Once upon a time, when I was a reporter and frontline data journalist, I had a habit of learning in public. I had a couple blogs where I would post code snippets and methodological approaches. I tweeted. I spoke regularly at conferences. I cleaned, documented and shared code more often.
But then I started working at large organizations filled with people much smarter than I was, and sharing started to feel uncomfortably self-promotional. And then I landed in a series of executive roles, and writing about the things I was doing — leadership, strategy, transformation — felt like being the kind of LinkedIn influencer I tend to roll my eyes at.
But that whole time, I’ve still been tinkering. Most days I still find time to code, analyze data or try out new technologies, both in a personal and professional context. Especially over the last few years, as generative AI has come on the scene, I’ve done a lot of building and experimenting. I’ve just mostly kept it to myself.
I’m a regular listener of the Latent Space podcast, and one of the hosts, Swyx, has been a yearslong advocate for the value of learning in public. In the world of data journalism and AI, specifically, Simon Willison is rather famous for this. Longtime friends like Ben Welsh, Matt Waite and Derek Willis have never really stopped.
I’m inspired by people like that, so I’ve decided to try learning in public again. Most of what I write about here will probably be related to journalism, data and technology, likely leaning heavily into specific applications of AI, machine learning and other tools from the world of data science. I’ll try to keep it practical and concrete.
I’m looking forward to it. I hope you find it useful.