This is a place to collect notes from the boundary between experimental science and software. That boundary is where much of the interesting work happens: an assay meets a spreadsheet, an instrument meets a workflow, and a useful answer depends on whether the process behind it can be understood and repeated.
By day I build lab automation, analytical data products and digital infrastructure for gene therapy manufacturing. Outside work, I make open-source tools in Go, Python and Swift. The common thread is a preference for systems that are inspectable, practical and kind to the people who have to use them.
More soon, on scientific software, automation, developer tooling and the small decisions that make technical work hold up under real conditions.