Yes, I don’t follow the tag time spec to a T (hence the change in name). The algorithm is essentially:
nextPing = lastPing + max(3m, min(60m, gap())). Where
gap() samples from the exponential distribution centered at your mean ping gap.
If the subconscious effect you describe is present, it’s very subtle! The 3 minute lower bound was introduced because pings closer than that were just annoying, and due to the nature of my consulting, I needed to give detailed reports on how I spent my time, pings longer than 1 hour apart wouldn’t give me this information. This is likely a special case of my working conditions so perhaps others will prefer to remove that feature.
More generally, my goal is to get as accurate view of how time is spent with as few samples as possible. I’m not necessarily married to the Poisson distribution. In the future I’d like to experiment with more black box algorithms that try to predict your behavior, and focus sampling during times of high variance / low confidence. The data collected in a sample would be valid for the time around the sample inversely proportionate to how variable your samples have been during that time period in the past.
That’s where my heads at, for now poisson sampling has been working very well (even with the bounds).
Thanks for adding it to the list! I’m happy to contribute, ya’ll have built a nice place here