How To Deploy Backend For FREE Using Render (Node.js + GitHub)

Updated 2026 8 min read Deployment · Node.js

Deploy your Node.js backend completely free — no credit card, no hidden costs. Learn how to use GitHub + Render to host your API in minutes. Perfect for portfolio projects, side hustles, and production staging.

Step 1: Prepare Your Node.js Code & Push to GitHub

Make sure your project has a package.json and a start script (e.g., "start": "node server.js"). Initialize a Git repository and push to GitHub (public or private). Render supports both.

Step 2: Connect Render with Your Repository

Go to render.com → Sign up with GitHub. Click "New +" → "Web Service". Select your repo. Render auto-detects Node.js environment.

                    
                    Pro tip: In the Render dashboard, set build command: npm install and start command: npm start. Add environment variables securely.
                

Step 3: Deploy & Enjoy Auto SSL

Click "Create Web Service". Render will build and deploy your backend within minutes. Every new git push auto redeploys. You'll receive a public URL like https://yourapp.onrender.com with automatic HTTPS.

Why Render Is Perfect For Developers

Render offers a generous free tier: 750 hours of active service per month (enough for one small web service), free SSL, global CDN, and GitHub integration. It supports Node.js, Python, Docker, Go, and more. Ideal for launching your backend at $0 cost while building your portfolio.

Next steps: Once your backend is live, connect a frontend (React, Next.js) or a database like MongoDB Atlas. You're now ready to share your API with the world.

Build Modern Login & Signup with Email Verification using Supabase 🔐

Download from Google Drive

June 2026 12 min read Supabase · Authentication

🔥 Build a production-ready authentication system with Supabase! Email verification, modern UI, popup notifications, and dashboard after login. Perfect for beginners & portfolio projects.

✅ Signup / Login ✅ Email Verification ✅ Popup Notifications ✅ Responsive UI ✅ Supabase Auth ✅ Dashboard after login

✨ What You'll Build

Complete authentication flow (Sign up, Log in, Email verification)
Automatic redirect to user dashboard after email confirmation
Secure session management with Supabase
Toast notifications for better UX

🛠 Technologies Used

HTML5, CSS3, JavaScript (ES6), Supabase (Authentication & Database). All frontend, no backend code required.

                    
                    📥 Download the complete source code: Click the "Download from Google Drive" button above to get the complete HTML file. Just replace with your Supabase credentials and it's ready to use!
                

Google Drive direct link: The project file is hosted securely on Google Drive. Click the button or open preview.

📌 What You Will Learn

✔ How to connect Supabase to any frontend project
✔ How to implement email verification system
✔ How to handle user sessions and protected routes
✔ How to build beautiful responsive authentication forms
✔ How to show toast notifications for better UX

📺 Watch the full video above

Follow along with the step-by-step YouTube tutorial. Don't forget to LIKE 👍, SHARE 🚀 and SUBSCRIBE ❤️ for more web development tutorials!

#supabase #javascript #webdevelopment #loginpage #signupform #authentication #emailverification #htmlcssjavascript

Build Your First AI Model: House Price Prediction System 🏠🤖 ML Project

Source Code (Coming Soon)

March 2026 16 min read Machine Learning · Flask · AI

🚀 Build Your First AI Model with a Real Project! In this video, we create a House Price Prediction System using Machine Learning from scratch. Train a Linear Regression model, build Flask backend, connect HTML frontend, and predict house prices in real time. Perfect for beginners, B.Tech students, and AI enthusiasts.

🏠 Area Based Prediction 🛏 Bedrooms + 🚿 Bathrooms 📍 Location Score 🤖 Linear Regression 🧠 Flask Backend 💾 Joblib Model Persistence

📌 What You'll Learn & Build

Understand features & labels for regression problem
Train a Linear Regression Model using Scikit-Learn
Save & load the model using Joblib
Create Flask backend API endpoints for predictions
Design responsive HTML form to collect house details
Connect frontend with AI model → Real-time price prediction

🛠️ Technologies Used

Python · NumPy · Scikit-Learn · Linear Regression · Flask · HTML/CSS · Joblib

This end-to-end project will give you hands-on experience in building full-stack AI applications. You will learn how machine learning models interact with web interfaces and serve predictions in real-time.

                    
                    🎯 Project Highlights: Dataset creation from scratch, training regression model, saving with joblib, Flask routes, and an interactive web form where users can input square feet, bedrooms, bathrooms, location score, and get instant AI-powered price prediction.
                

📖 Chapters (Video Timestamps)

00:00 – Introduction to House Price Prediction
01:15 – Dataset Creation & Feature Engineering
04:30 – Linear Regression Model Explanation
07:20 – Training the Model with Synthetic Data
09:10 – Saving Model with Joblib
10:40 – Flask Backend & API Route Setup
13:30 – Designing HTML Frontend Form & Styling
15:40 – Live Prediction Demo & Integration
16:30 – Conclusion & Next Steps

🎓 Who Is This For?

✔ Absolute beginners in Machine Learning
✔ College students looking for real-world AI projects
✔ B.Tech / MCA students who want to showcase portfolio
✔ Developers curious about integrating AI with web apps
✔ AI enthusiasts wanting to understand Linear Regression practically

📥 Source Code Status: The complete project code (Python scripts + Flask app + HTML templates) will be available soon. Stay tuned and subscribe to Tech By Sambhav for the latest updates.

💡 Key Takeaways

After building this project, you'll understand the complete lifecycle of an ML project: data preparation → model training → serialization → backend integration → frontend consumption. This is exactly how real-world AI products are built. By the end, you'll have your own House Price Estimator that you can showcase on your resume or GitHub.

Pro Tip: Extend this project by using real housing datasets (like Boston Housing or California Housing), add more features (garage, year built), or deploy the final Flask app on Render using the first tutorial of this page!

#machinelearning #housepriceprediction #linearregression #flask #python #ai #mlproject #techbysambhav