// Just speak naturally to get instant help: 🎤 "How do I implement authentication in Next.js 14?" 🤖 "For Next.js 14 authentication, I recommend using NextAuth.js v5..." 🎤 "Show me React Server Components best practices" 🤖 "React Server Components should be used for data fetching..." 

# Real conversations with TechMentor Voice: 🎤 "How do I optimize my Next.js app for production?" 🤖 "For Next.js production optimization, focus on these key areas: First, enable static generation where possible using generateStaticParams. Second, implement proper image optimization with next/image component..." 🎤 "Debug TypeScript interface inheritance issues" 🤖 "TypeScript interface inheritance issues often stem from property conflicts. Use intersection types with & operator for combining interfaces, or extend with proper overrides..." 🎤 "Best practices for React useEffect cleanup" 🤖 "useEffect cleanup prevents memory leaks. Return a cleanup function for subscriptions, timers, and event listeners. Here's the pattern: useEffect(() => { const subscription = subscribe(); return () => subscription.unsubscribe(); }, []);" 

Transcription Latency: ~300ms # AssemblyAI Universal-Streaming Context Retrieval: ~200ms # Context7 MCP integration  AI Processing: ~500ms # Gemini 2.0 Flash Voice Synthesis: ~300ms # ElevenLabs TTS ──────────────────────────────── Total End-to-End: ~800ms # Sub-second responses! 

Voice Input → AssemblyAI Universal-Streaming → Context7 MCP → Gemini 2.0 Flash → ElevenLabs TTS → Audio Output 

// Real-time WebSocket connection to Universal-Streaming v3 const wsUrl = `wss://streaming.assemblyai.com/v3/ws?api_key=${apiKey}`; const ws = new WebSocket(wsUrl); // Configure for optimal voice agent performance  const config = { type: 'configure', format_turns: true, // 🎯 Enhanced turn detection punctuate: true, // 📝 Automatic punctuation  end_utterance_silence_threshold: 1500, // ⏱️ Smart endpointing voice_activity_detection: true // 🔊 Advanced VAD }; // Process immutable transcripts with intelligent turn detection ws.onmessage = (event) => { const data = JSON.parse(event.data); // Critical: Prevent audio feedback loops if (isAISpeakingRef.current) { console.log('🔇 Ignoring transcript - AI is speaking'); return; } if (data.end_of_turn && data.transcript.trim()) { // Process complete developer questions processVoiceQuery(data.transcript); } }; 

// Audio feedback prevention system const isAISpeakingRef = useRef(false); const speakResponse = async (text: string) => { console.log('🔊 Starting AI response'); isAISpeakingRef.current = true; // CRITICAL: Stop listening while AI speaks await stopMicrophoneTemporarily(); try { // ElevenLabs TTS with proper cleanup const audioBlob = await generateSpeech(text); await playAudioWithCallback(audioBlob); } finally { // Resume listening after AI finishes isAISpeakingRef.current = false; setTimeout(resumeListening, 500); // Prevent echo } }; // WebSocket message filtering during AI speech ws.onmessage = (event) => { if (isAISpeakingRef.current) return; // 🛡️ Feedback protection processTranscript(event.data); }; 

// Smart library detection and documentation retrieval async function getRelevantDocumentation(query: string) { // 1. Detect frameworks/libraries from voice query const detectedLibraries = extractTechnicalTerms(query); // 2. Query Context7 MCP for live documentation const mcpResponse = await fetch('https://mcp.context7.com/mcp', { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify({ jsonrpc: '2.0', method: 'tools/call', params: { name: 'get-library-docs', arguments: { context7CompatibleLibraryID: detectedLibraries[0], tokens: 3000, topic: extractTechnicalTopic(query) } } }) }); // 3. Score and rank documentation chunks return scoreDocumentRelevance(query, documentation); } // Technical term extraction optimized for voice function extractTechnicalTerms(voiceQuery: string): string[] { const techPatterns = { 'next.js': /\b(next\.?js|nextjs)\b/i, 'react': /\breact\b/i, 'typescript': /\b(typescript|ts)\b/i, 'node.js': /\b(node\.?js|nodejs)\b/i, 'python': /\bpython\b/i }; return Object.keys(techPatterns).filter(lib => techPatterns[lib].test(voiceQuery) ); } 

const DOMAIN_EXPERT_PROMPT = ` You are TechMentor Voice, a specialized AI assistant for developers. EXPERTISE AREAS: - Modern JavaScript/TypeScript development - React, Next.js, Node.js ecosystems - Python, Django, FastAPI backends - Database design and optimization - DevOps, Docker, Kubernetes - Cloud platforms (AWS, Vercel, Cloudflare) VOICE-OPTIMIZED RESPONSES: 1. **Conversational**: Speak naturally as if pair programming 2. **Concise**: 100-200 words maximum for voice delivery 3. **Practical**: Include actionable code examples 4. **Current**: Focus on modern best practices 5. **Structured**: Clear transitions between concepts TECHNICAL RESPONSE FORMAT: - Start with direct answer - Provide brief code example if relevant - Explain reasoning behind recommendations - Suggest next steps or related concepts Remember: Users are SPEAKING to you and will HEAR your response. Make it conversational yet technically accurate. `; 

// Professional audio configuration for clear technical discussions const audioConfig = { sampleRate: 16000, // Optimal for speech recognition channelCount: 1, // Mono for efficiency  echoCancellation: true, // Prevent feedback noiseSuppression: true, // Clear technical terms autoGainControl: true // Consistent volume }; // Real-time PCM16 conversion for Universal-Streaming const convertFloat32ToPCM16 = (float32Array: Float32Array): ArrayBuffer => { const pcm16Array = new Int16Array(float32Array.length); for (let i = 0; i < float32Array.length; i++) { pcm16Array[i] = Math.max(-32768, Math.min(32767, float32Array[i] * 32768)); } return pcm16Array.buffer; }; // Audio processing with technical term optimization processorRef.current.onaudioprocess = (event) => { if (wsRef.current?.readyState === WebSocket.OPEN && !isAISpeakingRef.current) { const inputData = event.inputBuffer.getChannelData(0); const pcmData = convertFloat32ToPCM16(inputData); wsRef.current.send(pcmData); // Send to AssemblyAI } }; 

// Parallel processing for minimal latency async function processVoiceQuery(transcript: string) { const startTime = Date.now(); // Parallel execution of context retrieval and AI processing const [contextResult] = await Promise.allSettled([ getRelevantDocumentation(transcript), // ~200ms // Pre-warm Gemini connection during context fetch ]); const contextTime = Date.now() - startTime; // Process with Gemini using retrieved context const aiResponse = await processWithGemini(transcript, contextResult); const totalTime = Date.now() - startTime; // Performance logging for optimization console.log(`⚡ Total processing: ${totalTime}ms`); return aiResponse; } 

// Graceful fallbacks for each component const errorHandling = { universalStreaming: "Auto-reconnection with status indicators", context7MCP: "Graceful fallback to general knowledge", geminiAPI: "Comprehensive error responses with retry logic", ttsServices: "Automatic fallback from ElevenLabs to Web Speech" }; 

// The future of developer assistance is here const developer = new TechMentorVoice(); await developer.ask("How do I optimize this React component?"); // 🎤 → 🧠 → 💬 → 🚀