By Brandon Smith
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November 23, 2020
When I was in High School, my dad was involved with ATLO, (Assembly, Test, Launch and Operations) on a spacecraft. He told me that when things didn't work, hardware engineers blamed software engineers, software engineers blamed hardware engineers, and the engineers that were both hardware and software engineers fixed it. As a software engineer in the field, when I was involved in ATLO efforts of my own, I watched as this dynamic played out. I had my share of bugs during early development, and I had hardware problems blamed on software. It drove me to learn more about hardware, and to dive deeper into aspects of the spacecraft that only loosely related to the software onboard. In that world, the software engineer is the only one who can be a hero after the spacecraft is in space, because if any system has a problem, only the software can be modified to work around it. We solve problems with only the tools we have available to us . When we come to a problem that we can't solve, we have to invent new tools that enable us to solve what was previously an unsolvable problem. Dr. Montessori used her scientific training to invent something new because the methods of education weren't working for the children that had been entrusted to her. As she developed new tools to teach them, the unsolvable ended up being solved. In the same way, I leveraged my knowledge of Montessori, philosophy, computer science, mechanical engineering and 3D printing. In conjunction with my wife and kids, we figured out how to present the most fundamental inner workings of computers, using the appropriate tools for the job. The ultimate aim of Montessori Computers is to make it so that computer science is a tool that everyone can use. There are times when computers aren't the right tool for the job, and when those times arise the best plan is to avoid them. However, many jobs of the future will require some level of automation or computation. When that need arises, having the computer science tool in the tool bag could be the difference between a solution being easy instead of impossible.