AI memory palaces and superdidactic AI: 7. Five things to build

Section 7 of 9 of the report "AI memory palaces and superdidactic AI" by Hiina (v1, updated 2026-10-27)
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7. Five things to build

Start with the re-entry card: it's a weekend, it sits on a decision queue the builder already uses, and the queue's timestamps give a baseline to beat. The others reuse its pieces.

#	Build	Effort	Rests on
1	Re-entry card per decision-queue item	Weekend	Cognitive interview, Mäntylä, Leroy, Parnin, Mercado
2	Intent cards in Anki	1–2 days after #1	Retrieval practice, Shattuck/Klein intent, Kaasa
3	Bootstrapped model of the builder's queue answers	1–2 days	Dawes, Park, Twin-2K
4	Varied-cue subtitle vocabulary cards	Weekend	Butowska-Buczynska 2024, FSRS
5	Spaced repetition for intentions	1–2 weeks	HeartSteps, Duolingo bandit, implementation intentions

1. Re-entry card (weekend)

A page generated for each open decision-queue item that runs the section-2 sequence: ask, then show the builder's own words, then the state, the options with reasoning, and the trace.

Stack: an agent skill that reads the project's status note (state, next step, log), the repo's git log since the builder's last answer on that project, past answers from the decision queue, and dated lines the builder wrote in the log. It writes one HTML page per item and attaches it to the queue item. The page opens with one or two questions ("what was this for?", "what did you rule out?") and a reveal button. The builder's typed answer and the reason for the final choice are saved back to the project notes, so the next card has their words to quote.
How to tell: randomize open items to card vs no card for a month. Compare time from item creation to answer against the 12–62 day baseline, how often a decision is reversed within 14 days, and whether the pre-reveal answer matches the project's stated purpose. A null on time with fewer reversals still counts as a win.

2. Intent cards in Anki

For each active project, extract five to ten cards from the builder's own words: purpose, end state, antigoals, key decisions and their reasons. That is Klein's template, filled from log lines and queue answers. Push them through AnkiConnect into a low-volume project deck, with one card due the day before any queue item for that project.

Why it might work: retrieval practice is one of the most robust effects in learning (g = 0.50), intent transmits badly by default (34%), and people lose their own reasons within months (one in five consistent months later).
How to tell: FSRS retention on the deck, plus the pre-reveal match score from build 1 for projects with cards vs without.

3. Bootstrapped model of the builder

Give a model each past queue item, the project note at that time, and the builder's earlier answers, and ask it to predict the choice and reason. Run it on the answer history first, holding out the latest answers.

Why: Dawes-style bootstrapping says a model of a person's policy can match them and is less noisy. Park and Twin-2K say LLM clones get 80–90% of test-retest on attitudes but are "more rational" than people, so look for where it misses the builder's taste calls.
How to tell: accuracy against "always pick the default" and against base rates, plus calibration. If it's good on some item types, those items can show "you'd probably pick B because of X", which is itself a re-entry cue. The rubber-stamping risk from the companion report on AI enfeeblement applies: show the prediction after the builder's pre-reveal answer, not before.

4. Varied-cue subtitle vocabulary cards (weekend)

The subtitle premine pipeline already finds every subtitle line where a word appears. Add an Anki card template that shows a different matching subtitle line each review, chosen at review time from a field holding all of them. Split the deck's cards randomly in half: fixed sentence vs rotating sentence.

Why: the 2024 PNAS result that varied cues beat constant ones, and that learners misjudge it, is the cleanest evidence for "the model replaces the deck". This tests it on a real personal study deck with no LLM in the loop.
How to tell: FSRS stability and lapse rates per half after a month, and recognition of the word in the next episode watched. Cards with no example sentence go in neither half.

5. Spaced repetition for intentions (1–2 weeks)

A scheduler on a home server where each intention is a card: a cue (time and place), an action and a deadline. Prompts start dense after a plan is formed, lengthen when the person acts, shorten and change the cue after a miss. A contextual bandit picks the time slot with a dose penalty, and message wording rotates with a recovery penalty, Duolingo-style. Deliver through whatever already reaches the person's phone.

Why: the corrected effect sizes say timing is a small lever, so the real test is whether capture and specific cues move anything.
How to tell: run it as a micro-randomized trial on oneself. At each decision point, flip a coin for prompt vs no prompt and log whether the intention was touched within 24 hours. That's the HeartSteps design with one participant.

A VR memory palace for a project is a natural fit for someone already building in Godot and VR, but Legge's result predicts it won't beat build 1. If one gets built anyway, test it against build 1 on the same items.

Text licensed CC BY 4.0 (attribution: Hiina, ai.hiina.space). Source: https://github.com/hiinaspace/ai-hiina-space