f2dd30628affd55547abdfaa8e157c3e3058ea41
12
Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 prosolis
|
c6bf36bddf |
Triage the whole queue from the keyboard, and never type an n
The last of the UX review's item 8. Ctrl+. and Ctrl+, step through the underlines from anywhere in the text; the card that opens takes focus and answers Tab / Shift+Tab / Enter / Del / ? / Esc itself. Answering a card advances to the next by itself, and the last one closes and puts the caret back in the prose — so a document is triaged in five presses of Enter. The item asked for bare Tab or n/p. Neither can exist in a text editor: an unmodified letter is a letter. They work fine once a card holds focus, which is where the item wanted them; getting there needs a chord that is safe to press mid-sentence, and mid-composition, so the entry keys are IME-guarded like every other binding. The queue is the underlines read off the decoration DOM in document order, not the suggestion list: a stop she cannot see is worse than one she never visits, and it guarantees the card can anchor itself. Escape is stopped at the card. Unhandled it would also have left distraction-free mode, restoring the sidebar and — via the rail-follows-the- mode fix — pulling the rail out from under her mid-triage. The legend is bilingual and leads with the pair language, unlike the card's English buttons: those name what she is learning, this is an instruction for operating Petal, like the status bar. Key names are as printed on her keyboard (Entrée, Suppr, Intro, Supr). The es pack's own punctuation test caught the "?" and is right in general; the key cap is one named exemption. Verified in a real browser at 1517x810 on a fresh database with no model, over CDP — a keystroke feature deserves real keystrokes. Both layouts, wrap in both directions, the accept/dismiss/advance loop, Ask Petal and back, the full triage-to-empty criterion, and Accept-all clicked from a keyboard card. The wiring has no unit test for the reason items 6, 7 and 8 recorded: jsdom has no layout. triage.ts is pure and tested; browser-verified is written down as browser-verified. Claude-Session: https://claude.ai/code/session_01GJHNvirh7Hzhc9RL3HAvz7 |
||
|
prosolis
|
77f284f65c |
The zh pair's other direction, and a rule pack that mostly says no
`pair_lang` had always been answering a second question nobody asked: it says which two languages, and every surface built on it assumed English was the one being learned. That is why hanzi is never tokenized, never spell-checked, never glossed — correct for a Mandarin native practising English, backwards for an English native practising Mandarin. `users.direction` (migration 0016) separates the two questions; a `zh-learner` pair code would have been cheaper and would have made two directions of one pair look like two unrelated languages to every query. Segmentation is what replaces `wordAt` where there are no spaces: a shortest-path walk over log-probabilities, 232 ms and 14 MB for 188,522 words. The browser gets the word list because segmentation runs on hover; the server keeps the whole dictionary. Their coverage gates come out opposite on purpose — the client list is frequency-gated because the segmentation is measurably identical without the tail, and the dictionary is gated by nothing, because its only power is to explain and the word a learner stops on is the rare one. The 错别字 pack is 24 confusable pairs behind two mechanical gates. One admits a pair only if the wrong form is not a dictionary word and the right form is, which is why it refuses 自已 for 自己 — a real error whose wrong form is a headword. The other asks the segmenter whether the two characters already belong to two different words, without which 自己经常, 睡觉的时候 and 不知到底 would all be corrupted silently into text still made of real characters. Not deployed (this carries a migration), not seen in a browser, and no account has ever been in the learner direction. The IME composition guards were in scope and are not done — see BUILD_PLAN Phase 26. |
||
|
prosolis
|
b2d50e9136 |
A dismissed card stays dismissed, even offline
The server already suppressed every span she had accepted or dismissed, on
both the LLM reconcile and the mechanics pass, with tests either side. What
had no memory was the half that never asks it: item 3b's rule pack renders
250 ms after a keystroke with no network, and its record of "she already
answered this" was a set cleared on every document switch and added to only
for cards dismissed while still provisional.
So dismissing a persisted rule-pack card recorded nothing client-side and the
next keystroke put it straight back until the server's reply removed it again;
and after a reload the client knew nothing at all — permanently so with the
server unreachable, which is the case the rule pack exists for.
GET /docs/{id}/settled hands over the normalized originals of the document's
actioned rows, scoped through documents because an original quotes her
sentence. The client seeds a SettledSpans from it on open and adds to it for
every card that leaves, keyed on the original alone the way the server keys
it. The load adds rather than assigns, so a dismissal made while it is in
flight survives it.
normalizeForDedup now exists in both languages, compared across a network
boundary, so the same nine cases are asserted on both sides and each test
names the other.
Also: the status-bar count — "🌸 5片花瓣待打磨 · 5 petals to polish" beside the
word count, from the packs, hidden at zero. An empty rail already says nothing
is waiting; a badge announcing it after every check is a verdict, which the
review's non-goals rule out.
Verified in Chrome at 1517x810 with the server killed: a new violation was
detected, underlined and counted with no network, while the dismissed span
stayed gone.
|
||
|
prosolis
|
978cb80642 |
Ask Petal answers in both languages, with room to read
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. |
||
|
prosolis
|
25e415daa2 |
When she writes in Chinese, say Translate — not Clarity
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 |
||
|
prosolis
|
1f4ca4775a |
Let her choose her own pair
Raised by the user, not by the plan: there was no way to change language in the mobile UI. There was no way anywhere. `users.pair_lang` has been readable since Phase 19 and writable by nobody — /api/me was GET-only and Upsert deliberately skips the column — which is also why "no pt-PT account exists yet" has stood through two phases. Nothing could create one. PATCH /api/me answers with the whole user rather than 204, so the client re-reads the pair from the server instead of trusting its own request. One write reaches everything: langpack, Hunspell dictionary, Piper voice, lexicon provider and prompt language all read the column at use time. The server refuses a pair it has no copy for, and auth.shippedPairs is deliberately not internal/llm's list. That one names pairs the prompts can talk about (fr and es, since Phase 19); this one names pairs Petal can render itself in, which needs a langpack. Storing fr today would strand her on Chinese with no way back except a lucky guess at a button she cannot read. The picker sits in the sidebar footer because the sidebar is the mobile drawer — always one tap away. The status bar exists only while a document is open, which is the wrong moment to find the app speaking a language you can't read. Each language names itself, 中文 and Português: the one place bilingual copy would get in the way. Claude-Session: https://claude.ai/code/session_016y6gyuHkQXPiEuW8RGQyua |
||
|
prosolis
|
1bbc8fc8d3 |
Finish Phase 22: the half of Petal that works with the tunnel down
Grammar lite, the false-friend list, the daily invitation and the offline miscollocations — the four remaining §5–§6 items, all client-side and all alive on a box that cannot reach the model. The offline collocations forced a schema change. `type` had been doubling as the answer to "which engine found this" — `mechanics` meant offline — and that stops being true the moment an offline rule proposes a collocation. Migration 0013 adds `source` (llm | local) and every pass now scopes its DELETE by engine; without it the coach silently wiped every offline chunk on the page. Existing rows backfill by type, so a pre-0013 collocation row is claimed as the coach's, which it was: the offline list did not exist yet. The rule pack is hand-curated rather than mined, and the entries left out are the point — `married with` is wrong until "married with children", `arrive to` wants at or in depending on the noun. A pack running on every keystroke must not correct correct writing. Claude-Session: https://claude.ai/code/session_016y6gyuHkQXPiEuW8RGQyua |
||
|
prosolis
|
e9b8595456 |
Let the garden keep what she was given, not only what she sought
Two halves of the same idea, both read out of work Petal already records. Planting: an accepted collocation is a learnable chunk, so it becomes a phrase card. The scheduler didn't need to know — a three-word chunk climbs the ladder exactly like a looked-up word. What needed care was deciding what *isn't* a chunk (single words are word choice; a six-word-plus "collocation" is a rewritten sentence, and sentences make miserable flashcards), and that the example must be the *corrected* sentence — the stored draft still holds the phrasing she just left behind. Re-accepting the same chunk leaves the existing card alone rather than resetting a schedule it has been climbing. The whole thing is best-effort: accepting an edit must never fail because a flashcard couldn't be made. The growth journal: kept this month beside kept the month before, the phrasing that stuck, the patterns that faded. The queries were the easy part; the honesty is the feature. "Stuck" needs the phrase in a *second* document, because one document is just the edit where she left it. "Faded" says nothing at all unless she has been writing lately — otherwise a month away from Petal comes back to her as progress, which is the one way this could lie. And a suggestion had to start recording when she *decided* it, not when the model proposed it, so 0012 adds resolved_at and backfills the old rows to their created_at. It lives as a second tab in the garden, and it feeds the kitten: after an accept she now sometimes hears something true of her alone, once per line, half the time, never waited for. Claude-Session: https://claude.ai/code/session_016y6gyuHkQXPiEuW8RGQyua |
||
|
prosolis
|
24c3533e18 |
Give read-aloud a Portuguese voice, and a slower one
Phase 21's infra half. Two things the pt-PT pair needs from TTS, and one thing every learner has wanted since Phase 11. **A language is no longer a code change.** The handler knew exactly two languages, named in the Config struct: English on TTS_ENDPOINT and Chinese on TTS_ENDPOINT_ZH. Petal now discovers its Piper instances from the environment — English keeps the unsuffixed pair it has always had, and every other language is a TTS_ENDPOINT_<LANG>/TTS_VOICE_<LANG> pair — so fr and es cost a compose service and two lines of .env. <LANG> is the base tag, because an environment variable name cannot hold pt-PT's hyphen and only one Portuguese model is loaded either way. A language configured by halves is dropped rather than routed: half a configuration should reach the client as "no voice here, use Web Speech", not as an instance that errors on every tap. The startup line now names the voices it actually resolved rather than the English endpoint it was handed — the same lesson the dictionary line learned last week. **pt_PT-tugão-medium is the only European voice Piper ships.** The other five pt models in the catalogue are Brazilian, so the default anyone reaches for is the wrong country — the same trap as `dictionary-pt` packaging VERO, arriving through the catalogue rather than through the model. Named explicitly in compose, with the query that checks it in the deploy README. **The slow replay** (SUGGESTIONS §5e) is `slow: true` on /api/tts, raising Piper's length_scale to ~4/3. Piper stretches durations rather than resampling, so it stays a voice instead of a groan. The pace is part of the cache key — without it the slow replay of a word already heard at normal speed would be served back at normal speed, which is the one request where the difference is the whole point. 🐢 sits beside 🔊 on the word card, the selection bubble and the garden flashcard; the Web Speech fallback slows too, so the button means the same thing when Piper is down. **And the other reading gets her own voice.** The `alsoIn` block — the Portuguese sense of a word that is also English — now speaks in the pair's locale, which the pack names (`locale`) rather than anything inferring it from the letters. "comum" is spelled identically in both halves; a detector would have to guess, and this is the same reason the gloss shows both directions instead of picking one. Tests: config discovery (both existing deployment shapes, half-configured languages dropped, the pre-map voice defaults preserved), the slow scale and its separate cache entry, pt routing on the base tag with pt-BR landing on the European instance, and speech.ts's request body. The i18n shape suite now asserts every pack names a speakable locale in its own language — and that pt-PT's is not pt-BR. Verified: go build/vet/test, tsc, vitest 125/125, vite build. Live smoke against two fake Piper servers: en/pt × normal/slow all reached the right instance at the right length_scale with four distinct cache entries, and an unconfigured language still 404s. |
||
|
prosolis
|
ccb43e5a4d |
Phase 21: Petal learns to be an English+Portuguese pair
The plan said "Hunspell pt-PT vendored like en-US". Measuring that first is what saved it: nspell expands affixes eagerly on construction, and European Portuguese's 1,340 rules over 44,257 stems want over a gigabyte of browser heap — ~340 MB for the first 12,000 entries, and no return at all after three minutes on the whole file. So the expansion runs once at build time instead: 1,039,058 forms, 2.66 MB gzipped, read by the same nspell in 842 ms. The obvious npm package would also have shipped the wrong language. Both dictionary-pt and dictionary-pt-br carry VERO, the Brazilian word list, so vendoring by name puts pt-BR spellings behind a pt-PT label — the drift SUGGESTIONS §3 warns about, arriving through the packaging where no reviewer can see it. The source is Projecto Natura's, and the build script now asserts the fault lines (receção in, recepção out) before writing anything. Spellcheck consults both dictionaries and flags only what both reject, which is the no-detector answer to a pair with no script boundary. The word card does the same in the other direction: "data" is a word in both languages, so Petal shows both readings rather than guessing which she meant. Writing the tests caught the one real bug — extendedAlphabet was a snapshot while correct/suggest read live, and her dictionary arrives after English, so every lookup would have resolved "cora" while the underlines were already right. Not done, and not claimed: the pack has not been read by a pt-PT speaker, and the Piper voice is deferred with the deploy. Claude-Session: https://claude.ai/code/session_016y6gyuHkQXPiEuW8RGQyua |
||
|
prosolis
|
97e9c269ec |
Phase 20: the dictionary stops being English and Chinese only
Word lookups now come from DreamDict's dict.db for every pair but Chinese — opened read-only beside petal.db, no service, nothing over the VPN, because a hover gloss has to answer in milliseconds. `Provider` is the two questions the popover and the tooltip already asked, so the embedded *Lexicon satisfies it with no changes at all; Set.For(lang) is the single place the choice between them is made. The prerequisite in the dreamdict repo turned out to be two things, not one: the module path was unfetchable *and* the query layer sat in internal/, which no other module may import whatever the module is called. Both fixed upstream. The plan's central assumption did not survive the data. It mapped Gloss ← Translate(word, "en", L1) one-to-one; against the real 452 MB database that table answers for 17% of the 2,000 commonest English words into pt-PT. Wiktionary's translation sections are thin in that direction — "ephemeral", "think" and "quickly" have no en→pt-PT row at all. Shared WordNet synsets answer for 61%, so DreamDict gained Equivalents() and Petal glosses through it. Ordering those was wrong in an instructive way too: sorting by frequency glosses "think" as lembrar, "remember", because lembrar is the commoner Portuguese word even though pensar shares six of think's synsets to lembrar's one. Counting sense agreement first asks the right question. The same measurement is why zh stays on ECDICT: DreamDict reaches a Chinese gloss for 53% of those words, ECDICT for nearly all of them. The plan said converge only if quality holds. It didn't, so nothing converged. Two decisions about failure worth keeping. A missing dict.db is not an error — a laptop checkout has never had one — but a present-and-never-imported one is, because that is a half-finished deploy. And a pt-PT writer with no dictionary falls back to the embedded datasets with the gloss suppressed, keeping definitions, synonyms and phonetics rather than blanking the popover: an empty field reads as "not found", the wrong language reads as broken. The new fields surface as an etymology line and a three-band chip. Three, not five: the difficulty score separates "everyday" from "you'll have to explain this" but cannot rank obfuscate against serendipity, and a finer scale would be a confident-looking lie. An unscored word gets no chip. Writing the tests found two bugs first — trimEtymology sliced by byte, which would have emitted invalid UTF-8 for exactly the Greek and Latin etymologies the feature exists for, and its ellipsis path overran its own cap. go build/vet/test, tsc, vite, vitest 96/96 clean; live smoke against the real dict.db with one instance flipped from zh to pt-PT mid-run. Not deployed: go.mod still replaces github.com/prosolis/dreamdict with ../dreamdict, so the Docker build needs the two upstream commits pushed and the replace dropped. The deployed dict.db also predates DreamDict's Spanish data. Claude-Session: https://claude.ai/code/session_016y6gyuHkQXPiEuW8RGQyua |
||
|
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. |