Resources: FAQs

The bright, natural lighting. The flat palm behind a lipstick. The countdown timer flashing to cause FOMO. The chat scrolling so fast it looks like the Matrix made of heart emojis. You know when you are in a live shopping event. Sometimes, the infrastructure knows as well. If implemented incorrectly, live shopping can (belt) buckle

Activity feeds power some of the most heavily used features on the web: X's home timeline, Facebook's news feed, LinkedIn's updates, and the notifications panel in nearly every social app. They look simple on the surface, but feeds that work fine with 10,000 users often collapse under the weight of 10 million. The core challenge

Vision AI integration is an engineering problem more than a model-selection problem. Yes, you need a great vision model, but the infrastructure you build will be the difference between a fragile prototype and a production system. If you're adding vision AI to a live or near-real-time video application, you'll quickly run into questions that model

If you've built a product that handles video uploads or live streams, you've probably encountered FFmpeg. Once you're in production, you need to decide which codec plays on which devices, how much CPU time you're burning per video, and sometimes whether you need a lawyer to understand patent licensing. What FFmpeg is FFmpeg describes itself

WebRTC has become the standard transport layer for AI agents requiring real-time voice and video. Originally designed for browser-to-browser video calls, WebRTC is a protocol stack that enables real-time audio and video communication over UDP. Because it prioritizes low latency over guaranteed delivery, it is ideal for the sub-500ms response times that natural conversation requires.

Object detectors such as YOLO and EfficientDet treat each video frame independently. This works fine for static images, but in real-time video streams, it causes detections to flicker. Bounding boxes jitter, confidence scores oscillate near thresholds, and objects "blink" in and out of existence. In a display overlay, this is merely annoying. In a closed-loop

When building multimodal systems that need to sync audio and video in real time, one question matters more than you'd expect: Can the lips match the voice? Get it wrong, and your AI character looks like a dubbed foreign film. Get it right, and it feels real. And getting it right depends heavily on your

Building a voice agent that feels responsive is hard. Users expect conversational AI to respond instantly, but the realities of LLM processing, tool execution, and text-to-speech synthesis introduce unavoidable latency. The result? An awkward 3-second silence that makes your voice agent feel broken. Speculative tool calling is the architectural pattern that solves this problem. Why