说汉语
AI-POWERED CHINESE LEARNING PLATFORM
A comprehensive full-stack Chinese learning application built with Next.js 15, TypeScript, and OpenAI Realtime API. Designed for beginners learning HSK1 and HSK2 vocabulary through interactive flashcards, AI conversation practice, typing drills, and timed assessments with real-time speech-to-speech translation.
Learning Chinese is notoriously difficult for beginners. Traditional methods like textbooks and classroom instruction lack interactive practice, immediate feedback, and real-time conversation opportunities.
Beginners struggle with pronunciation, character recognition, and conversational confidence. Existing language apps offer limited AI conversation features, lack HSK-focused curriculum, or don't support real-time voice-to-voice translation.
There was a clear need for an AI-powered platform that combines structured HSK vocabulary learning with natural conversation practice and multi-modal learning approaches.
Shuo Hanyu is a comprehensive AI-powered learning platform that integrates OpenAI's cutting-edge Realtime API for natural voice-to-voice conversation practice in 15 languages.
The platform combines interactive flashcards with spaced repetition, typing practice with live AI feedback, a complete HSK dictionary, and timed assessments—all designed specifically for HSK1 and HSK2 learners.
Using vector embeddings and RAG technology, the application delivers contextual, AI-generated learning content while tracking user progress across all modules for a personalized learning journey.
HSK1 and HSK2 vocabulary flashcards with AI-generated content from vector embeddings and spaced repetition learning.
Revolutionary voice-to-voice translation using OpenAI Realtime API with support for 15 languages and natural conversation flow.
Translation drills with real-time AI suggestions and adaptive difficulty for improving written Chinese proficiency.
Complete HSK1-HSK2 vocabulary database with searchable word list and audio pronunciation.
Challenge your vocabulary knowledge with timed quizzes and instant performance feedback.
Comprehensive progress tracking with persistent data and analytics insights powered by Amplitude.
Cutting-edge voice-to-voice translation with real-time speech streaming. Natural conversation flow with ultra-low latency for immersive language practice.
Vector embeddings stored in Parquet files enable semantic search and AI-generated contextual flashcards. LangChain integration for enhanced contextual responses.
OpenAI GPT-4 provides intelligent translation feedback, live corrections, and adaptive suggestions throughout all learning modules.
Built with Next.js 16 App Router and React 19 for optimal performance. TypeScript ensures type safety across the codebase. Tailwind CSS 4 provides utility-first styling with stone/neutral theme. Radix UI components for accessibility.
OpenAI Realtime API handles voice-to-voice translation with WebSocket connections. GPT-4 powers translation feedback and corrections. Vector embeddings (via Python/pandas) enable RAG-based learning. LangChain orchestrates AI responses.
Supabase provides PostgreSQL database and authentication. Next.js API Routes handle AI integrations and translations. React Context API manages user state and progress. Amplitude tracks engagement analytics.
Implementing OpenAI Realtime API required handling WebSocket connections, audio streaming, and managing state for bidirectional voice communication. Solved with careful connection management and error recovery.
Managing user progress across flashcards, typing practice, conversation, and assessments required careful state design. Used React Context with Supabase persistence for seamless experience.
Processing HSK vocabulary into vector embeddings for RAG required Python pipeline with pandas and pyarrow. Stored in Parquet format for efficient semantic search and AI flashcard generation.
Optimizing for React 19 and Next.js 16 while handling AI API calls, audio streaming, and real-time updates. Used code splitting, lazy loading, and efficient caching strategies.
Working with OpenAI's brand-new Realtime API provided invaluable experience in real-time voice streaming, WebSocket management, and building natural conversation interfaces.
Implementing vector embeddings for semantic search taught me how to build intelligent, context-aware AI features. The RAG approach dramatically improved flashcard relevance.
Building for learners on-the-go required careful UX design for mobile devices. Voice features work seamlessly on phones with optimized audio handling.
Supporting 15 languages required thoughtful architecture for language pairs, translation pipelines, and UI internationalization. Built scalable patterns for future expansion.
Amplitude integration revealed user behavior patterns that guided feature prioritization. Data showed conversation practice was most engaging, informing product roadmap.
Latest Next.js and React versions enabled incredible performance with App Router, Server Components, and optimized rendering. Perfect for AI-heavy applications.
说汉语
Experience AI-powered language learning with real-time conversation practice