𝗕𝗲𝘆𝗼𝗻𝗱 𝟭𝟱𝟬𝗺𝘀: 𝗛𝗼𝘄 𝗜 𝗥𝗲𝗱𝘂𝗰𝗲𝗱 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 𝗳𝗼𝗿 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗔𝗜 𝗩𝗼𝗶𝗰𝗲 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁𝘀
Live coding and technical interviews are stressful for developers. Watching someone evaluate your every line of code causes pressure.
Generative AI now changes this. You can use AI to simulate real interview scenarios.
I built an AI assistant for interviews. My goal was to keep the response time under 150ms.
Human conversation feels awkward if there is a pause longer than 200ms. To make an AI feel human, the entire pipeline must be fast. This includes audio capture, streaming, LLM inference, and text-to-speech.
Standard HTTP requests are too slow for this. You need to process data on the client side.
The first problem is Voice Activity Detection (VAD). You must know exactly when a user starts and stops talking. This prevents sending silence to your server.
I used an AudioWorklet in JavaScript to handle raw PCM samples in a separate thread. This keeps the main UI thread free. It ensures the AI assistant does not slow down the browser or the code editor.
Another challenge is real-time code analysis. The system must understand both audio and the state of the code editor.
By using WebSockets to combine text editor data with voice input, the AI can detect bugs or edge cases as the user types.
If you want to practice for interviews, try these steps:
• Practice the "Think Aloud" method. Explain your logic out loud while you code. • Use AI simulations. This provides reports on your response times and code quality.
Low latency voice apps require a balance between audio compression and server power.
How do you handle audio streaming in your projects? Have you used VAD models in the browser?
Share your thoughts in the comments.
Optional learning community: https://t.me/GyaanSetuAi