There will be no courses. What was there: one draft called "jk" with two empty
lessons, and 4,000 lines of code around it — courses, modules, lessons,
enrolments, per-lesson progress, SCORM, BigBlueButton, completion certificates,
three React pages, a router, two models.
Its real cost was everywhere else. Every query that measured practice had to
remember `Quiz.course_id.is_(None)`, and forgetting it in one place would have
silently mixed course attempts into a learner's analytics; the bank predicate
carried a subquery to exclude a course's own questions from every search,
recommendation and share; quiz access had a second, parallel rule about
enrolment. All of that is gone, so the remaining rules say what they mean.
`quizzes.allow_review` goes with it. It was only ever enforced for a course
quiz, so it had become a promise nothing keeps — the public session page was
still offering "no answer review" about sessions that review fine.
The fixtures' question 5 lived in a course quiz and stood for "a question that
exists but is not in your bank". There is no such thing now — a question is in
the bank unless it is deleted — so the counts it kept out of the numbers are
back in, and the tests that turned on it now turn on deletion or on the
attempt that actually holds a question.
Files the LMS uploaded stay on disk and stay protected: LEGACY_LMS_PREFIXES in
app/utils/upload_access.py is what keeps them unreachable.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TqXevQJhxFrM7jJg82cgZN
Retrieval fused a bi-encoder and BM25 by reciprocal rank. A bi-encoder embeds a
document long before the question exists, so the two never meet: it is good at
"same topic" and mediocre at "answers this". A cross-encoder reads the pair.
The proxy already serves three — `cohere-rerank-v4.0-pro` is the default and
measurably better than the fast variant. Query text goes exactly where the
embeddings already go, and nothing new was signed up for.
It found a defect nobody was looking for. In AI Mode each finder scored
`1/(1+rank)` *within its own corpus*, so the best article, section, question and
card all scored 1.0 and the shortlist was a meaningless round-robin. A
cross-encoder is the first thing in this system that can compare a question
with a section. Candidates per kind widened so it can select rather than merely
reorder.
Measured against labels neither ranker produced. Questions, 60 disease tags:
precision@3 0.394 → 0.483. Sections, 60 article titles: 0.772 → 0.833.
"Management of bronchiolitis" led with influenza transmission and a pregnancy
question; "when do you image a first febrile seizure" returned the definition
rather than the sentence saying imaging is unnecessary.
And the honest negative, in docs/reranking.md: board vignettes are written
*not* to name their diagnosis, so on "what causes croup" it prefers a question
that says the word in passing over the barking-cough vignette that never says
it. Some of the bi-encoder's strength is traded away.
Not on the typeahead. A page of results is a choice being made and worth a
third of a second; a typeahead is a word being finished, runs on every
keystroke, and has nothing to judge yet.
The three-state thresholds stay on cosine, argued at the constant: a reranker
only ever sees a shortlist and structurally cannot answer the corpus-wide
question those numbers ask, and whether an answer claims to come from the
library is a promise that must not depend on a network hop.
Every failure returns None and leaves the order alone — unconfigured, no proxy,
connect error, bare 502, timeout, non-JSON, a duplicate or out-of-range index,
a non-numeric score, a list the wrong length. Verified against the running site
with a bogus model name: same results, fused order, no error to the reader.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TqXevQJhxFrM7jJg82cgZN
