difficult topics in simple language

- 📝 Generate beginner-friendly quizzes

- 🔄 Simplify complicated study material

- 📄 Upload PDF notes and ask questions about them

- 🔒 Local AI processing with Ollama

- 🤖 Powered by the open-weight Llama 3.2 model

- 🌙 Clean dark interface

## 🧠 How It Works

Luma uses:

- **Streamlit** — Web interface

- **Ollama** — Local AI inference

- **Llama 3.2** — Open-weight language model

- **PyPDF** — Extracts text from PDF study notes

- **Python** — Application logic

The basic flow is:


Student
&#x20;  ↓

…

View on GitHub

The main application is:

app/web.py

The project also contains:

• README.md
• requirements.txt
• .gitignore

The application code connects the Streamlit interface to the locally running Ollama API, while PyPDF is used to extract text from uploaded PDF notes.

HOW I BUILT IT

I built Luma using Python and Streamlit for the web interface.

Ollama provides local AI inference using the open-weight Llama 3.2 model. Luma communicates with Ollama's local API to generate explanations, quizzes, simplified text, and answers.

For PDF notes, I used PyPDF to extract text from uploaded documents before using the local Llama model to answer questions about the extracted content.

Technology stack:

• Python
• Streamlit
• Ollama
• Llama 3.2
• PyPDF
• Local HTTP API

The basic architecture is:

Student
↓
Luma Web Interface
↓
Python + Streamlit
↓
Ollama
↓
Llama 3.2
↓
AI Response

For PDF questions:

PDF Notes
↓
PyPDF
↓
Extracted Text
↓
Ollama + Llama 3.2
↓
Answer From Notes

WHY DOES OPEN INNOVATION MATTER?

Open innovation matters because it gives developers more control over the technology they build with.

For Luma, using an open-weight model with local inference made it possible to build an AI study companion without making a closed cloud AI API the core dependency.

This is especially useful for private study material. Students can run the AI locally, experiment with the model, customize the application, and build new features around an open technology stack.

Open innovation also makes experimentation and learning more accessible because developers can build around open technologies instead of treating AI as a completely closed service.

MY AGENT SESSION

I did not use a separate autonomous agent session for this project.

Luma is a local AI application built around Ollama and Llama 3.2 rather than a multi-step autonomous agent framework.

PRIZE CATEGORIES

GitHub

I am entering the partner category/categories that directly match the technologies used in Luma, particularly the Ollama/local AI category if available in the submission form.

Luma uses Ollama as the local inference layer and Llama 3.2 as its open-weight AI model.

TEAM SUBMISSION

This is a solo project.

I am the sole creator and submitter of Luma.

Dev username:
@c_k_74275275b328c851fce40