I first tried a robot vacuum cleaner 5 years ago. We bought a Eufy and it taught me a valuable lesson – I needed to either understand how it works or be able to predict what it would do.
The Eufy I’d purchased functioned on a “bounce” algorithm. So it ping-ponged its way through a room and eventually our home. This sounded reasonable before I bought it.
But it drove me crazy. As it was hard to predict, I ended up using its remote control to take it where I wanted it. At that point, however, it was easier to just vacuum myself.
I’d picked the Eufy up on sale. It reminded me of another old lesson – if you decide to buy something, invest in making it good. It pays off over time.
3 years ago, as we moved into our home, I purchased the Roborock S7. This time, it wasn’t about the sale (lesson learned). I did extensive research and I decided to try the Roborock for 3 reasons –
(1) Feedback on its mapping technology was great. The Roborock promised to map out our home and go through the map systematically.
(2) It had mopping functionality. That sounded very cool.
(3) It also had auto-empty functionality. This meant emptying the dust and dirt every few months vs. every time we used it.
3 years in, our Roborock – nickamed Zorro – has become a key fixture in our home. It has delivered on all the above with impressive consistency.
Zorro is a great example of an AI tool that adds a ton of value to our home. It uses its vision to see around the home and uses its intelligence to navigate and clean. A big part of my job these days is to build AI tools, I look to Zorro as inspiration for what a great tool does.
First, it solves real problems well. In doing so, it removes time spent on tedious tasks.
Second, it does so in a manner that makes the output predictable. When we delegate control to a tool, it helps us ensure the tool is working in a way that solves our problems in a manner that works for us.
