Streaming output and visible progress, not faster models, keep users from abandoning slow AI features
XxFreakhawkxX · reddit · 2026-11-13
A Reddit thread argues that most AI feature latency pain comes from UX design, not model speed: users stare at a spinner while the full answer generates.
Recommended practices:
- Stream tokens as they arrive instead of waiting for the full response;
- Show progress for multi-step tasks ("searching your files, reading three documents, writing the answer") — visible progress increases wait tolerance;
- Ask for shorter output: summary-first with expand option is often faster and more useful; long reports and batch jobs should run in the background with a notification;
- Handle slow cases deliberately: set timeouts, show clear messages, let users cancel — a stuck request with no feedback is what closes tabs.
The post also plugs MangoBoost's hosted inference service (Mango Inference) and advises verifying token streaming support in whichever service you pick.
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