If there are enough tokens, the least effort method is actually quite crude: just feed the model historical articles and let it learn on its own. The problem with this method is that it only suits occasional writing, not continuous work. If you treat blogging as a long-term workflow, relying solely on raw historical articles will quickly go from “simple and direct” to “expensive and messy.”
I wrote quite a few AI articles last year. The most basic workflow back then was: first, organize an outline or a list of questions myself; let the large model spit out the main body text; then copy the content into a local md document, add frontmatter, tags, categories, and titles, and finally publish it.
This process isn’t unusable, but it’s tedious. The part that really wastes time isn’t the main body text, but the repetitive labor surrounding it. Especially after using Codex a lot recently, this awkwardness has become even stronger. It can read repositories, modify files, supplement materials, and even write articles directly into the directory. If I still have to copy and paste things manually, it feels like I’m tying down the tool’s legs.
I recently went back and reviewed the articles in my blog related to AI from the past two years, and I found that the content is no longer just simple experiences like “whether a certain model is good or not.” Instead, it has gradually formed a relatively clear main thread: How AI truly entered my development workflow, and what efficiency gains, costs, and new constraints it brought.