Phase 28's step (b): both remaining items are about direction, and both had
a wrong answer that looked right.
isTranslation could not simply be read backwards. readsAsEnglish is a
deliberately low bar — Latin letters, not swamped by another script — which
every Portuguese sentence clears as easily as English does, so swapping its
two halves would have called every genuine Portuguese correction inside a
Portuguese document a translation. The flipped direction uses sentenceLang
from doclang.go instead, where English has its own curated marker list and
has to out-evidence the pair language to win. The English-document path is
untouched; reconcilePending carries the verdict to ask the question the
right way round.
The tap-through's whole observable change is a model call that stops
happening. /suggestions/{id}/translate now recovers the explanation's
language by re-running targetFor rather than assuming the pair, which gives
today's answer everywhere except the case that was broken: the Portuguese
writer whose explanation already arrived in Portuguese, previously
round-tripped through the model into Portuguese again. It answers "" there,
and the client's existing `res.translation.trim() || explanation` fallback
seeds the bubble with the explanation itself — no frontend change at all.
It deliberately does not render that explanation into English on the
grounds that English is technically the other half: an unasked-for
rendering into the language she is practising is noise, not a seed.
Tests pin both directions of the detector, with Portuguese-in-Portuguese as
the case the file exists for, plus four handler tests through the real
/check and /translate paths — including the skipped seed asserting the
model was never called, and the learning_pair zh learner whose English
explanation still renders into Chinese.
Left of the phase: (c) the garden's language tagging and read-aloud.
Claude-Session: https://claude.ai/code/session_01GJHNvirh7Hzhc9RL3HAvz7
She reaches for her own language mid-sentence when English won't come, and
Petal already handled it: it found the span and rendered it into English. It
just filed the result as a Clarity fix, so the pair model's flagship moment
read as tidying up her Chinese.
The type is now derived from the span rather than asked of the model. A type is
structural, and a model that re-reasons every pass would drift between labels
for a sentence nobody had touched — the instability the last session spent
itself removing. The label the model volunteers is still ignored.
Only the grammar checkpoint can be promoted. A pass with a forced type owns its
family: voice reads paragraphs for tone and its rows carry no replacement, so a
"translation" there would be a card offering nothing to accept.
zh is a different script and counting Han runes is close to certain. The Latin
pairs share an alphabet with English and get none of that, so they fall back to
function words and need two before Petal claims anything — with every word that
is also English left out, even the common ones. The heuristic is justified by
how cheap being wrong is: it changes a coloured pill, and nothing else.
The pill is the one bilingual type name in the rail. Every other type stays
English because those are the terms she is learning; this card's whole subject
is her own language. And it stops truncating its two lines — elsewhere the diff
is a word and the explanation is what she reads, but here the two sentences are
the card.
Two things only the running page could report. The inline underline was
invisible: the decoration carries a per-type class and the base rule is a
transparent border, so a type with no colour rule gets no mark at all. And at
1517×810 with the document list open there is no rail — the margin is 258 where
railEnabled wants 348 — so what she gets is the inline hover card. Item 7 is
written the other way round.
Migration 0015 rebuilds the suggestions table for the CHECK, which makes it the
first one here that could quietly drop her rows; there is a test that carries
every column, both timestamps and both indexes across it.
Claude-Session: https://claude.ai/code/session_016y6gyuHkQXPiEuW8RGQyua
A card vanishing and coming back seconds later, with different words, was
never about latency: every pass deleted its whole family and re-inserted
it, so each round minted new row ids. The rail keys on suggestion.id, so a
full remount was guaranteed — new id, new created_at (hence the re-fired
chime), and a fresh explanation from a model that re-reasons every time it
is asked. One unchanged mistake carried three different explanations in a
single sitting.
Passes now reconcile instead of replace. A re-proposed edit keeps its row:
its id, its created_at, and the wording she has already read. And the
grammar checkpoint stops asking about sentences nobody touched — the
document is split into hashed sentences, checked_chunks records which ones
a family has read, and only the difference is sent. When nothing changed
it doesn't call the model at all, and doesn't spend its rate-limit slot on
having done nothing.
The tone is part of a sentence's identity: cached advice was written for
the old register, so switching doc type re-reads every line.
replaceMechanics reconciles too, which mattered more than expected — the
rule pack fires 250 ms after a keystroke, so it was re-minting every local
card's id several times a sentence.
Only the grammar checkpoint is chunked. Voice is a property of the whole
document, and the collocation coach is a button she pressed asking for a
fresh read.
No client change was needed; stable ids were the whole of it.
Claude-Session: https://claude.ai/code/session_016y6gyuHkQXPiEuW8RGQyua
prosolis