acb35108c07627c33a2d399986ebd866a5570910
17
Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 prosolis
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466055020f |
The garden learns which language a card is in, and read-aloud stops guessing
Phase 28 (c), the last of the phase. A word met inside a Portuguese document is a Portuguese card: migration 0018 mirrors documents.doc_lang onto vocab_words, set server-side from the ownership lookup capture was already making. Every card still reviews — filtering the queue to the half she is learning would drop the words she actually met. Read-aloud was the larger surprise. detectLang routed Han/kana to Chinese and everything else to en-US, so the zh pair was accidentally right and every Latin pair wrong. doc_lang now reaches the client read-only on the document JSON, and docLang(text, verdict) answers for a passage taken out of it — with the script test still winning, because quoted Chinese must never be spelled out one "Chinese letter" at a time. Claude-Session: https://claude.ai/code/session_01GJHNvirh7Hzhc9RL3HAvz7 |
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prosolis
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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
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prosolis
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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. |
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prosolis
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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 |
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prosolis
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10e8aef86c |
Stop regenerating the world on every check
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 |
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prosolis
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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 |
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prosolis
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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 |
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prosolis
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30d5e691c9 |
Phase 18: settings that belong to the writer, not the browser
The mute toggle, the falling-petals toggle and the chosen companion lived in localStorage, which is a property of the machine. Now that two people can sign in to one Petal, sharing a laptop would have meant sharing a mascot and one person's silence muting the other. Each key is namespaced by user id. The awkward part is timing: sounds.ts and petals.ts read their value the moment they are imported, long before /api/me can have answered. Rather than block startup on the network for a mute flag, a read before the answer arrives sees the old un-namespaced key -- on a single-writer browser, exactly the right value -- and setPrefsScope then adopts it into that account's namespace and tells every reader to look again. Adoption moves rather than copies, so the first account inherits what was set before accounts existed and the second starts from Petal's defaults. The personal spelling dictionary moves further than that: onto the server. It is built from her own writing, so it should not be readable by whoever sits down at the same browser next -- but merely namespacing it would have split the list she already has between her laptop and her tablet, which is worse than where we started. A table keyed (user_id, lang, word) follows her instead. The lang is the dictionary's, not hers: an English exception must not silence a pt-PT flag once the second pair ships. Adding a word takes effect in the editor immediately and persists in the background, so the underline goes away the instant she asks. A browser still holding the old list hands it over on first load, and only lets go once the server has taken it. Claude-Session: https://claude.ai/code/session_016y6gyuHkQXPiEuW8RGQyua |
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prosolis
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1cf207d73f |
Phase 16: Petal authenticates for itself
Petal is now an OIDC client in its own right rather than trusting a header from the proxy. The Phase-0 Resolver seam was the only integration point: main.go picks the session store when Authentik is configured and the static local user otherwise, and no handler or query moved for either. internal/auth gains three pieces. session.go issues an opaque cookie token and stores only its SHA-256, so a database copy yields nothing usable; the 30-day expiry slides on every request, throttled to one write an hour, and logout deletes the row rather than just the cookie. oidc.go runs the authorization-code flow with state, nonce and PKCE, and discovers the provider lazily and on retry — an Authentik outage should block new logins without stopping Petal booting or invalidating live sessions. users.go provisions accounts from the token's claims and gates them on an allowlist that matches emails as well as subject ids, since a subject is an opaque uuid that doesn't exist until someone has already logged in once. Migration 0010 lands sessions, images and users.pair_lang together. The images table closes the capability-URL hole the Phase-0 audit flagged: a hash was previously enough to fetch anyone's picture. Rows are keyed (name, user_id) so one file can have several owners and deduplication survives; a stranger gets 404 rather than 403, the cache header drops to private, and files already on disk are claimed at startup or every image already pasted into a document would 404. On the frontend a single 401 interceptor feeds a warm bilingual sign-in overlay, drawn over a still-visible editor because nothing has been taken away. Behind it is the part that matters: a save that comes back 401 stashes its body to localStorage before anything else and stops the auto-save loop, and reopening that document after signing in merges the draft back and saves it. An expired session must not cost writing. Writing the round-trip test against a stub identity provider turned up a real bug: the one-shot state/nonce/PKCE cookies were cleared in a defer, which runs after the redirect has written the response header, so the clearing Set-Cookie was silently dropped and they lingered for their full ten minutes. Also swaps the emoji favicon for a drawn sakura, which renders as Petal's own rose palette everywhere instead of whatever each platform's font decides, and doubles as the app tile in Authentik. Migration 0010 verified against a VACUUM INTO copy of the live millenia database: counts intact, FTS still matching, the one existing image claimed. Claude-Session: https://claude.ai/code/session_016y6gyuHkQXPiEuW8RGQyua |
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prosolis
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78ed1dd281 |
Writing passport: evidence of process instead of an AI score
She's submitting work that gets run through an AI detector and wants to pre-check she won't be wrongly flagged. Petal should not answer that with a detector of its own: they misfire badly on non-native English (Stanford 2023 found >50% of TOEFL essays flagged as AI vs. near-zero for native writers), so a percentage aimed at an ESL writer is worse than nothing — it either scares her off her own voice or gives false comfort. So the artifact is provenance, not a verdict. Petal already snapshots every ~3 minutes; this turns that history into a standalone printable report: session breakdown, word-count growth, span, active time. No score is emitted anywhere. Two schema additions back it. preserve_history opts a document out of the 40-snapshot prune cap — right for recovery, wrong for provenance, where you want the whole span including the oldest rows. content_hash/prev_hash chain each snapshot to the one before it, so a history edited or thinned after the fact fails verification. Pruning legitimately severs links, so a link break reports as "gaps" unless preserve_history is on; only a hash that fails against its own contents is unconditionally "broken". The chart's x axis is snapshot order, not wall-clock, and that is the load -bearing decision. On a linear time axis an essay written in three sittings across three days renders as three vertical cliffs separated by empty space — visually identical to text pasted in three chunks, i.e. the report would have argued the opposite of the truth. Breaks are compressed into explicitly labelled gutters instead. TestChartGivesWidthToWriting pins it. The report volunteers its largest single word-count jump and states its own limits: it cannot show who was at the keyboard, or whether typed text was composed or copied in. Overclaiming would be self-defeating — a reader who catches it overstating discounts all of it. HTML rather than server-rendered PDF, as with the other exports: a CJK-safe PDF needs an embedded Unicode font or a headless browser. Print styles are there so the browser's Save as PDF is the handoff path. Claude-Session: https://claude.ai/code/session_016Yr6jELuRc7hyzYLccQKZd |
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prosolis
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96f68a91ee |
Add deterministic mechanics suggestion family (rule-based, no LLM)
Reuse the companion's prose.ts rules engine as the single source of
deterministic detection instead of duplicating it. Applyable rules now
also emit exact-span fixes (original -> replacement) that surface as
suggestion cards; awareness-only rules (run-ons, splices, ...) stay
companion bubbles. The companion hides fix-bearing hints so a span is
never both a bubble and a card.
Spans are widened to a distinctive phrase ("a old" -> "an old",
"She have" -> "She has") so they re-anchor by string in the editor; a
lone lowercase "i" stays awareness-only since a single char can't anchor.
Backend: detection lives client-side, so the new persist-only
POST /docs/{id}/mechanics endpoint receives findings and stores them as
the 'mechanics' family with their exact offsets. It honours
actioned-suppression, leaves the LLM families untouched, and a checkpoint
no longer wipes it. fetchPending dedupes spans with mechanics winning any
collision against an LLM card (its span is exact). Migration 0008 adds the
'mechanics' suggestion type.
Client renders the mechanics fixes immediately (no LLM wait) and the cards
use a calm sage "Tidy-up" accent.
Verified end-to-end in a real browser on millenia: detect -> persist ->
render -> accept applies the fix.
Claude-Session: https://claude.ai/code/session_016Yr6jELuRc7hyzYLccQKZd
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prosolis
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4161830da6 |
Code-review fixes for collocation coach + vocab garden
Correctness: - useCheckpoint: clear the busy flag unconditionally so overlapping explicit passes don't strand each other's spinner; explicit actions now also supersede a queued auto-check and clear the stranded "checking" dot. Deduped runVoice/runCollocation into runExplicitPass. - EditorCore: token-guard the auto-capture so a late capture can't resurrect a removed word; move toggleSaveWord side effects out of the setWordInfo updater (StrictMode double-fire); fix sentenceAround offset desync via shared exampleAt (textBetween + parentOffset, single resolve); optimistic saved state so the heart doesn't flash unsaved. - vocab capture: normalize word to lower+trim (matches lexicon) so "Apple"/"apple" don't make duplicate cards; check rows.Err() in queryList. - GardenPanel: Promise.allSettled so a /due failure doesn't blank the whole garden; scrim click during review ends the review (mirrors Esc); gate footer on !error; O(1) due lookup via a Set. Features requested in review: - Definition-only review card: add vocab_words.definition (migration 0007) as an English fallback meaning, threaded through capture and used by review/garden when there's no Chinese gloss. - Scheduler caps: maxEase 3.0 + maxInterval 365d so "easy" growth can't push a word out of rotation for years. Tests: TestCaptureCaseInsensitive, TestCaptureStoresDefinitionFallback, TestCapsBoundGrowth. go build/vet/test, tsc, vitest 51/51, vite build clean. Claude-Session: https://claude.ai/code/session_016Yr6jELuRc7hyzYLccQKZd |
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prosolis
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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
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prosolis
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9e141e4169 |
Phase 10: organization & polish — cross-doc search, tags, touch, warm failures
Cross-document FTS5 search (trigram tokenizer for EN + space-free CJK, kept in sync by triggers, back-filled from existing docs). GET /api/search uses the FTS index for queries >=3 runes and a LIKE fallback for 1-2 (so 2-char Chinese words resolve); snippets are built in Go with rune-aware boundaries and sentinel highlights. Tags: user-scoped tags + document_tags join (both cascade), idempotent create/assign, per-tag doc counts. Doc list and search carry each doc's tags (one tagsByDoc query). Frontend: useTags, TagChip/TagPicker/SearchBox, rewritten DocList with chips + filter bar + search. Tablet/touch: responsive sidebar drawer (hamburger + scrim <768px), coarse- pointer tap targets, tap-to-open + outside-pointerdown-close for suggestion cards. Warm LLM-down state: useCheckpoint llmDown flag drives a gentle bilingual StatusBar note (writing still saves locally). Migration 0004 (tags + FTS). Tests: tags lifecycle, search EN/CJK/LIKE/update- reindex. go build/vet/test, tsc, vite all clean; verified live on deployment host. Claude-Session: https://claude.ai/code/session_016Yr6jELuRc7hyzYLccQKZd |
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prosolis
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8e1111d768 |
Phase 8: version history + export (trust foundation)
Version history: new document_versions table (migration 0003) holding full-body snapshots that cascade with the doc. Throttled auto-snapshots on save (>=3min apart, max 40/doc, pruned), explicit manual restore points, and a pre_restore safety copy taken before each restore so restoring is itself undoable. Endpoints under /api/docs/:id/versions, all owner-scoped. Empties and bare renames never snapshot. Export: pure-Go Tiptap-JSON -> Markdown / HTML / plain-text / docx (no cgo/pandoc, single-binary intact), CJK-safe with RFC 5987 filenames. docx is a hand-built OOXML zip. PDF is handled client-side via the browser print dialog + an @media print stylesheet so CJK renders with the reader's own fonts. Frontend: ExportMenu (downloads + Print/PDF) and HistoryPanel (snapshot list, preview, restore) wired into the title row; bilingual zh-first to match chrome. Restore remounts the editor via editorEpoch. Stop saving empty docs: blank Untitled drafts now reuse-on-create and self-discard when navigated away from (refs avoid stale closures). Tests: versions_test.go, export_test.go (incl. valid-zip docx). go build/vet/test, tsc, vite build all clean; live end-to-end smoke verified snapshot/throttle/restore/export. Claude-Session: https://claude.ai/code/session_016Yr6jELuRc7hyzYLccQKZd |
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prosolis
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4c288834c0 |
Editor: document tone, right-click word lookup, expanded stats
Four enhancements to make the editor fit real school usage:
- Per-document tone (academic/professional/casual/humorous/creative/
persuasive/general): new documents.tone column (migration 0002), threaded
through the docs API, a bilingual ToneSelect dropdown on the title row, and
injected into the grammar-checkpoint LLM prompt so advice fits the register.
The voice pass stays tone-agnostic.
- Right-click word lookup: a new offline `lexicon` package serves definitions
(Wordset, modern ESL-friendly glosses) and synonyms (WordNet synsets first,
then frequency+stopword-ranked Moby for breadth) from gzipped embedded data,
behind /api/word/{word} with light morphology. The WordCard popover shows the
definition and tappable synonym pills that swap the word in place.
- Expanded writing stats: clicking the word count opens a StatsPanel with page
count, sentences, paragraphs, reading time, average word length, word variety,
and Flesch-Kincaid reading level — all computed client-side.
Claude-Session: https://claude.ai/code/session_016Yr6jELuRc7hyzYLccQKZd
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prosolis
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9c98e97030 | Phase 1: data layer (SQLite, migrations, models, seed) |