SnapList AI

Amrit Raj — Fri, 16 Oct 2026 19:46:14 +0000

What I Built

SnapList AI is an open-source AI tool that helps indie crafters, vintage eBay sellers, and Etsy artisans create high-quality product listings from simple notes and photos. It reduces costs by using open-weight AI models instead of relying entirely on expensive proprietary APIs.

Demo

money-fnfb.onrender.com

Code

Amrit42-am / Money

SnapList AI ⚡

Open-Source AI Side Hustle: E-Commerce Listing & SEO Optimization Engine
Built for the DEV Open-Source AI Side Hustle Challenge • Ready for 1-Click Render Deployment.

💡 The Problem & The Side Hustle Opportunity

Every day, millions of indie creators, Etsy artisans, flea-market pickers, and eBay flippers spend countless hours drafting product titles, guessing SEO tags, and writing descriptions.

While closed proprietary AI tools (e.g., commercial SaaS wrappers) charge $0.02 - $0.06 per listing generation and lock merchants into opaque monthly subscriptions, these recurring costs erode the thin margins of a side hustle. Furthermore, merchants are forced to upload unreleased prototype designs, personal inventory notes, and supplier pricing to closed servers that use their data for model training.

SnapList AI solves this by putting open-source AI at the core of the business:

High-Converting Listings in Seconds: Generates marketplace-tailored titles, 13 search tags (Etsy algorithm compliant), conversion…

View on GitHub

How I Built It

SnapList AI uses Node.js, Express, HTML, CSS, and JavaScript. It supports open-weight AI models like Llama, Mistral, and Qwen through Hugging Face, Ollama, and vLLM. A built-in fallback engine ensures listings can be generated even without an AI API token.

Why Does Open Innovation Matter?

Lower Costs: Reduces AI expenses for small sellers.
Privacy: Local models help keep product data private.
Flexibility: Users can switch between different AI models.
Offline Support: Local models work without an internet connection.

My Agent Session

I built SnapList AI with Google Antigravity, which helped create the Express backend, integrate multiple AI models, design the glassmorphic frontend and ROI calculator, and configure Render deployment.

My First User

I tested SnapList AI with a friend who sells vintage electronics on eBay. For a 1979 Sony Walkman, it generated an SEO-friendly title, 13 search tags, and a detailed product description in seconds.

My friend said it reduced listing preparation time from 10–15 minutes per item and saved hours when listing 20 items, without requiring a paid subscription.

Prize Categories

Best Use of Render -> SnapList AI is designed for deployment on Render and uses a render.yaml blueprint for configuration and deployment.

StepWise DSA: Stop Copying Solutions. Start Solving.

Amrit Raj — Sun, 04 Oct 2026 17:18:00 +0000

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

What I Built

I built StepWise DSA, an AI-powered Data Structures & Algorithms learning mentor.

I built it for a friend who was preparing for software engineering interviews and regularly practiced problems on platforms like LeetCode. The biggest issue was that whenever they got stuck and asked an AI for help, it would often provide the complete solution immediately.

That helps you finish the problem, but it doesn't necessarily help you learn how to solve the next one.

StepWise takes a different approach.

Instead of immediately giving the answer, it guides the learner through the problem using progressive hints.

It has two main modes:
Approach Mode — breaks a DSA problem into logical steps and helps the learner discover the algorithm themselves.
Code Hint Mode — analyzes the learner's own code and gives targeted hints about bugs, logic, complexity, or edge cases without rewriting the solution for them.

The goal is simple:
Don't give me the answer. Help me reach it.

Demo

stepwise-jdb8.onrender.com

Code

Amrit42-am / StepWise

StepWise DSA

Tagline: "Learn the approach, not the answer."

StepWise DSA is an AI-powered DSA learning mentor. It helps students understand how to solve DSA problems without immediately giving them the final answer. The application has two core modes:

Approach Mode: Guides the student's thinking stage-by-stage.
Code Hint Mode: Analyzes student code to provide bug hints, complexity analysis, and edge cases.

Each learner registers an account and signs in. Every learning session, hint, submitted code revision, and conversation is saved to that authenticated account in MongoDB.

Tech Stack

Frontend: React + Vite
Backend: Node.js + Express
Database: MongoDB Atlas
AI: Gemma open-weight model (via local Ollama) or Google Gemini API.

Project Structure

/client - React frontend
/server - Express backend with AI service
/render.yaml - Render deployment configuration

Local Development Setup

1. MongoDB Setup

Create a MongoDB Atlas cluster.
Copy your connection string.
Set…

View on GitHub

How I Built It

StepWise is a full-stack web application built with React, Vite, Node.js, Express, and MongoDB Atlas.

The frontend handles the learning experience, while the Express backend manages authentication, AI requests, conversation state, and hint progression.

MongoDB Atlas stores user accounts, learning sessions, submitted code, and hint history.

The core of the application is an open-weight Qwen model accessed through Groq.

I designed a custom AI mentoring system around a Socratic teaching approach. Instead of allowing the model to immediately generate a solution, the backend maintains a Hint Level from 1 to 5.

The AI starts with a conceptual hint and becomes progressively more specific only when the student asks for additional help.

For example:
Hint 1: Look at how many times your code searches through the array.

Then:
Hint 2: Can you avoid repeating that search?

And finally:
Hint 3: Think about a data structure that allows fast lookup.

The student still has to make the final connection and write the solution themselves.

The application also keeps track of the conversation context so the AI knows what hints have already been given and doesn't randomly jump straight to the answer.

Why Does Open Innovation Matter?

Open innovation matters because the goal of StepWise isn't simply to generate code — it's to control how AI teaches.

Most AI coding assistants are optimized to be helpful by solving a user's problem as quickly as possible. For learning DSA, that can be counterproductive.

Using an open-weight model gives me more control over the model's behavior and lets me design a different interaction:
AI as a mentor, not an answer generator.

The model can be adapted through system instructions, prompting strategies, and application-level logic to enforce progressive hints and protect the student's problem-solving process.

It also gives the project more flexibility to change models or potentially move inference to a local environment instead of being completely locked into one closed AI provider.

My Agent Session

I built StepWise using an iterative AI-assisted development workflow, using AI to help develop and test the mentor logic, API flow, and progressive hint system.

Prize Categories

Best Use of Render
StepWise is deployed on Render, hosting the full-stack application so that the project can be accessed and used through a public URL.

Best Use of MongoDB Atlas
MongoDB Atlas is the application's persistent data layer. It stores user accounts, DSA sessions, hint history, submitted code, and learning progress.

DEV Community: Amrit Raj

SnapList AI

What I Built

Demo

Code

Amrit42-am / Money

SnapList AI ⚡

💡 The Problem & The Side Hustle Opportunity

How I Built It

Why Does Open Innovation Matter?

My Agent Session

My First User

Prize Categories

StepWise DSA: Stop Copying Solutions. Start Solving.

What I Built

Demo

Code

Amrit42-am / StepWise

StepWise DSA

Tech Stack

Project Structure

Local Development Setup

1. MongoDB Setup

How I Built It

Why Does Open Innovation Matter?

My Agent Session

Prize Categories