Forem: sumit sharma

ZingQR: A Minimalist QR Code Generator Built with React

sumit sharma — Sat, 25 Apr 2026 17:48:23 +0000

Introduction

**
In today’s fast-moving digital world, QR codes have become an essential tool for sharing information quickly and efficiently. Whether it’s for websites, payment links, contact details, Wi-Fi access, or business promotions, QR codes make communication simple and instant.
To solve this need in a clean and user-friendly way, I built ZingQR, a minimalist QR Code Generator designed with simplicity, speed, and modern UI in mind.
ZingQR allows users to generate QR codes instantly without unnecessary complexity. The goal was to create a lightweight and professional web application that focuses on usability while maintaining an attractive visual experience.
👉 Live App: https://zingqr.vercel.app
**

Why I Built ZingQR

**
Many QR code generators available online are overloaded with ads, confusing layouts, or paid restrictions. I wanted to build something different:

Fast and lightweight
Clean and modern design
No signup required
Easy QR code generation
Smooth user experience
Mobile-friendly responsive layout

The idea was simple: users should be able to visit the site, enter their content, generate a QR code, and download it within seconds.
**

Technologies Used

**
I used modern frontend technologies to ensure performance and scalability:

React.js for building the user interface
Vite for fast development and optimized performance
Tailwind CSS for clean and responsive styling
QR Code Generation Library for dynamic QR code creation
Framer Motion / Animations for smooth UI interactions

These tools helped create a professional and scalable product with excellent performance.
**

Key Features

Instant QR Code Generation Users can enter text, URLs, contact info, or other content and instantly generate a QR code in real time.
Customization Options Users can personalize their QR codes with options like:

Color customization
Size adjustment
Simple styling controls

This improves flexibility for both personal and business use.

Download Support Generated QR codes can be downloaded easily for future use in marketing materials, presentations, or sharing.
Responsive UI The application works smoothly across desktop, tablet, and mobile devices.
Clean Landing Page The homepage is designed to look modern, premium, and trustworthy while clearly explaining the product. **

Challenges Faced

One of the major challenges was creating a visually appealing UI without making the application feel heavy or slow.
Another challenge was handling real-time QR generation while keeping performance smooth across devices.
I also focused on balancing minimal design with enough features so the application feels useful without becoming complicated. **

What I Learned

This project helped me improve my skills in:
React component structure
State management
Responsive design principles
UI/UX thinking
Deployment and production optimization
Building real-world user-focused applications

More importantly, I learned how simplicity often creates the best user experience.
**

Future Improvements

**
I plan to add more advanced features such as:

Dynamic QR codes
Analytics tracking
User history
Logo embedding inside QR codes
Social media QR templates
Business card QR generation

These improvements can make ZingQR even more powerful for professional users.
**

Final Thoughts

**
ZingQR is more than just a QR generator—it represents my approach to building practical, clean, and useful products.
As a developer, I believe projects should solve real problems while delivering a smooth experience. This project reflects that mindset.
Building ZingQR taught me that great products are not always the most complex ones—they are often the simplest, fastest, and most user-friendly.
If you're interested in frontend development, product design, or building useful web applications, projects like this are a great way to grow.
Sometimes, small ideas create the biggest impact.
ZingQR is one of those ideas.

Try It Yourself

**
👉 Live App: https://zingqr.vercel.app 👉 GitHub: https://github.com/Sumitsharma31/ZingQR

If you find it helpful, a ⭐ on GitHub means the world to me. Contributions, feedback, and ideas are always welcome!

I built a full-stack AI blogging platform "gen-Blog" that writes, images, and publishes its own posts

sumit sharma — Sat, 18 Apr 2026 20:27:04 +0000

What if a blog could write itself?

That was the question I started with. Six weeks later, I shipped GenBlog — a full-stack AI-assisted content platform that generates complete, SEO-optimized articles (with a relevant featured image, tags, and meta description) from a single topic prompt. Or on a schedule, with zero human input.

Live demo: gen-blog.vercel.app

The problem I wanted to solve

**
Content-driven websites have a brutal cold-start problem. You need posts to get traffic, but writing quality posts takes hours. Tools like ChatGPT help, but you're still copy-pasting into a CMS, sourcing images manually, writing your own meta descriptions, and publishing by hand.

I wanted a system that handles all of that automatically — and still produces content that actually looks good and reads well.

What GenBlog does

Give it a topic. It returns a full article — structured markdown, a title, a slug, tags, a meta description, and a contextually relevant high-resolution featured image. You can trigger this manually from the admin dashboard, or let a cron job fire it automatically on a schedule.

Generated drafts land in the admin portal where you can review, edit, publish, or delete. Published posts are immediately live on the reader-facing frontend with full SEO metadata and beautiful markdown rendering.

The stack
Frontend — Next.js 16 (App Router) with React 19, Tailwind CSS 4, React Markdown with GFM support, and Next Themes for dark/light mode. State is handled purely with React hooks and context — no Redux, no Zustand. Icons are Lucide React for consistency. API calls go through Axios with custom interceptors.

Backend — Express.js running on Node.js v22+. MongoDB + Mongoose for data persistence. JWT for secure admin authentication. Node-Cron for scheduled generation. The two external APIs doing the heavy lifting are Google Gemini Pro for content generation and Unsplash for images.

Deployment — Frontend lives on Vercel (edge network, seamless Next.js integration). Backend is a Web Service on Render so the cron jobs keep running persistently and don't get killed by serverless cold starts.

How the AI pipeline works

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When a generation is triggered (manually or by cron), the backend service builds a detailed prompt and sends it to the Gemini Pro API. The prompt asks for structured output — a title, meta description, comma-separated tags, and a long-form markdown body. Once the content comes back, a second call goes to the Unsplash API using the generated title as the search query to find the best-matching featured image. Both pieces get assembled into a Mongoose document and saved as a draft.

The whole thing takes about 8–12 seconds end-to-end. Not instant, but fast enough that the admin UX doesn't feel painful.

Things I learned the hard way

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Prompt engineering matters a lot. Early versions returned inconsistently formatted markdown — sometimes with extra preamble, sometimes with missing sections. I eventually added strict output formatting instructions and a few few-shot examples directly in the system prompt. Quality improved dramatically.

Cron jobs and serverless don't mix. I started with a serverless function on Vercel for the backend. The cron would schedule fine but the function would time out or get killed mid-generation. Moving to a persistent Render Web Service fixed this entirely.

Unsplash rate limits are real. The free tier is 50 requests/hour. For a blog that auto-generates posts, that ceiling can come up fast. I added basic caching on the image lookup so repeated topics don't burn unnecessary requests.

SEO from day one. Next.js App Router makes dynamic metadata generation clean, but you have to plan your data model around it. I made sure every post has a slug, meta description, and OG image field from the very first schema design — retrofitting it later would have been messy.
**

Project structure

**
GenBlogs/ ├── frontend/ │ ├── src/app/ # App Router pages & layouts │ ├── src/components/ # UI components │ └── src/lib/ # Axios client & utilities └── backend/ ├── src/models/ # Mongoose schemas ├── src/routes/ # API endpoints ├── src/services/ # Gemini & Unsplash logic └── src/index.js # Entry point

What's next

**A few things I'm planning to add:

Multi-model support — let users choose between Gemini, Claude, and GPT for generation
Content quality scoring before auto-publish (checking readability, length, keyword density)
Newsletter integration — auto-send a digest when new posts go live
Analytics dashboard to track which AI-generated topics actually get traffic.

If you've built something similar or have opinions on the Gemini vs GPT-4 quality tradeoffs for long-form content, I'd love to hear them in the comments. And if you check out the live site, let me know what you think!

gen-blog.vercel.app