DEV Community: Karthikpillai77

Listing Writer: a private AI that writes product listings for small sellers

Karthikpillai77 — Fri, 16 Oct 2026 06:17:20 +0000

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

What I Built

Listing Writer is a small web app that turns a few product facts into a ready-to-post online listing.

You type the product name, a few key details (materials, size, colour, what makes it special), the price, where you'll post it (Etsy, eBay, Instagram or WhatsApp Business), and the tone you want. It returns a title, a description of about 100 words, six tags, and a short caption with the price.

Who would pay for it? Small sellers: home bakers, craft makers, boutique owners and anyone selling on Instagram or WhatsApp. They know their products well but spend a lot of time writing descriptions, and many are not confident writing in English. A tool that does this in seconds saves them time and makes their listings look more professional.

The app is told to use only the facts you give it. It must not invent materials, sizes or discounts, so the seller never ends up promising something that isn't true.

Demo

Watch the demo here: https://drive.google.com/file/d/1oc-JS8zHFYSKIUqPVGMfTGDnTDO86xX1/view?usp=sharing

Code

Karthikpillai77 / Side-Hustle

# Listing Writer

A tiny local web app that turns a few product facts into a polished online
listing (title, description, tags and a short caption) for small sellers.
Powered by the open-weight Gemma model running locally through Ollama.
No cloud, no API key, no per-listing cost, and product details never leave
the seller's computer.

## Run it
1. Install Ollama: https://ollama.com
2. `ollama pull gemma3:4b` (or `gemma3:1b` for a smaller PC)
3. `python app.py`
4. Open http://localhost:8002

Use a different model (PowerShell): `$env:MODEL="gemma3:1b"; python app.py`

View on GitHub

How I Built It

I'm just starting out, so I kept it small on purpose.

Model: Gemma 3, an open-weight model from Google.
Local inference: Ollama runs the model on my own computer.
Backend: one Python file (app.py) using only the standard library. It serves the page and sends a prompt to Ollama.
Frontend: one plain HTML file with a small form.

The prompt does the main work. It gives the model the product facts and the platform, then asks for a fixed format: a title under 80 characters, a description of 80 to 120 words, six tags and a short caption. It also tells the model never to add facts that weren't provided.

Why Does Open Innovation Matter?

A side business only works if the tool costs almost nothing to run. Because Gemma runs locally through Ollama, there is no per-listing fee and no subscription. A seller can write as many listings as they like without watching an API bill.

It is also private. A seller's new products, prices and supplier details stay on their own computer instead of going to someone else's server, which matters when a launch is meant to be a surprise.

It works without an internet connection once the model is downloaded, and I can swap in a smaller or bigger model by changing one setting, which let me match the model to my laptop. With a closed API I would be paying per request and depending on someone else's pricing.

One honest limit: a small local model can make mistakes or write something that doesn't sound quite right, so a seller should always read the listing before posting it.

Prize Categories

Best Use of Gemma: Gemma 3 runs locally through Ollama and is the entire AI core of Listing Writer. It writes every listing on my own computer.

Grass Pass: a local AI that gets me off the screen and outside

Karthikpillai77 — Sat, 10 Oct 2026 06:18:32 +0000

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

What I Built

Grass Pass is a small web app that turns "I have 30 minutes" into a short outdoor plan.

You choose how much time you have, who is going, and how you feel, then type what is near you (a park, a river bank, paddy fields). It gives you one simple activity with a few things to bring, three steps, three small things to look for along the way, a fallback if it rains, and a last line telling you to put your phone in your pocket and go.

I built it for people like me who spend most of the day on a screen and keep saying they'll go outside later. The app is meant to be the shortest part of the experience: you read the plan for a minute and then close the tab.

How it went outside: [Write 2 or 3 honest sentences. What did you type in? What plan did it give you? Did you do it? What did you notice or enjoy, or what didn't work?]

Demo

Watch the demo here: https://drive.google.com/file/d/1oc-JS8zHFYSKIUqPVGMfTGDnTDO86xX1/view?usp=sharing

Code

Karthikpillai77 / grass-pass

# Grass Pass

A tiny local web app that turns "I have 30 minutes" into a short outdoor plan.
Powered by the open-weight Gemma model running locally through Ollama.
No cloud, no API key, works offline once the model is downloaded.

## Run it
1. Install Ollama: https://ollama.com
2. `ollama pull gemma3:4b` (or `gemma3:1b` for a smaller PC)
3. `python app.py`
4. Open http://localhost:8001

Use a different model (PowerShell): `$env:MODEL="gemma3:1b"; python app.py`

View on GitHub

How I Built It

I'm just starting out, so I kept it small on purpose.

Model: Gemma 3, an open-weight model from Google.
Local inference: Ollama runs the model on my own computer.
Backend: one Python file (app.py) using only the standard library. It serves the page and sends a prompt to Ollama.
Frontend: one plain HTML file with a small form. It remembers what is near you, so you don't retype it every time.

The prompt does the main work. It tells the model how much time you have, who is going, what is nearby and how much energy you have, then asks for one safe, simple activity under 150 words, so the plan stays quick to read.

Why Does Open Innovation Matter?

An app that sends you outside should work when you're outside. Because Gemma runs locally through Ollama, Grass Pass needs no account and no API key, and it works without an internet connection once the model is downloaded. That matters in places with weak signal, which is exactly where a walk often takes you.

What you type about where you live and what is near your home also stays on my own computer instead of going to someone else's server. It costs nothing to run, and I can switch to a smaller or bigger model by changing one setting, which let me match the model to my laptop. With a closed API I would be paying per request and sending my location details to someone else.

One honest limit: a small local model can make mistakes or suggest something that doesn't suit your area, so use common sense and stay safe outdoors.

Prize Categories

Best Use of Gemma: Gemma 3 runs locally through Ollama and is the entire AI core of Grass Pass. It writes every outdoor plan on my own computer.

Pantry Pal: a recipe helper that knows what my friend can't eat

Karthikpillai77 — Sun, 04 Oct 2026 13:06:36 +0000

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend

What I Built

Pantry Pal is a small web app that suggests a recipe from whatever ingredients you have at home, while avoiding the foods a specific person can't eat.

I built it for my friend Rahul, who is allergic to peanuts. Most recipes online don't take that into account, so he has to check every ingredient by hand before he cooks anything.

To use it, you type in the person's name, any foods they must avoid (for Rahul, peanuts), and what's in the kitchen. Pantry Pal then returns one simple recipe that takes under 30 minutes, with a short line confirming it follows those restrictions. The name and allergies are remembered in the browser, so they only need to be typed once.

Demo

Watch the demo here: https://drive.google.com/file/d/11AAUH9Q2Jrt7zD0SHBSBCxJ2BwjAdzdI/view?usp=sharing

Code

Karthikpillai77 / pantry-pal

How I Built It

I'm just starting out, so I kept the project small on purpose.

Model: Gemma 3, an open-weight model from Google.
Local inference: Ollama runs the model on my own computer.
Backend: one Python file (app.py) using only the standard library. It serves the page and sends the prompt to Ollama.
Frontend: one plain HTML file with a small form.

The prompt does the main work. It tells the model who it's cooking for, lists the foods to never use, and asks for one simple recipe plus a line confirming it's safe for those restrictions.

Why Does Open Innovation Matter?

Food allergies are personal health information. Because Gemma runs locally through Ollama, Rahul's allergies and what's in his kitchen never leave my computer. There's no account, no API key, and no data sent to a third party.

It also costs nothing to run and works offline once the model is downloaded. I can switch to a smaller or bigger model by changing one setting, which let me match the model to my laptop. With a closed API I'd be paying per request and sending health details to someone else's server.

One honest limit: a small local model can make mistakes, so this is a helper and not a medical tool. Anyone with a serious allergy should still check ingredient labels themselves.

Prize Categories

Best Use of Gemma: Gemma 3 runs locally through Ollama and is the entire AI core of Pantry Pal. It reads the person's allergies and the available ingredients, then writes a safe recipe, all on my own computer.