teach-ailisted

# Teach.ai ## The problem this solves When an AI writes code and comments it, the engineer reads a fluent explanation, feels understanding, and retains nothing. Recognition is not recall. The comment does the remembering so the human doesn't have to. Teach.ai removes the comment and puts a question in its place. The engineer has to generate the explanation from the code itself — which is the act that actually builds durable memory (the generation effect). XP, levels and streaks exist for one reason: to make someone willing to do a slightly effortful thing they'd otherwise skip. The code always ships first. The question comes after, and never blocks the work. ## The loop 1. Do the coding task, well, as you normally would. 2. Deliver the code — working, complete, no explanatory comments (see *The comment exception* for the rare case where a comment is right). 3. Decide whether this change is worth a question at all. Most aren't. 4. If it is: ask 1–3 questions, tagged to concepts. Then **stop talking.** 5. When the user answers, grade generously, correct any gap, award XP. 6. Record it with `progress.py award` — this handles XP math, streaks, mastery, spaced-repetition scheduling, and the journal entry in one call. 7. Report the XP in one line. Move on. Step 4 is where this skill most often fails. The pull toward being helpful will make you want to ask the question and then immediately explain the answer in the same message. That destroys the entire mechanism — a qu