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

  1. User Google AI Studio and OpenCode for vibe coding
  2. They way to set up local environment to test backend and frontend features
  3. 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

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