The tutor prompt said "never mix languages in a single response" and
mirrored the language of the question, so asking in English — which she
does, because she is practising — returned the one explanation surface
that gives nothing in her own language. It now answers in both, pair
language first, halves separated by a blank line.
Which half is the safety net and which is the lesson depends on who is
writing: the pair is (English + X) and Petal is used from both ends, so
the prompt asks for both and says it doesn't know which way round.
The split is a rendering nicety, never a parse the reply depends on: a
half-streamed reply is all one half, a model that ignores the
instruction renders as one block, and nothing is ever dropped.
For the height, the first attempt clamped the box to the room left below
the anchored card so it could never overhang — measured, that gave 176px
against a 442px answer, worse than the 220px it replaced. The card's own
chrome spends ~290px of an 810px window, so "fits below the word" and
"room to read" are not both available. The ceiling is now a flat 50vh and
the overhang is made navigable instead, per item 4: the card reports its
reach like the rail already does, the column grows, and the page can
scroll to the actions below it.
Every `中文 · English` string moves out of ~29 components into
web/src/i18n: one Pack type, a verbatim zh pack, and two ways to read
it — usePack() for components, pack() for the modules that build a line
when something happens rather than when something renders.
Anything with a value in it is a function on the pack rather than a
template at the call site, English pluralisation included: word order
isn't universal, and a pack author has to be able to move the number.
The roster constants (tones, rewrite styles, export formats, companions)
keep only value + emoji, so a label can't drift from its key.
On the server, internal/llm/lang.go replaces "Simplified Chinese" in the
three prompts that actually name her language. pt-PT is spelled
"European Portuguese (pt-PT, never Brazilian Portuguese)" in the prompt
itself, and each Lang carries her word for "why" so the tutor prompt
still recognises the question when she asks it her way.
pair_lang reaches the model through the row-scoped query each handler
already ran — the one that proves she owns the document — rather than a
second lookup that could disagree with it.
Also records Phase 18's deploy: migration 0011 rehearsed against a copy
of the live VPS database, then applied for real.
prosolis