petal

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Author	SHA1	Message	Date
prosolis	76dede8856	Correct the language she wrote in, not the one she was practising 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	2026-07-28 23:20:53 -07:00
prosolis	336cae93e0	Phase 19: the copy stops being hardcoded Mandarin 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.	2026-07-27 08:37:05 -07:00
prosolis	8aa437ec82	Phase 12 + 13: collocation coach + vocabulary garden Phase 12 — collocation coach: a third suggestion family for gentle "natives usually say…" hints on non-native word pairings, reusing the existing runPass/pendingScope/rail machinery. - llm/collocation.go (RunCollocation, 25s floor, reuses ParseCheckpoint) + collocationSystemPrompt/CollocationMessages (warm, Mandarin gloss, defers grammar/spelling to the grammar family) - migration 0005 rebuilds the suggestions table to extend the type CHECK (SQLite can't ALTER a CHECK) - collocationScope + CollocationLimit + POST /{id}/collocation - fix: grammarScope was `type != 'voice'` and would wipe the new collocation flags; now `type NOT IN ('voice','collocation')` - frontend: --color-blossom, "Make it sound natural 🌸" pill, collocating/runCollocation in useCheckpoint, StatusBar dot Phase 13 — vocabulary garden: capture looked-up words and surface them for gentle spaced repetition. - new internal/vocab package: migration 0006 (vocab_words, SM-2-lite columns, doc_id ON DELETE SET NULL, UNIQUE(user_id,word)), scheduler.go (Leitner ladder 1/3/7/16/35 then geometric; gentle "again", no streak-shaming), handlers (capture-upsert/list/due/ review/delete, owner-scoped, SQLite-side datetime math) - auto-capture on word lookup (dictionary-known words only, captures the surrounding sentence + doc_id) + 🤍/💚 toggle on WordCard - GardenPanel: blossom grid (bloom by reps), flashcard review (sentence blanked, flip, again/good/easy, recognition↔production), sleepy-kitten footer; opened from a global 🌷 header button Tests: TestCollocationPassCoexists, vocab scheduler + handlers, db CHECK extended. go build/vet/test + tsc + vite + vitest (51/51) clean; migration verified against a copy of the live DB; live backend smoke walked the full vocab lifecycle + the warm-502 collocation path. Claude-Session: https://claude.ai/code/session_016Yr6jELuRc7hyzYLccQKZd	2026-06-26 15:50:25 -07:00

Author

SHA1

Message

Date

prosolis

76dede8856

Correct the language she wrote in, not the one she was practising

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

2026-07-28 23:20:53 -07:00

prosolis

336cae93e0

Phase 19: the copy stops being hardcoded Mandarin

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.

2026-07-27 08:37:05 -07:00

prosolis

8aa437ec82

Phase 12 + 13: collocation coach + vocabulary garden

Phase 12 — collocation coach: a third suggestion family for gentle
"natives usually say…" hints on non-native word pairings, reusing the
existing runPass/pendingScope/rail machinery.
- llm/collocation.go (RunCollocation, 25s floor, reuses ParseCheckpoint)
  + collocationSystemPrompt/CollocationMessages (warm, Mandarin gloss,
  defers grammar/spelling to the grammar family)
- migration 0005 rebuilds the suggestions table to extend the type CHECK
  (SQLite can't ALTER a CHECK)
- collocationScope + CollocationLimit + POST /{id}/collocation
- fix: grammarScope was `type != 'voice'` and would wipe the new
  collocation flags; now `type NOT IN ('voice','collocation')`
- frontend: --color-blossom, "Make it sound natural 🌸" pill,
  collocating/runCollocation in useCheckpoint, StatusBar dot

Phase 13 — vocabulary garden: capture looked-up words and surface them
for gentle spaced repetition.
- new internal/vocab package: migration 0006 (vocab_words, SM-2-lite
  columns, doc_id ON DELETE SET NULL, UNIQUE(user_id,word)),
  scheduler.go (Leitner ladder 1/3/7/16/35 then geometric; gentle
  "again", no streak-shaming), handlers (capture-upsert/list/due/
  review/delete, owner-scoped, SQLite-side datetime math)
- auto-capture on word lookup (dictionary-known words only, captures
  the surrounding sentence + doc_id) + 🤍/💚 toggle on WordCard
- GardenPanel: blossom grid (bloom by reps), flashcard review (sentence
  blanked, flip, again/good/easy, recognition↔production), sleepy-kitten
  footer; opened from a global 🌷 header button

Tests: TestCollocationPassCoexists, vocab scheduler + handlers, db CHECK
extended. go build/vet/test + tsc + vite + vitest (51/51) clean;
migration verified against a copy of the live DB; live backend smoke
walked the full vocab lifecycle + the warm-502 collocation path.

Claude-Session: https://claude.ai/code/session_016Yr6jELuRc7hyzYLccQKZd

2026-06-26 15:50:25 -07:00