A lot of of us have a Raspberry Pi, many of us have more than one, and some have probably an excessive amount. And who can blame us? A micro-computer that you can plug regular USB peripherals into, has HDMI out, wireless networking (on later models), low power consumption and can run your favorite Linux distro, all for just $35? Frankly it’s amazing we don’t all have more than we do. But what are we doing with them?
In recent purely non-scientific Twitter poll I asked my followers just that: What are you using your RaspberryPi for? Not surprisingly some people are running a Linux desktop on them, and several are running fun or useful software on them like PiHole, RetroPi, or even just as a mini file server. But only about 20% of the people who responded were making use of one fantastic thing about the Pi: It’s GPIO pins. In fact, most people (35%) said their Pi was just sitting on a shelf not doing anything!
Well my friends, nothing makes this foodie nerd sadder than a wasted Pi. So blow the dust off that little board of yours and grab some LEDs or whatever sensors you might have, because in 10 minutes we’re going to turn that shelf decoration of yours into a functional smart IoT device!
Recently I, and several of my coworkers, were let go from Endless as they continue look for ways to accomplish their mission of empowering the world with technology. I was with Endless for right at one year, though it seems much longer than that. During my brief time there I learned so much, met so many wonderful people, and got a taste of life beyond the confines of North America and Europe. I am grateful for the opportunity that Endless gave me, and wish them only success in the future.
After a little over 6 years, I am embarking on a new adventure. Today is my last day at Canonical, it’s bitter sweet saying goodbye precisely because it has been such a joy and an honor to be working here with so many amazing, talented and friendly people. But I am leaving by choice, and for an opportunity that makes me as excited as leaving makes me sad.
Late last year Amazon introduce a new EC2 image customized for Machine Learning (ML) workloads. To make things easier for data scientists and researchers, Amazon worked on including a selection of ML libraries into these images so they wouldn’t have to go through the process of downloading and installing them (and often times building them) themselves.