Every pass was English-shaped: CheckpointMessages took the text and the tone and
nothing else, so there was never a language decision to get wrong. On the live
build two pt-PT sentences drew no cards at all — Petal read the Portuguese, said
nothing about it, and filed a mechanics note about the one English line.
The rule is two decisions reading different state. What gets corrected follows
the document. What language the explanation is written in follows the writer —
the half of her pair she is not learning, from users.direction — because an
explanation is teaching, and teaching lands in the language she reads most
easily. Those coincide for every account that exists today (learnerPairs is
{"zh"}), which is a fact about the roster and not about the design, so Target
keeps them apart. It carries a third language too: the collocation gloss is
addressed to her rather than to the document, and folding it into Explain would
have quietly moved it into English on every English document.
The document verdict is a proportion, not a presence — one Portuguese quotation
must not flip an English essay. Per sentence, three-way: pair, English, or no
answer. The third value is the load-bearing one; counting the undecided as
English is exactly what would hold a journal of short Portuguese sentences in
English forever, so the Latin pairs needed an englishMarkers list curated against
pt/fr/es as carefully as latinMarkers was curated against English. Hysteresis at
70/40 because a bilingual paragraph would otherwise alternate its cards' language
every few keystrokes, and hysteresis needs a yesterday — hence the column. Plus a
corroboration floor: a ratio computed over "Não. Eu." is 100% of nothing, and a
flip rewrites every card in the document.
The verdict folds into the chunk salt beside the tone, so a document that changes
language re-opens every sentence rather than serving back cards in a language it
no longer speaks.
checkpointSystemPrompt could not simply take a language — it opens by naming the
reader an ESL learner, and appending "explain in Portuguese" hands the model two
contradictory framings. Separate constants, sharing the JSON contract below the
framing. Both carry a "never translate it into English" line, which is the
instruction the model will most want to disobey. The English prompts are
untouched byte for byte, and a golden says so out loud.
Collocation deliberately did not move: its prompt is per-language knowledge, not
framing, and "natives usually say" for Portuguese is a claim Petal cannot back.
Not deployed and not smoked against a real model. The tests drive the real router
and a real DB; what none of them prove is how Qwen behaves on a Portuguese
document, in particular whether the never-translate line holds.
Claude-Session: https://claude.ai/code/session_01GJHNvirh7Hzhc9RL3HAvz7
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