Three things landed together; the message names all of them, because a commit
that mentions one is a commit nobody finds the other two in.
**Figures.** Thirty-four JPEG 2000 files — 21 on questions, the rest unattached
in the media library — are WebP now, with `questions.image_path`,
`questions.explanation_image_path` and `media_assets.path` repointed together.
Serving already converted them on the way out, so nothing was broken; this
removes the step and makes what is stored the same thing that is served. The
originals stay: they are the only copy of what came out of the PDF, they cost a
few megabytes between them, and a conversion nobody can undo is not one to run
against a live bank. Paths are found by what the columns say rather than by
listing a bucket, because three tables record them and updating two would be
worse than none.
**The openai SDK is gone.** Ten call sites — one more than the map said, the
Celery article drafter — every one of them a POST with a JSON body, and not one
reading usage, cost, tool calls or logprobs. Every other call to the same proxy
was already plain httpx: embeddings, the ChromaDB embedding function, speech
both ways, model discovery, the vision probe. So this deletes an abstraction
rather than swapping one for another, and leaves one HTTP client instead of
two. `chat()` and `achat()` return the message content; a `ProxyError` carries
the status and the first 500 characters of the body, which is where the proxy
explains itself.
Behaviour is preserved deliberately, including a 600-second fallback timeout
for the four call sites that were running on the SDK's ten-minute default.
Lowering that is a real change and belongs in its own commit.
Proved against the live proxy on both services rather than only against mocks:
a completion, an async completion, a real 400 the vision probe still classifies
as a refusal, 407 models read from the catalogue, and a word read off an image.
**Voice.** A chosen voice is honoured whatever serves it. The prefix check only
accepted a locally served one, so a site adding a hosted voice would offer it
in Settings, save the learner's choice, and then quietly read every question in
the default voice. The list has always come from the database — adding a voice
is a row in Settings → AI models, never a code change.
And the sign-in page stops offering a locked door: `signup-policy` reports
whether registration is open at all, and the Sign up link goes when it is not.
The switch existed and the only way to discover it was to fill the form in.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TqXevQJhxFrM7jJg82cgZN
**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
**Linking.** A question could be tied to an article only from the article, by
typing the question's number into a box — so opening a question you had just
linked showed no sign of the link, and there was no control to add one. Both
ends now search: find the article by title from the question, find the question
by stem from the article, pick which section of the article the link lands on,
and see what is already linked. One shared finder, so the two ends of one
relationship cannot describe it differently. `GET /questions/{id}/articles`
mirrors the endpoint that already existed the other way, and `GET
/articles/linked` is retired — it answered this question by shipping the whole
prose of every linked article to the quiz player for a list of titles.
"Practise this topic" is a reader's control and no longer appears on an editing
screen.
**The player.** The rail was a bordered card floating in the page with a
scrollbar of its own, so a session had two scrollbars side by side and a
collapse handle tucked inside the card's padding. It is a column now: flush,
full height, its own background rather than its own border, the handle on the
boundary it moves, and a progress bar under the count. The bar at the foot is
the bottom edge of the window — three flush segments, no gaps, no pills —
because Exit as a small grey pill beside a large blue Next made leaving look
like the accident.
Study mode no longer asks whether you are sure. Leaving suspends: every answer
is saved, nothing is graded, and it is waiting where you left it — so the
dialog asked permission for something reversible, under a name for something
that does not happen. An exam still asks once, because a block has a clock, and
it now says what it is: "Leave this block?", not "End Session".
Options are lettered. The explanations already are — a stem extracted from a
board PDF says "Preferred Response: E" — so numbering them 1 to 5 left the
reader translating between two labellings of the same five lines. The tutor is
told the same letters, and the answer key is marked against its own option and
declared authoritative, so a model that would have answered differently cannot
tell a student the marked answer is wrong.
"Preferred response" and "Source page 518" are gone: the first labelled a block
that is obviously the answer, the second named a page of a book the learner
does not have. The clocks moved out of a grey strip across the explanation,
where they read as part of the answer, to the foot of the rail with everything
else about the session.
**AI Mode.** Sources are headed and counted at the end, where evidence belongs,
with the practise button after them rather than above. That button appears only
when there is something to build from and says what it will build — it used to
sit under "how can I help you today?" offering to make a session out of
nothing. A cited question opens in place: `/questions/:id` is the editor, so
following one dropped a learner into a form for changing the question they had
just been told about. And a session built from a chat is named like every other
session, rather than after the chat — asking "hi" produced "hi — practice".
Also: two test questions with raw `<p> </p>` in their stems were live in
the bank; retired. And 36 article summaries were written as a table of contents
with the colons filed off — "Peanut allergy prevention and management: LEAP
guidelines by risk tier, risk stratification, and anaphylaxis treatment" — every
noun phrase sounding informative and none of them saying anything. Rewritten as
claims, with the rule added to the prompt that produced them.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TqXevQJhxFrM7jJg82cgZN
"PREP" is the American Academy of Pediatrics' trademark for their own product.
The plans here are our own sets of questions grouped by year, so they are now
named for what they are: Board Review 2021, and Mixed Review for the plan that
draws from every year at once.
Renamed in the database as well as the code — 13 plans, 14 quizzes a learner had
already generated from a block, and the 12 year tags, which appear in the
question bank's filters and are as visible as the plans. The seeder matches both
the old and new names so a fresh import still finds its material, and the tagger
mints the new one so the next run cannot undo this. Prompts and comments that
described the source PDFs by that name now describe them by what they are.
The generation run's 377 failures were not a bug
Every call was reserving the model's full 64k output ceiling, and OpenRouter
refuses the whole request when the balance is below the reservation — "you
requested up to 64000 tokens, but can only afford 52017" — however short the
answer would actually be. `_call_model` now takes a max_tokens, and the article
writer asks for 4000, which is comfortable for three views of one topic and
keeps each request small enough to be affordable. 98 articles were written
before the balance ran down; 158 exist in total.
Generation is paused at the user's request while credits are topped up.
208 backend, 243 frontend green.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TqXevQJhxFrM7jJg82cgZN
MinIO was resolving to the wrong container
Putting the backend on danvics_milvus to reach the clinical index gave it a
second service called `minio`, and Docker resolved that one first. Every object
read failed with InvalidAccessKeyId while the bucket simply looked empty — all
435 stem images unservable, and nothing in the logs saying why. The quiz MinIO
now answers to `quiz-minio`, which nothing else on this host claims.
A topic named after a shelf retrieved headings, not prose
"Pediatric Pulmonology" returned ten chunks whose top hit was 29 characters —
`**270** Pediatric Pulmonology`, an index line. Chapter titles rank well against
a query that looks like a chapter title. The model was handed a prompt with
citations and no content and said so, which was the correct response and read as
a JSON failure.
Two gates, both stated in the code. A chunk under 200 characters is a heading or
a running header rather than something to write from. A topic whose passages
total under 3,000 characters is skipped with the count in the reason, rather than
asking a model to write a medical article out of fragments — it will either
refuse or invent, and only one of those is visible.
The 71 generated drafts are deleted at the user's request. Nothing linked to
them and generation is resumable, so the cost was model calls rather than work.
Question bank corrections, from the agent that ran alongside:
262 questions had OCR-mangled units repaired — `inEq/L`, `mrnol/L`, flattened
`10⁹` superscripts and the rest — each with a version snapshot written first, so
every edit is reversible from the existing question editor. 94 stem images that
belonged to the explanation were removed; PREP's own `Item Q37A` / `Item C37B`
labels turned out to be a far better signal than word cues, taking the confident
split from 69/58/308 to 300/81/54. 13 uncertain images are listed for a person.
203 backend, 223 frontend green.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TqXevQJhxFrM7jJg82cgZN
Daniel