In a recent essay, Eugene Yan describes writing as a learning loop rather than a standalone act. He frames the practice as a cycle of reading, note-taking, and writing, with each stage feeding the next. For those working in AI and machine learning, this reframing is useful: research and model development are similarly iterative, and documenting understanding is part of the work itself.
Yan's main point is that writing begins before the actual writing takes place. By collecting ideas through reading and capturing them in notes, practitioners build the raw material for clearer explanations. The implication is that waiting until a project is finished to write about it is backward; writing should happen throughout.
Because there is only one source in this brief, there are no contrasting views to compare. The essay stands alone as a practical argument for treating writing as a thinking tool, especially for technically demanding fields where precision matters.