4. There is no way to ask for cleaning of a sequence of zones without resorting to scheduling. So you always have to wait that it goes back to the base before sending the #D7 to subsequent zones.

3. The #D7 actually “gets confused” but for different reasons. When you ask for zone cleaning, it often insists that the location of the base has changed (it has not) and there’s no valid map to work on. You just have to try again and at the 2nd attempt it will clean as expected.

2. @NeatoRobotics support told me my problem was that I placed the base under the sofa and the robot “gets confused”. Totally NOT the case (it was zone overlapping). If you care about interior design there is no reason to have the ugly thing visibile in your house at all times.

1. When you define zones, be sure they overlap a bit, otherwise #D7 won’t know how to get from the base to any target zone and will remain stuck “thinking” next to its base until it runs out of battery.

3 months in, my experience with the @NeatoRobotics D7 is infinite better than with @iRobot i7+ (or any older model I had). No-go lines and zones definitions make all the difference for me. That said, a few issues:

In the past, seeing someone walking on the street and fully immersed in book reading would trigger fascination, curiosity, admiration. Same person immersed in phone watching triggers irritation, criticism, derision. It’s the supposed content we judge, not the action per se.

Massive research just published by @richwatson at Gartner: “Assessing Application and Infrastructure Monitoring for Containers”. It’ll take me forever to read this thing…

“By incentivizing hyper-efficiency, the machines have deprived the employees of tasks they used to find enjoyable. Some also feel like their most important assignment now is to train and babysit their often inscrutable robot colleagues.” https://t.co/IhDzgkhldp

2/ “…for a long time, people would deploy models and leave them out for years, not having any idea of whether the model was making money, losing money, or behaving terribly.”

1/ From @OReillyMedia “Machine Learning at Enterprise Scale”, quote by Patrick Hall, H2O:

“If the environment changes from that snapshot, the model will perform poorly…And this idea of model management, model monitoring, and complex deployment strategies is very important…