Mobile phones have the potential to be useful agents for their owners by detecting and reporting situations that are of interest. Several challenges emerge in the case of detecting and reporting "nice to know" situations. Being alerted of these events may not be of critical importance but may be useful if the user is not busy. For detection, the precision of sensing must be high enough to minimize annoying false notifications, despite the constraints imposed by the inaccuracy of commodity sensors and the limited battery power available on mobile phones. For reporting, the noti- fications cannot be too obtrusive to the user or those in the vicini- ty. Peripheral cues are appropriate for conveying information like proximity, but have been studied primarily in settings like offices where sensors and cueing mechanisms can be controlled. We explore these issues through the design of PeopleTones, a buddy proximity application for mobile phones. We contribute (1) an algorithm for detecting proximity, (2) techniques for reducing sensor noise and power consumption, and (3) a method for gene- rating peripheral cues. Empirical measurements demonstrate the precision and recall characteristics of our proximity algorithm. A two-week study of three groups of friends using PeopleTones shows that our techniques were effective, enabling the study of how people respond to peripheral cues in the wild. Our qualitative findings underscore the importance of cue selection and personal control for peripheral cues.