Build the layer.
Then build the thing.
The post's advice is simple: learn prompts, LLMs, AI basics, and core concepts before jumping into advanced AI. Then use that base for projects and automation.
Two broad learning stages with manual marks and cross-stage local notes
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The page has a foundations stage with prompts, language models and AI basics and core concepts, then an intermediate stage with building a project, automating a workflow and review and iteration. Bars compare three fixed items per stage at one hundred twenty pixels per item. This is an attributed broad path, not a course catalog, provider link or tested curriculum. Selecting intermediate does not require foundation completion and invokes no model, project build or automation. Each stage has three checkboxes, producing eight possible subsets. The stored completed list spans both stages, but the visible count filters keys by the selected stage prefix. With all foundation items and two intermediate items selected, the global list has five keys while intermediate displays two of three complete. Bars compare three foundation marks and two intermediate marks at one hundred twenty pixels per item. These are manual ticks, not demonstrated mastery or executed work. The actual JSON export has five fields: real export timestamp, selected stage, completed keys, notes and a fixed scope boundary. Bars compare five exported fields with three stored properties at eighty pixels per item. Notes from both stages remain in the shared note object when switching views. Reload restores stage, marks and notes. Reset saves foundation, an empty completed list and empty notes, instead of deleting its key. Nothing is enrolled, submitted, built or scheduled by export.