Twinglish: Building a Social Platform for English Language Learners
Have you ever hesitated to post something online because you weren't sure if your English was correct? As someone who's witnessed countless non-native speakers struggle with this exact problem, I decided to build something that could help: Twinglish.
The Problem That Sparked Everything
The idea came from observing international students and colleagues who had brilliant ideas but were reluctant to share them publicly due to language barriers. They would spend 20+ minutes crafting a simple tweet, second-guessing every word choice and grammar structure.
What if there was a safe space to practice English writing? A place where you could post freely and receive instant, helpful feedback without judgment?
What is Twinglish?
Twinglish is a Twitter-style social platform designed specifically for English language learners. Users can:
Write short posts in English (280 characters max, just like Twitter)
Get instant AI corrections powered by OpenAI's GPT models
Learn from detailed explanations about why changes were suggested
Track their progress with streaks and achievements
Practice in a judgment-free environment focused on learning
Think of it as Twitter meets Grammarly, but specifically built for language learning with a social twist.
The Tech Stack Decision
For this project, I wanted to build something robust yet maintainable. Here's what I chose:
Backend: FastAPI + PostgreSQL
FastAPI for its excellent async support and automatic API documentation
PostgreSQL for reliable data persistence
Alembic for database migrations
Redis for caching and session management
OpenAI API for grammar corrections and explanations
Frontend: Next.js + React
Next.js for server-side rendering and great developer experience
Plain CSS with custom properties for theming (keeping it simple)
JWT authentication for secure user sessions
SWR for efficient data fetching
Deployment
- Docker Compose for local development
Key Features That Make It Special
1. Real-time Grammar Correction
When you submit a post, the OpenAI API analyzes your text and provides:
Corrected version with proper grammar
Detailed explanation of changes
Encouragement when your English is already perfect
2. Smart Filtering System
Users can filter their posts by:
All posts - Everything they've written
Perfect posts - Posts that needed no corrections
Posts with corrections - Learning opportunities
3. Progress Tracking
Daily writing streaks
Achievement system
Statistics on improvement over time
Personal progress dashboard
4. Mobile-First Design
Since many users access social platforms on mobile, I prioritized:
Responsive design that works beautifully on all devices
Touch-friendly interface
Fast loading times
Offline-first approach for better UX
Technical Challenges and Solutions
Challenge 1: OpenAI API Integration
Problem: Ensuring consistent, helpful responses from the AI while managing API costs.
Solution: I crafted specific prompts that encourage the AI to be constructive and educational. The system returns structured JSON with both corrections and explanations, making responses more predictable.
async def correct_tweet(original_text: str) -> Tuple[str, str]:
prompt = f"""
You are a helpful language learning assistant. Correct the following English text,
fixing any grammar, spelling, or punctuation errors. Then explain the corrections you made.
Original text: "{original_text}"
Provide your response in JSON format with two fields:
1. corrected_text: The corrected version
2. explanation: A friendly explanation of the changes and why they improve the text
"""
Challenge 2: Real-time User Experience
Problem: Users expect instant feedback, but OpenAI API calls can take 2-3 seconds.
Solution: Implemented optimistic UI updates with loading states and fallback responses for offline scenarios. The app gracefully handles API failures while keeping users engaged.
Challenge 3: Data Privacy and Security
Problem: Users are sharing personal thoughts and learning data.
Solution:
JWT-based authentication with secure token management
No storage of personal data beyond what's necessary
Clear privacy policies about AI processing
Option to delete all user data
What I Learned Building This
1. User Experience is Everything
The difference between a tool people use once and one they return to daily often comes down to small UX details. Things like:
Instant visual feedback when posting
Clear progress indicators
Encouraging messages for both perfect and corrected posts
2. AI Integration Requires Thoughtful Design
Working with OpenAI's API taught me that the prompt engineering is just as important as the code. The way you ask the AI to respond directly impacts user experience.
3. Mobile-First Really Matters
Over 80% of early testers accessed Twinglish on mobile devices. Building mobile-first wasn't just a nice-to-have—it was essential for adoption.
What's Next?
The roadmap includes:
Multi-language support (starting with Spanish and Portuguese)
Community features (following other learners, collaborative challenges)
Advanced analytics (personalized learning insights)
Native mobile apps for iOS and Android
Teacher dashboard for educators to track student progress
Try It Yourself
Twinglish is currently in private beta. If you're interested in trying it out or know someone who might benefit from it, feel free to reach out.
Final Thoughts
Building Twinglish reminded me why I love creating software: the opportunity to solve real problems for real people. Language learning is deeply personal, and technology can make it more accessible and less intimidating.
Every time I see a user post their first "perfect" tweet or complete their week-long streak, I'm reminded that sometimes the best projects come from simply paying attention to the struggles around us.
Want to see the code? Check out the GitHub repository or connect with me on Twitter to discuss the technical details.