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As a teenager, I was deeply intrigued by artificial intelligence. I trained my first artificial neural network at 13. I hacked simple training algorithms in C and C++ from scratch, which was the only way to explore the field in the 1990s. I went on to study computer science, mathematics, and psychology to better understand the underpinnings of this sprawling topic. Often, the way machine learning (the term data science didn’t exist yet) was applied seemed more like alchemy than real science or principled engineering. My journey took me from academia to large companies and startups, where I kept building systems to support machine learning. I was heavily influenced by open source projects like Linux and the then-nascent Python data ecosystem, which provided packages like NumPy that made it massively easier to build high-performance code compared to C or C++. Besides the technical merits of open source, I observed how incredibly innovative, vibrant, and welcoming communities formed around these projects.