DEV Community: Sandeep Verma

⚡ SellSpark AI: Building a Local AI Product Listing Generator with Qwen 2.5

Sandeep Verma — Sun, 18 Oct 2026 11:37:06 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

⚡ SellSpark AI — Turn Product Details Into Powerful Listings

What I Built

I built SellSpark AI, an AI-powered product listing generator designed to help small businesses, freelancers, and independent online sellers create professional marketing content quickly and efficiently.

🎯 The Problem

Small online sellers often struggle to create attractive product descriptions, engaging social media captions, and effective marketing content.

Writing these manually takes time, while hiring professional copywriters or subscribing to paid AI services can be expensive for small businesses.

💡 The Solution

SellSpark AI transforms basic product information into ready-to-review marketing content using a locally running open-weight AI model.

Users simply enter their product details, select their preferred writing style and selling platform, and let AI generate the content.

✨ Key Features

🛍️ AI Product Titles: Generates attractive product titles.
📝 Product Descriptions: Creates professional marketing descriptions.
⭐ Key Selling Points: Highlights important product features.
📱 Social Media Captions: Generates engaging Instagram captions.
#️⃣ Hashtag Generator: Creates relevant marketing hashtags.
🎨 Multiple Writing Styles: Professional, Friendly, Luxury, Creative, and Minimalist.
🛒 Platform Selection: Amazon, Flipkart, Instagram, Etsy, Shopify, and more.
📋 Copy-Friendly Output: Easily copy generated content.
📥 Download Listings: Save generated descriptions as text files.
🔒 Local AI Processing: Product information is processed locally through Ollama.

💰 Who Would Pay for It?

SellSpark AI is designed for:

Small e-commerce businesses
Instagram sellers
Handmade-product sellers
Independent online store owners
Freelancers managing product listings

A future version could offer premium features such as bulk listing generation, saved brand styles, multilingual content, and advanced product templates.

These features could form the basis of an affordable subscription service for small businesses.

Demo

I tested SellSpark AI using a sample product:

Product: Lavender Bliss Candle

Category: Handmade Products

Features: Handmade, soy wax, lavender fragrance, reusable glass jar

Price: ₹299

Target Audience: Gift buyers and home decor lovers

Writing Style: Luxury

Selling Platform: Instagram

SellSpark AI successfully generated:

An attractive product title
A professional product description
Key product features
An Instagram marketing caption
Relevant hashtags

The application also allows users to copy and download their generated listings.

The prototype runs locally using Streamlit and Ollama.

Here are the samples
Homepage

Input

AI generated output

Code

The complete project is available on GitHub.

🔗 GitHub Repository:

https://github.com/sandeepji11/sellspark-ai

🛠️ Tech Stack

Technology	Purpose
Python	Backend application logic
Streamlit	Interactive web interface
Ollama	Local AI model inference
Qwen 2.5 (1.5B)	Open-weight language model
Requests	Communication with the Ollama API

The project includes installation instructions and information about running the application locally.

How I Built It

I developed SellSpark AI using Python, Streamlit, Ollama, and the open-weight Qwen 2.5 (1.5B) language model.

Step 1: Setting Up Local AI

I installed Ollama and configured the Qwen 2.5 (1.5B) model to run locally on my MacBook.

The model can generate responses without requiring a paid AI API.

Step 2: Building the Interface

I used Streamlit to create an interactive web application where users can enter product information, select writing styles, and choose selling platforms.

The interface includes a product input form and an AI-generated results section.

Step 3: Connecting the AI Model

I connected the Streamlit application to Ollama through its local REST API.

When users submit product details, the application constructs a structured prompt and sends it to the locally running Qwen model.

The AI generates product titles, descriptions, selling points, captions, and hashtags.

Step 4: Improving Output Quality

I refined the prompts to discourage unsupported product claims, invented specifications, and inaccurate marketing information.

I also configured the model with a low temperature to encourage more consistent outputs.

Since AI-generated content can still contain mistakes, users are encouraged to verify product details before publishing.

Step 5: Adding Practical Features

I implemented:

AI connection status detection
Multiple writing styles
Selling platform selection
Copy-friendly generated output
Downloadable text files
Error handling for unavailable AI services

Step 6: Testing

I tested the application with sample product information and confirmed that Qwen 2.5 could generate product listings through the Streamlit interface.

The application runs locally, with Ollama handling AI inference.

Why Does Open Innovation Matter?

Open innovation is central to SellSpark AI.

Instead of relying on a proprietary AI API, I built the application around Qwen 2.5, an open-weight language model, and Ollama, an open-source local inference tool.

This approach offers several important advantages.

🔒 1. Better Privacy

Small businesses may not want to send their product information or marketing strategies to external AI services.

SellSpark AI processes product details locally through Ollama, giving users greater control over their information.

💰 2. No Per-Request API Fees

Many commercial AI services charge based on usage.

By running Qwen locally, SellSpark AI avoids per-request AI API charges.

This makes it possible to experiment with a low-cost product aimed at small businesses.

Local inference still requires suitable hardware and electricity.

💻 3. Offline Functionality

After installing the required software, dependencies, and model, SellSpark AI can generate product listings without an internet connection.

This is useful for users who want an AI tool that does not depend on continuous access to a cloud service.

🧠 4. Model Flexibility

Using Ollama allows the application to support different compatible open-weight models.

Developers can experiment with alternative models and customize prompts without depending on a single proprietary AI provider.

🚀 5. Greater Control

Open-weight AI gives developers more flexibility to customize application behavior and explore specialized solutions for different industries.

For SellSpark AI, this creates opportunities to develop industry-specific writing styles, regional-language support, and custom product listing templates.

🌍 Open Innovation vs. Closed AI

The biggest advantage of the open-weight approach for this project is the ability to run the core AI functionality locally without a paid API.

A closed cloud API might offer stronger generation quality, but it would introduce external API dependencies and potentially recurring usage costs.

For a small-business-focused tool like SellSpark AI, local inference offers an interesting alternative focused on privacy, flexibility, and cost control.

🔮 Future Improvements

I plan to expand SellSpark AI with:

📸 Product image-based description generation
🌐 Multilingual product listings
📦 Bulk product listing generation
🎨 Custom brand writing styles
📊 Product listing management
🛒 Marketplace integrations
🤖 Support for additional open-weight AI models

My long-term goal is to turn SellSpark AI into an affordable and accessible marketing assistant for independent online sellers.

🎯 Final Thoughts

Building SellSpark AI helped me explore how open-weight AI can be used to create practical applications with real business potential.

The project demonstrates that developers can build useful AI-powered tools without relying entirely on proprietary cloud APIs.

SellSpark AI is a small step toward making AI-powered marketing tools more accessible to independent sellers and small businesses.

⚡ Built with Qwen 2.5, Ollama, Python, and Streamlit.

OpenSourceAI #SideHustle #Qwen #Ollama #ai #python #hacktoberfest

🌿 TerraQuest AI: Stop Scrolling, Start Exploring | Open-Weight AI Outdoor Adventure Generator

Sandeep Verma — Sun, 11 Oct 2026 18:07:14 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass

🌿 What I Built

TerraQuest AI — Stop Scrolling. Start Exploring.

What if artificial intelligence could encourage us to spend LESS time using technology?

We usually associate AI with chatbots, productivity tools, and digital experiences.

But I wanted to explore a different possibility:

What if AI could inspire people to close their laptops, put away their phones, and discover the world around them?

That idea inspired me to build TerraQuest AI.

TerraQuest AI is an AI-powered outdoor adventure generator that creates personalized real-world missions using an open-weight language model.

🌍 The Problem

Digital entertainment is everywhere.

People spend hours scrolling through social media, watching videos, and interacting with screens.

But sometimes, the biggest challenge to going outside isn't finding time.

It's deciding what to do.

TerraQuest AI addresses this problem by turning outdoor exploration into a personalized adventure.

💡 The Solution

TerraQuest AI generates creative outdoor missions based on four user preferences:

📍 Location
⏱️ Available time
🌱 Interests
⚡ Difficulty level

For example, a user interested in nature might receive missions such as:

Find three different types of leaves.
Observe a bird and describe its behavior.
Discover an interesting natural pattern.
Spend five minutes enjoying the outdoors.

The application also includes:

📵 Touch Grass Mode

Once an adventure is generated, users are encouraged to put their devices away and complete their missions in the real world.

🏆 Adventure Achievements

Users can mark adventures as completed and unlock achievement badges.

🧠 Personalized AI Adventures

Instead of displaying predefined activities, TerraQuest AI generates missions using a locally running language model.

The goal is simple:

Spend less time scrolling. Spend more time exploring.

🎬 Demo

🌿 TerraQuest AI in Action

The application follows a simple workflow:

Open TerraQuest AI.
Enter your city.
Select your available time.
Choose your interests and difficulty level.
Click "Generate My Adventure".
Receive personalized outdoor missions.
Activate Touch Grass Mode.
Complete your adventure and unlock achievements.

📸 Application Screenshots

Homepage — Create Your Adventure

AI-Generated Outdoor Adventure

Touch Grass Mode

Adventure Achievements

💻 Running the Demo

TerraQuest AI currently runs locally using Streamlit and Ollama.

To try the application, follow the setup instructions in the GitHub repository linked below.

💻 Code

GitHub Repository

https://github.com/sandeepji11/terraquest-ai

The project includes the Python application and instructions for running it locally.

🛠️ Technology Stack

Technology	Purpose
Python	Core application logic
Streamlit	Interactive web interface
Ollama	Local AI model execution
Qwen2.5 1.5B	Personalized adventure generation
GitHub	Open-source code hosting

🧠 How I Built It

Step 1: Building the Interface

I used Python and Streamlit to create a simple, interactive web application.

The interface allows users to enter their location, select their available time, and choose the type of outdoor adventure they prefer.

I designed the application with a green-and-white theme inspired by nature.

Step 2: Integrating Open-Weight AI

The core of TerraQuest AI is the Qwen2.5 1.5B language model.

I used Ollama to run the model locally.

When users request an adventure, the application creates a prompt containing their preferences.

This prompt is sent to Ollama through its local API.

The model then generates a personalized outdoor mission.

The response is displayed directly inside the Streamlit application.

Step 3: Creating Touch Grass Mode

The goal of the challenge is to encourage people to spend less time looking at screens.

To support this goal, I created Touch Grass Mode.

After generating an adventure, users can start their mission and put their devices away.

The application encourages real-world participation rather than continuous digital engagement.

Step 4: Adding Achievements

I implemented a simple achievement system using Streamlit session state.

Users can track completed adventures and unlock badges.

The available achievements include:

🌱 Nature Explorer
🌿 Nature Enthusiast
🏆 Outdoor Champion

The current version stores progress temporarily during the application session.

Step 5: Testing

I tested the application by generating personalized outdoor missions with different input preferences.

The AI successfully generated adventure suggestions through the locally running language model.

🚧 Challenges

One challenge was integrating the locally running AI model with the Streamlit interface.

Another was designing the application around minimal screen time.

Rather than creating an experience that requires constant interaction, I focused on generating short missions that encourage users to step away from their devices.

🌱 Why Does Open Innovation Matter?

Open innovation is at the heart of TerraQuest AI.

I wanted to build an AI-powered application without depending entirely on proprietary cloud-based AI services.

Using an open-weight model made that possible.

🔒 Privacy

TerraQuest AI processes adventure-generation prompts locally through Ollama.

This means users do not need to send their prompts to a hosted language-model API.

💻 Local AI Inference

The core adventure-generation feature runs on a local machine.

Once the required model and dependencies are installed, it does not require an internet connection for AI inference.

🔄 Flexibility

Open-weight models allow developers to experiment with different model configurations.

TerraQuest AI can potentially support alternative models in future versions.

🌍 Accessibility

Open-weight AI gives developers opportunities to build intelligent applications without paying for every inference request.

💚 Why It Matters for This Project

TerraQuest AI demonstrates that AI doesn't always need to keep people engaged with technology.

Sometimes, AI can be most useful when it encourages people to disconnect.

The best AI experience might be the one that inspires you to close your screen.

🤖 My Agent Session

I used AI assistance during development to help plan the application, write Python code, troubleshoot errors, and improve the Streamlit interface.

The project uses Ollama and Qwen2.5 1.5B for its core adventure-generation functionality.

I did not record a DevReplay agent session for this version.

🏆 Prize Categories

No optional partner-technology prize category is being claimed for this submission.

🔮 Future Improvements

I would love to expand TerraQuest AI with:

🗺️ Nearby park and trail discovery
🌿 AI-assisted plant identification
📵 Offline adventure cards
🏅 Persistent achievements
🌎 Community outdoor challenges

These improvements could help transform TerraQuest AI into a more complete outdoor exploration companion.

🌍 Final Thoughts

Building TerraQuest AI helped me explore an interesting question:

Can artificial intelligence encourage people to disconnect from technology?

I believe it can.

AI doesn't always need to maximize the amount of time people spend using applications.

Sometimes, its greatest contribution is helping people discover meaningful experiences beyond their screens.

🌿 TerraQuest AI — Stop Scrolling. Start Exploring.

Thanks for reading!