DEV Community: Ashandeep Kaur

🌱 ZVYA AI: Making Everyday Business Tasks Simpler with AI

Ashandeep Kaur — Sun, 25 Oct 2026 15:28:58 +0000

🌱 ZVYA AI: Making Everyday Business Tasks Simpler with AI

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

What I Built

Small businesses and independent sellers often spend hours managing customer messages, writing product descriptions, and preparing promotional content.

ZVYA AI is my idea for a local-first AI business assistant designed to make these everyday tasks simpler.

The goal is to help small sellers save time, reduce repetitive work, and focus more on growing their businesses.

Demo

A working demo is not available yet.

Code

The implementation is still in progress.

How I Built It

My planned approach is to use Python and Streamlit for the interface, with a locally running open-weight AI model through Ollama.

The goal is to explore a privacy-friendly approach that can help small business owners without depending entirely on paid, closed AI APIs.

Why Does Open Innovation Matter?

Open innovation gives students and independent developers the opportunity to experiment, learn from existing tools, and build solutions that address real-world problems.

For a project like ZVYA AI, open-weight models and open-source frameworks could make AI more accessible to small businesses and developers working with limited resources.

The Lazy Payoff

The vision is simple: spend less time on repetitive business tasks and more time on customers, creativity, and growth.

Prize Categories

To be confirmed based on the official challenge rules and the final implementation.

ZVYA --- AI

Ashandeep Kaur — Sat, 17 Oct 2026 14:54:05 +0000

I built ZVYA AI: a local-first business assistant for small sellers

What I built

I built ZVYA AI, a local-first AI business assistant for small sellers, independent makers and home businesses. Many small businesses receive orders as informal chat messages, then manually copy product details, quantities, prices and delivery requests into notes or spreadsheets. ZVYA AI helps turn those messages into structured orders and keeps the seller in control of the final record.

A seller can paste a customer message, ask a local AI model to extract order details, review and edit the result, then save the confirmed order. The app also supports order status updates, manually recorded payments, outstanding balances, business summaries and CSV export.

Code

GitHub: [https://github.com/ashandeepkaur27/ZVYA---AI.]

How I built it

Python + Streamlit for the app and interface.
Ollama + Gemma 3 for local extraction of order details from customer messages.
SQLite for storing orders locally.
Pandas for lightweight reporting and CSV export.

The app does not need a hosted AI API key. In the default setup, the prompt is sent to the Ollama service on the same computer. The extracted fields are shown for review before the seller saves an order. This human-review step matters because AI can misunderstand prices, dates or quantities.

I also kept a distinction between an advance amount mentioned in a customer message and a payment that the seller has actually received. Only manually recorded receipts count toward the payment summary.

Why this matters

Small sellers deserve practical digital tools without needing a complicated subscription-based business platform. ZVYA AI explores how a local AI model can reduce repetitive order entry while keeping data and the final decision with the seller.

What I learned

Building this project helped me connect a local language model to a working Streamlit interface, validate model output, persist records with SQLite, and think about the difference between AI-extracted information and verified business data.

What's next

Potential next improvements include customer follow-up message drafts, reminders for upcoming delivery dates, and more robust date parsing.

Hashtags

hf26challenge #AI #Gemma #Ollama #Python #Streamlit #OpenSource

TouchGrass - AI

Ashandeep Kaur — Sun, 11 Oct 2026 17:13:11 +0000

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

## What I Built

I built TouchGrass AI, a local AI-powered outdoor activity companion that encourages people to take a break from their screens and reconnect with nature. 🌿

The idea is simple: instead of spending all day scrolling, users can generate small outdoor missions, complete them in the real world, and record their experiences in Field Notes.

Key Features

🌱 AI-Generated Missions: Get outdoor activity ideas powered by local AI.
✅ Mission Checklist: Track your progress as you complete activities.
📝 Field Notes: Record your experiences and reflections.
🌲 Nature-Inspired UI: A forest-green and mint interface designed around the idea of spending more time outdoors.
🔒 Local AI Inference: Uses Ollama with Gemma 3, so AI generation runs locally instead of relying on a paid cloud AI API.

TouchGrass AI is designed for students, people who spend a lot of time on screens, and anyone who wants a simple nudge to step outside.

Demo

GitHub Repository: https://github.com/ashandeepkaur27/TouchGrass-AI

TouchGrass AI currently runs locally on my computer. I’m including a short screen-recorded demo showing how users can generate outdoor missions, track their progress, and write Field Notes.

Demo video: [https://youtu.be/kPPGA8FwSwA]

Code

Explore the source code, README, screenshots, and setup instructions here:

TouchGrass AI on GitHub

The project is built with Python and Streamlit, with Ollama providing local AI inference.

How I Built It

I built TouchGrass AI using Python, Streamlit, and Ollama with the Gemma 3 model.

Streamlit powers the user interface, while Python handles the application logic and communication with Ollama's local API. When a user requests an outdoor mission, the application sends a prompt to the locally running Gemma 3 model and displays the generated response.

The app also uses Streamlit session state to manage mission progress and Field Notes during the current session.

My goal was to build a practical, beginner-friendly project that combines local AI with a real-world purpose. Rather than using AI just to generate text, TouchGrass AI uses it to encourage small offline activities.

Why Does Open Innovation Matter?

Open innovation made it possible for me to experiment with AI in a more accessible and transparent way.

Using the open-weight Gemma 3 model through Ollama allowed me to run AI locally without depending on a paid cloud AI API for text generation. This gave me more control over the development process and helped me learn how local AI inference works in a real application.

Open-source tools also made it possible to combine Python, Streamlit, and local AI into a project that others can explore, learn from, and improve.

I hope TouchGrass AI inspires other developers to build technology that helps people develop healthier relationships with their screens.

My Agent Session

I built this project using Python, Streamlit, and Ollama. I have not included a DevRelay agent session.

Prize Categories

Best Use of Gemma — TouchGrass AI uses Google's open-weight Gemma 3 model through Ollama for local AI-powered outdoor mission generation.

devchallenge #hf26challenge

Interview Buddy AI — A Local AI Interview Coach for My Friend

Ashandeep Kaur — Mon, 05 Oct 2026 06:55:15 +0000

What I Built

I built Interview Buddy AI, a local AI-powered mock interview coach for a friend who is preparing for job interviews.

The problem was simple: practicing interviews alone can be difficult. My friend needed a way to practice answering interview questions, receive feedback, and improve without needing another person to conduct every practice interview.

Interview Buddy AI provides a mock interview experience where the AI asks interview questions, evaluates answers, gives a score and feedback, and can ask follow-up questions.

It supports both text and voice-based interview practice.

Demo

GitHub:
https://github.com/ashandeepkaur27/Interview_Buddy-AI

Video Demo:

The demo shows:

Starting an interview
AI-generated interview questions
Answering questions
AI feedback and scoring
Follow-up questions
Voice input and transcription
Code

The complete source code is available here:

https://github.com/ashandeepkaur27/Interview_Buddy-AI

The repository includes the application code, README, requirements, setup instructions, .gitignore, and MIT License.

How I Built It

Interview Buddy AI is built around Gemma 3, an open-weight AI model running locally through Ollama.

Gemma 3

I use Gemma 3 to generate realistic interview questions, evaluate answers, provide feedback, and generate follow-up questions.

Ollama

I use Ollama to run Gemma 3 locally instead of depending on a cloud AI API for the core interview functionality.

Faster-Whisper

For voice-based interviews, Faster-Whisper converts spoken answers into text locally so the AI can evaluate them.

Streamlit

The application interface is built with Streamlit.

The overall flow is:

User → Interview Buddy AI → Gemma 3 through Ollama → Question / Evaluation / Feedback

For voice:

User's Voice → Faster-Whisper → Text → Gemma 3 → Feedback

Why Does Open Innovation Matter?

Open innovation made this project possible without depending completely on a closed AI API.

Running Gemma 3 through Ollama allows the core AI experience to run locally on the user's own computer. This gives users more control over their data and setup.

It also makes experimentation easier. Developers can change models, experiment with prompts, and modify the application's behaviour without being locked into a single closed AI provider.

For an interview practice tool, this is useful because users may share personal information about their education, projects, career plans, and previous interview experiences.

The open approach allowed me to build a more customizable and locally focused interview practice experience.

What I Learned

Building this project helped me understand how local AI models can be integrated into a real application.

I learned how to connect an application with Ollama, work with an open-weight model such as Gemma 3, process voice input with Faster-Whisper, and combine these components into a practical AI application.

My biggest takeaway was that AI applications do not always need to depend on a closed cloud API. Local models can be useful when privacy, customization, and control are important.

Prize Categories

Best Use of Gemma

Interview Buddy AI uses Gemma 3 as the core AI model for generating interview questions, evaluating answers, providing feedback, scoring responses, and generating follow-up questions.

Intro

Ashandeep Kaur — Fri, 02 Oct 2026 08:07:22 +0000

I am new to community, Happy to be the part of dev.