Index of everything on this site: https://ai.hiina.space/index.md
An AI-built memory palace is the wrong shape for getting someone back into a project, but the question behind it is right and mostly unstudied. Re-entry works through a few cues: the person's own earlier words, where things stand, what they meant to do next, and being made to recall the intent before being told it. Superdidactic AI is real but modest, and the limits sit on the learner's side. Everything past that, from sleep learning to uploads, either doesn't work yet or isn't close.
The motivating use case is a solo builder who supervises many agent projects through a decision queue: agents do the work and surface the choices that need a human. The question was whether AI could rebuild the builder's headspace for each choice, how far AI teaching can go, and where the spectrum ends, from memory palaces through sleep learning and brain-computer interfaces to uploads.
| Part of the spectrum | What's actually true | Status |
|---|---|---|
| Memory palaces | Large effects for ordered lists only; a richer room doesn't help | Proven, wrong target |
| Project re-entry cues | Own-words cues ~90% vs 5–17%; automated traces 2x notes; showing agent reasoning catches bad plans 81% vs 55% | Strong pieces, never assembled; gap is real |
| AI tutoring | 0.1–0.3 SD at scale, ~0.6 in a tight trial; 2 sigma was never real | Real, modest |
| AI persuasion as mirror | Information density drives it; personalization adds ~0.4 pp | Teaching gets the dense part, not the retrieval |
| ML spaced repetition | Neural schedulers beat FSRS on log loss; LLM cards need human-picked targets; varied cues beat fixed | Scheduler solved, deck replacement promising |
| Sleep learning and TMR | New learning in sleep is tiny and implicit; TMR g ≈ 0.13–0.29; slow-wave stimulation ≈ 0 | Mostly no |
| Nudges and JITAIs | Bias-corrected effects small and decaying; capture and specific cues matter more than timing | Real, small |
| Neural UIs and BCI | Pixel-rendered desktops can't hold state or text; BCIs reached ~mouse parity at ~10 bits/s | Far |
| Uploads and "the soul" | Emulation not close even for a worm; distilling a work policy has 50 years of precedent | Policy yes, self no |
The most promising build is a re-entry card on each decision-queue item (section 7, build 1). It's a weekend of work, and the queue's own history gives the baseline: in the motivating setup, asks answered live took minutes, while asks left in notes took 12–62 days. This report builds on a companion report on personal web-surfing models and an ought-driven tab feed, and doesn't repeat the companion reports on AI enfeeblement and idle supervisors, or on AI meditation and flow steering.
Next section: https://ai.hiina.space/reports/ai-memory-palaces-and-superdidactic-ai/02-context-reinstatement-prompt-engineering-the-human.md
Text licensed CC BY 4.0 (attribution: Hiina, ai.hiina.space). Source: https://github.com/hiinaspace/ai-hiina-space