Inspiration
Because I always found it hard to remember all passively consumed items from social media, I wanted to learn by active recall and actively correct based on my own mistakes. Reflexion turns language learning into a deliberate cycle of think-act-refine, breaking the illusion of knowledge from familiarity and building genuine fluency through personalized AI feedback.
What I learned
- User Google AI Studio and OpenCode for vibe coding
- They way to set up local environment to test backend and frontend features
- Use different AI models for different tasks
How IBuilt It
Built during a Gemini3 hackathon using cutting-edge technologies:
- AI Model: Google Gemini 3 Flash for core language processing
- Speech Analysis: Gemini 2.5 Flash TTS engine for pronunciation feedback
- Multimodal Processing: Real-time audio transcription and pronunciation evaluation
- Frontend: React with TypeScript for responsive UI
- Backend: Node.js with Express for production deployment
- Deployment: Google Cloud Run for seamless cloud hosting from Google AI Studio and GitHub Repository
- Development Tools: Vite for fast development cycles, Google AI Studio and Open Code for vibe coding workflow
🎯 Challenges & Solutions
Challenge 1: Balancing AI Feedback
Problem: Early AI feedback was either too generic or overly critical Solution: Fine-tuned prompts to emphasize "active correction" over rote examples Result: Improvement in user satisfaction with feedback quality
Challenge 2: Multilingual Support
Problem: Accurate pronunciation across different languages Solution: Language-specific models with phonetic analysis Result: Reliable pronunciation feedback for German, French, English, and Chinese
Built With
- gemini
- github
- google-ai-studio
- google-cloud
- node.js
- opencode
- react
- typescript
Log in or sign up for Devpost to join the conversation.