**Prepared sessions.** Most of this existed: unanswered first, weakest topic
next, wrong-before-right after that, all scaled by what share of the real paper
each topic carries. What it could not do was change with time, say anything
about itself, or be reached without filling in a form.
Evidence now decays on a thirty-day half-life. Exponential rather than a fixed
window because memory has a slope, not a cliff — under a window, 29 days counts
fully and 31 counts for nothing — and because it is memoryless, so an answer's
weight does not shift when unrelated questions are answered, which is what lets
the preview stay a valid forecast. Spring is worth an eighth of last week. Two
things decay: a question's recall probability, drifting towards even rather
than past it, so an old right answer becomes eligible rather than wrong; and a
topic's accuracy, against a prior of two "no idea" answers, which fixes "right
once, known forever".
Strict unanswered-first meant that on a bank of 2,900 nothing was ever
recycled — spaced repetition existed and was unreachable. Review now takes up
to two fifths of a session. And the damping that spread the picks across topics
was applied only to seen material, so a learner with no history was handed the
heaviest domain entire instead of a spread; that was live.
The plan is the product. It is computed, shown, and then the session is built
from that plan's own ids and the plan returned with it, so the two cannot
differ; every figure in it is a tally over the chosen questions rather than a
forecast. No model touches the ranking — a learner asking "why these twenty"
has to get the same answer twice.
**Vision.** The proxy's own `/model/info` says which models can see, so nothing
is hard-coded: 77 report yes, 11 no, and 328 say nothing at all, which means
absent rather than incapable — so those are asked once with an 8px PNG and the
refusal cached. The deployment's main model turns out not to see, and questions
carry figures the learner is looking at, so the tutor was answering about an
image it had never been shown. It routes to a configured tool model now, folds
the description back in as text saying plainly where it came from, and caches
on the bytes because the same figure is re-sent every turn.
Also fixed on the way: `article` was missing from the admin's task list, so
article drafting always ran on the fallback model whatever an administrator
chose; and `.jpx` stem images were sent as JPEG because `mimetypes` guesses
that from the name, so the provider rejected them two hops later.
An administrator must pick a tool model in Settings → AI models. Until then the
tutor says a figure exists that nothing could read, rather than describing one
it cannot see.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TqXevQJhxFrM7jJg82cgZN
The objective did almost nothing
It scoped question counts and nothing else, which is why changing it appeared to
have no effect. An exam now carries a family (USMLE, COMLEX, boards), a
description, and the article views it offers, and `/exams/` reports what the
current objective actually changes rather than leaving the learner to guess.
Reading follows from it: an article returns only the views its objective allows,
so someone revising a basic-science step is never shown bedside dosing they must
not act on — a view you can open but must never use is worse than one you were
never offered. An editor still gets the whole article, because they cannot edit
what they cannot see. An objective configured to show nothing falls back to all
three; that is a configuration mistake, not a preference worth honouring.
Unused figures deleted, at the user's request
3,262 figures — 334 MB — that nothing had ever used. "Unused" was defined by
exclusion and every exclusion was checked rather than assumed: kept if any
question uses it as a stem or explanation image, if any question version
mentions it, or if it appears in article prose or a flashcard. 440 kept, and
five question figures spot-checked as still readable afterwards. MinIO is now
596 objects, 520 MB, down from 3,858 and 854 MB.
This is not reversible from the application; the nightly borg backup of the
volume is the only way back, and that is stated in the script rather than
assumed.
For the record, since it was asked: the extraction is PyMuPDF, with an MD5 skip
list for repeated branding images. It pulled every embedded image from all 18
source PDFs, which is why one 767-page document alone produced 908 of them.
208 backend tests green.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TqXevQJhxFrM7jJg82cgZN
The links I put in the save bar are gone — that bar was right as it was, and a
row of navigation crammed above it was clutter in the one place a person is
trying to finish a question. The footer is where going somewhere else belongs.
`SiteFooter` replaces the copyright line: four columns — Study, Library, Find,
PedsHub — with About, Contact, Account and Settings among them, and the standing
note that this is revision material rather than clinical guidance, said once at
the bottom of every page. A test asserts every link points at a route that
actually exists, because a footer full of dead links is worse than a short one:
the reader learns not to trust any of them.
Two retrieval faults the writing found
A bare condition name is a thin query. "Rickets" alone retrieved five passages
about *Rickettsia* — an embedding has little to go on in one word, and the
nearest neighbours of a short string are whatever looks like it. Asking as
"Rickets in children: definition, causes, clinical features, diagnosis and
management" took the contamination from five passages to none, so both the
pipeline and the generated route now ask that way.
And a category that names a department rather than a condition retrieves chapter
headings and whatever sits near them. "Pediatric Nephrology" passed the material
check with entirely irrelevant passages, and an article called that is a
department, not something to revise. Those names are now excluded from the topic
list.
Both were found by an agent writing articles and reporting what looked wrong,
rather than by anything automated noticing.
247 frontend tests green.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TqXevQJhxFrM7jJg82cgZN
The manager still opened a modal
The bank was moved to the full editor a while back and this page was missed, so
editing from the manager still meant a dialog whose category control was a flat
select of seven hundred breadcrumb strings — no search, no way to pick a branch
and then narrow within it, and too small to follow. The full page already has
the searchable drill-down with sub-selection, images, versions and option
explanations. Edit now goes there and carries the way back, filters and page
intact. The modal stays where a quick correction belongs.
Articles, written rather than generated
Per the user's instruction: no OpenAI, no OpenRouter for writing — bge-m3 for
the search and nothing else. `scripts/article_pipeline.py` splits the job so
only the machine half is machine work:
topics — conditions that still have no article, biggest first
fetch — embed the topic, search the library, write the passages and the
references derived from their metadata to a file
import — take a finished article and store it as a draft
No model API is called at any point in that pipeline. Whoever writes the prose
reads the passages and writes original text from them; the references still come
from what retrieval actually returned, so they cannot be invented by the writer
either — the same property the generated route had, kept.
The importer refuses an article missing any of short, long or clinical. A view a
reader is offered and finds empty is worse than one that was never promised.
244 frontend tests green.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TqXevQJhxFrM7jJg82cgZN
Daniel