Build Log: AI Voice Assistant
From idea to production in 7 days
This is a transparent look at how I built the Gemini-powered Voice Assistant. Not a tutorial — just proof of thinking and execution.
Problem Definition & Architecture
- ├─Identified gap: voice assistants sacrifice privacy for convenience
- ├─Decided on offline-first approach with cloud AI fallback
- ├─Chose Next.js + TypeScript for production-grade PWA support
- ├─Generated initial project structure via prompting
- ├─Suggested Web Speech API vs Whisper WASM tradeoffs
- ├─Created TypeScript interfaces for voice state management
Chose hybrid approach: offline speech recognition (Web Speech API) + cloud AI (Gemini) for responses. Privacy for listening, intelligence for responding.
Gemini Integration & Voice Pipeline
- ├─Designed conversation state machine
- ├─Wrote Gemini prompt engineering for assistant persona
- ├─Debugged audio context issues across browsers
- ├─Generated Gemini API integration code
- ├─Created Web Speech API hooks and utilities
- ├─Built audio visualization components
Implemented streaming responses from Gemini for natural conversation flow. AI suggested this pattern after initial batch response felt robotic.
PWA & Offline Architecture
- ├─Designed service worker caching strategy
- ├─Tested offline scenarios and fallback states
- ├─Optimized bundle for mobile performance
- ├─Generated PWA manifest and service worker
- ├─Created WebWorker setup for Whisper WASM
- ├─Built glassmorphism UI components
Opted for Web Speech API as primary (better browser support) with Whisper WASM as experimental alternative for true offline.
Polish, Test, Deploy
- ├─Cross-browser testing (Safari issues resolved)
- ├─Refined Gemini prompts for better responses
- ├─Deployed to Vercel with environment configuration
- ├─Debugged Safari-specific Web Speech quirks
- ├─Suggested bundle size optimizations
- ├─Generated README and documentation
Added visual feedback for voice activity after user testing showed confusion about listening state.
Build Metrics
AI didn't replace the thinking — it amplified it. The architecture decisions, debugging strategy, and UX refinements were human. AI accelerated the execution.