𝗕𝗲𝘆𝗼𝗻𝗱 𝟭𝟱𝟬𝗺𝘀: 𝗛𝗼𝘄 𝗜 𝗥𝗲𝗱𝘂𝗰𝗲𝗱 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 𝗳𝗼𝗿 𝗮 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗔𝗜 𝗩𝗼𝗶𝗰𝗲 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁
Live coding and technical interviews cause stress for developers. Most people struggle when an expert watches every line of code in a shared IDE.
Generative AI changes this. You can now simulate real interview scenarios through interactive practice.
I spent months building SaaS tools for recruiting. I faced one major problem: network latency. To build a smooth AI interview assistant, the response time must stay under 150ms.
Humans perceive any delay over 200ms as awkward. To stay under the limit, the entire pipeline must move fast: • Audio capture • Streaming • LLM inference • Text-to-Speech • Audio playback
Standard HTTP requests are too slow for this task. You need to process data on the client side.
Voice Activity Detection (VAD) is the first hurdle. You must know exactly when a user starts and stops talking. This prevents sending silent audio to your server.
I used a JavaScript AudioWorklet to solve this. This moves raw PCM audio processing to a separate thread. It keeps the main UI thread free. This means the AI stays active in the background without slowing down the user's browser or IDE.
Real-time code analysis is another challenge. The system must understand both audio and the code in the editor. By using WebSockets, I sync text editor data with voice input. This allows the AI to detect bugs or suggest optimizations as the user types.
If you want to prepare for technical interviews, try these steps:
- Practice thinking aloud. Explain your logic while you code.
- Use AI simulations. Get reports on your response times and code fluidity.
Building low-latency voice apps requires a balance between audio compression and server power.
How do you handle audio streaming in your projects? Do you use VAD models in the browser?
Share your thoughts in the comments.