Chapters is a reading tutor that runs a full 30-session semester with one kid, writing her a new book each time based on what it currently believes about her level, interests, and skill gaps instead of dragging the whole session history into every prompt, it keeps a small editable model of the student and updates that directly.
Viewers can see the comparison between Fox( the better tutor ) and the Owl( the baseline tutor with no understanding from memory ) and see how ours is a strongly better agent.
How it works: each session the tutor reads its current model of the kid, generates a book for her, she reads and reacts, and the tutor revises the model adding, updating, or archiving specific facts rather than appending a transcript. A separate small model checks in every couple session to catch facts that have gone stale or contradict new evidence.
Where the tools come in:
- RawTree is the permanent archive: everything the model lets go of still lands here, queryable with plain-English-to-SQL.
- Liquid AI's LFM2 is that periodic memory review, running locally.
- Nimble pulls a real, current web fact into each story.
- Black Forest Labs FLUX paints a real cover for every book.
Result: a tutor whose prompt stays flat (~1,100 tokens) for the whole semester and ends up more accurate than one that just keeps appending everything (which balloons to ~49,000 tokens).
