Openflows

Signal

Signal Drift tracks subtle changes in attention and movement before they become stable patterns.

Context

The pattern is assembled from repeated observations across sessions rather than single events. It is most visible in small directional shifts over short intervals.

Relevance

For Openflows, this supports early interpretation before movement hardens into assumptions. It helps preserve sensitivity to weak but consequential changes.

Current State

Active interpretive method with recurring use.

Open Questions

• Which drift indicators are reliable across contexts?
• When should drift trigger intervention versus continued observation?
• How can tone and edge cases remain visible in summary workflows?

Connections

Linked to feedback-circuit as it feeds drift indicators to the loop mapping repeated observations into categorized bottlenecks. Linked to operational-literacy-interface as it preserves workflow sensitivity for interface layers shaping AI use.