I watched 30 users talk to the same voice agent Same script. Same questions. The only thing I changed was the response latency: 300ms, 500ms, 800ms. At 300ms, people just talked. No awkward pauses, no confusion. One user didn't even realize it was an AI until I told her afterward. At 500ms, something shifted. Users started talking over the agent. They'd ask a question, wait half a second, then r
Building AI calling agents shouldn't require a commercial license or massive per-minute markups. If you are a Python developer, you should be able to spin up a sub-500ms latency voice agent on your own machine. Prerequisites Python 3.10+ A Twilio or Telnyx SIP Trunk LiveKit Credentials An OpenAI API Key First, clone the Siphon repository and install the requirements. pip install siphon-ai Next, c
The Hidden UX Problem in Voice AI: When Should the AI Stop Talking? One of the hardest parts of building a voice AI product is not making the AI talk. It is knowing when the AI should stop talking. I did not fully appreciate this at the beginning. When I started building RingBooker, an AI receptionist for salons, spas, med spas, beauty clinics, I was focused on the obvious problems: Latency. Sp
Some time ago, I was building a chat application using AWS Websocket API gateway. Things were going smoothly. I created a WebSocket API Gateway, added $connect, $disconnect, and sendMessage/addGroup routes. From the frontend (React) side, everything was fire-and-forget. You send a message, and the onMessageHandler takes care of it 💪🏼 But then a new requirement of uploading files using S3 signed