Phase 24, the fr half: langpack, Hunspell dictionary, Piper voice, and the
lexicon coverage that turned out to have been measured already (63.1%, better
than pt-PT's 62.1%). No migration; not deployed.
The plan recorded that build_ptpt_dictionary.py "generalizes" to French. It
did not. It handled single-character flags and plain PFX/SFX and stopped on
everything else, and fr.aff uses four of the things it stopped on. FLAG long
is the dangerous one: French flags are two characters, so the old reader's
set(flagstr) yields a bag of unrelated letters and expands every entry through
the wrong paradigm without ever erroring. Plus continuation flags (French
really does affix an affixed form), NEEDAFFIX on 68,075 of 84,140 stems, and
FULLSTRIP. Renamed build_hunspell_dictionary.py with a per-language profile,
asserting that CIRCUMFIX and FORBIDDENWORD are still unused rather than
assuming it — and it rebuilds pt-PT byte-identical to the shipped asset, which
is the only thing that makes "generalized" a claim rather than a hope.
Elision was decided by building both halves and measuring. Keeping l'arbre and
its thirty-three siblings: 3,159,832 forms, 8.25 MB gzipped. Dropping them:
473,326 and 1.19 MB. They are not new words, but the tokenizer keeps internal
apostrophes, so they genuinely would have been underlined — so they moved out
of the dictionary into withElision, which splits at a known clitic and still
requires the remainder to be a word (l'zzzz stays flagged). Real nspell: 369 ms
and 74 MB, against pt-PT's 842 ms and 139 MB, on the larger language.
Where the regional trap lives is the mirror image of Portuguese's: every fr_*
Piper voice is fr_FR and Debian's fr_FR/fr_CA/fr_BE dictionaries are one shared
word list, so nothing can be quietly wrong about the country and the whole
decision sits in the copy. What French has instead is the 1990 reform, packaged
three ways; comprehensive ships, because Petal never corrects her French and
coût and cout are both correct.
Then the interim review pass, at the user's suggestion and explicitly "for
now": four models read each Latin pack independently, and only findings at
least two of them reached on their own were applied — five per pack. It earned
its keep on the pack that was already live. pt-PT was carrying pre-Acordo
spellings (adjectivos, actualmente) in a file whose own header commits to
post-Acordo, plus Brazilian decepção, because the Phase 21 greps checked for
Brazilian vocabulary and never checked the pack against its own spelling
policy. That grep now exists and was confirmed to fail on the old text before
being kept. Where reviewers agreed a line was wrong but split on the fix, the
wording is mine and the reasoning is in BUILD_PLAN rather than averaged away.
Still owed, and both packs now say so precisely: a quorum of models agreeing is
agreement, not authority. No native speaker has read either pack, and none of
this has been seen in a browser.
go build/vet/test clean, tsc, vite build, vitest 190/190.
Claude-Session: https://claude.ai/code/session_016y6gyuHkQXPiEuW8RGQyua
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
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
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
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.
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
It logged dictionary.Langs(), which is a compile-time constant of the languages
DreamDict *supports*. The database deployed until today supported Spanish and
contained none of it, so the line printed a confident "[en fr pt-PT es zh]"
over a file where every Spanish lookup came back empty — the exact failure the
line exists to catch, reported as success.
Contents() counts rows per language instead. For a file somebody has to copy
onto the box by hand, "what is in it" is the only question worth asking, and
the answer is now en=136615 es=102971 fr=56096 pt-PT=136300 zh=120883.
Claude-Session: https://claude.ai/code/session_016y6gyuHkQXPiEuW8RGQyua
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
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.
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
The destination is an OIDC subject id, which the app cannot know — it
belongs to the identity provider — so this runs deliberately, with Petal
stopped and a backup taken, rather than as a startup migration. documents,
tags, vocab_words and images carry user_id directly; versions, suggestions
and tag assignments hang off their parents and follow, which is why it has
to be one transaction with foreign keys off. Sessions for the old identity
are deleted rather than moved: a session is proof someone signed in, and
nobody ever signed in as 'local'.
Dry run by default, VACUUM INTO backup first, and it verifies every row it
expected to move actually moved — and that the source is left owning
nothing — before committing.
The 'is the app stopped?' guard took two attempts. BEGIN EXCLUSIVE, the
obvious check, sails past a running-but-idle Petal because in WAL mode it
only conflicts with another writer, which is precisely the case worth
catching. PRAGMA locking_mode = EXCLUSIVE conflicts with any connection at
all, since it locks the shared-memory index every WAL reader maps.
Sequencing this also turned up a crash waiting to happen: the image
backfill claims unowned files for 'local', which no longer exists after a
migration, and the resulting foreign-key error is fatal inside images.New.
Petal would have crash-looped the first time it started on a migrated
database. It now skips a missing owner, which costs nothing — the
migration moves the image rows itself.
Claude-Session: https://claude.ai/code/session_016y6gyuHkQXPiEuW8RGQyua
Deploying against the real Authentik turned this up immediately: its issuer
ends in a slash, OIDC requires the discovered issuer to match the configured
one byte-for-byte, and trimming it made discovery fail every time. The stub
in the tests happened to advertise a slashless issuer, so the whole suite
passed while the only provider Petal actually talks to could not be reached.
The stub now takes its issuer as a knob, and a regression test runs the flow
against one that ends in a slash.
Claude-Session: https://claude.ai/code/session_016y6gyuHkQXPiEuW8RGQyua
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
piper-tts 1.6.0 moved synthesis from POST / to POST /synthesize, with an
identical request body; the VPS sidecars run 1.6.0 and returned 405 to
every read-aloud request, while millenia's older server still expects /.
Rather than pinning both deployments to one Piper release, the path is
configuration -- default "/" keeps millenia working untouched, and the
compose stack sets /synthesize. The container healthcheck moves with it,
since it was probing the old route too.
Deploy plumbing so Petal can run on the public VPS behind the Traefik
already on that box, with vLLM reached over headscale.
- Dockerfile: node build -> go build -> alpine runtime. CGO stays off
(modernc SQLite is pure Go), so the runtime layer exists only for
ffmpeg (read-aloud transcodes Piper's WAV) and tzdata (the companion's
bedtime nag and night mode read the local clock). Runs as uid 10001
with /data as the single writable mount.
- docker-compose.yml: Traefik labels following this host's convention
(external `traefik` network, `web-secure` entrypoint, `default` cert
resolver). Petal publishes no host port. ./data is a bind mount, not a
named volume, so the nightly backup and a restore are reachable from
the host.
- Piper runs as two sibling containers rather than host systemd units.
The plan assumed Piper was already installed on the VPS; it is not,
the host has no lingering user session to keep user units alive, and
containers keep the TTS ports on an internal network unreachable from
anywhere but Petal. One image, voice chosen per service, model cached
in a shared volume -- so the pt-PT voice is a new service, not a new
image.
- db.Backup + a `-backup` flag: VACUUM INTO, not a file copy. Petal runs
in WAL mode, so the newest committed pages may live in petal.db-wal;
copying the three files separately can capture a torn mid-checkpoint
state. VACUUM INTO reads one coherent snapshot without taking a write
lock, and emits a single file with no -wal/-shm companions. Refuses an
existing destination so a failed run can't destroy the last good
backup.
- deploy/backup-petal.sh: nightly snapshot, compress, push to millenia
over headscale with a post-transfer size check, prune both sides.
- deploy/petal.env.example: LLM_TIMEOUT raised 30s -> 90s for the
WAN+VPN round trip, since the voice and collocation passes send a
whole document and the timeout is a hard deadline on Complete.
Petal ran as a single hardcoded user, with db.LocalUserID named directly
at ~35 query sites. That made the caller's identity a compile-time
constant scattered across every package — nothing a real login could
replace without touching all of them.
New internal/auth moves it into the request context:
- Middleware(Resolver) resolves the caller once per API request
- handlers read auth.UserID(r.Context()) instead of naming a user
- Resolver is the seam an Authentik session check drops into
- StaticResolver(db.LocalUserID) keeps Petal single-user today
Behavior is unchanged. UserID returns "" rather than panicking when the
middleware is absent, so a mis-wired route fails closed: every query is
WHERE user_id = ?, which then matches nothing.
main.go splits /api into a public group (/health, /version) and an
authenticated group for everything else — a monitoring probe must not
need a session.
Two pre-existing access-control gaps fixed while threading, both
harmless with one user and not with two:
- setStatus (accept/dismiss) updated a suggestion by bare id with no
ownership check at all
- listForDoc/fetchPending read a document's suggestions by doc_id
alone; a suggestion quotes the sentence it corrects, so that leaked
the source prose
Both now scope through documents.user_id.
Tests: internal/auth covers the context round-trip, the absent-context
case, and both 401 paths. Two-user isolation suites in docs and
suggestions mount the same routers twice behind two resolvers over one
database and assert a stranger gets 404 on every id-taking path, sees
nothing in list/search, and leaves the owner's data untouched.
Those suites earned their keep immediately: docs.fetch gained a userID
parameter but kept binding db.LocalUserID in the query. Unused
parameters are legal Go, so it compiled clean, vet was silent, and every
existing test passed while the lookup stayed unscoped.
Still global, out of scope and flagged in BUILD_PLAN.md: the image store
has no per-user association, and frontend localStorage keys are
per-browser rather than per-account.
Qwen3-family models reason by default and prepend a plain-text preamble
ahead of the answer — not a <think> block, so it cannot be stripped
after the fact. Every Petal pass parses a JSON object out of the
completion, so an unsuppressed preamble fails the parse outright.
Send chat_template_kwargs {"enable_thinking": false} on every request,
matching the unconditional think:false the Ollama backend already sends.
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
Suppression keyed on the exact original->replacement pair, which the
model routinely sidestepped: it reverses an accepted edit (reverse
pair), re-polishes its own accepted output (new original == accepted
replacement), and the editor's smart-quote churn ("..." -> '...')
defeated even a byte-exact match. Result: a few sentences got nudged
back and forth pass after pass.
Replace the exact-pair actionedKeys with a suppressor that compares
under a normalization folding all quote variants and collapsing
whitespace, and drops a fresh suggestion when it re-touches an
already-settled span: same edit re-proposed, an original the user
already accepted/dismissed, the model re-touching its own accepted
output, or a multi-word sub-clause contained in an accepted span.
Tradeoff: once a sentence is accepted/dismissed it won't be re-flagged
until its text changes — stability over marginal improvement, the right
call for the calm ESL persona.
Claude-Session: https://claude.ai/code/session_016Yr6jELuRc7hyzYLccQKZd
The checkpoint model sometimes "flags" a correct sentence and echoes it
verbatim as the replacement, producing a card whose before/after are
identical and whose explanation says it's already fine — a suggestion
that suggests nothing. ParseCheckpoint already dropped empty-original
items; also drop these no-ops. Awareness-only families (voice) carry an
empty replacement, so the guard only fires on the edit families.
Claude-Session: https://claude.ai/code/session_016Yr6jELuRc7hyzYLccQKZd
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
Backend:
- Extract shared internal/httputil (WriteJSON/ErrorJSON/BadRequest/
ServerError); drop the triple-duplicated helpers in docs, suggestions,
vocab. ServerError now logs the real error and returns a generic 500 so
raw DB/internal errors never reach the client.
- vocab capture: validate doc_id ownership (blank -> none, unknown -> 400
instead of a leaked FK 500); rune-safe clamp word/gloss/definition/
phonetic/example.
- vocab review(): wrap the read-modify-write in a transaction (TOCTOU).
- /api request-size cap via MaxBytesReader middleware (2 MiB), exempting
/api/images (own 10 MiB limit).
Frontend:
- StatusBar: drive the checking/voicing/collocating indicators from one
array; llmDown uses !anyBusy.
- Slide-overs: new useFocusTrap hook (focus-in, Tab trap, focus-restore)
on GardenPanel + HistoryPanel, both role=dialog/aria-modal/aria-label.
- speech.ts: export stopSpeech(); GardenPanel cancels audio on unmount.
Tests: add doc_id-validation and field-clamp coverage; full suite green.
Claude-Session: https://claude.ai/code/session_016Yr6jELuRc7hyzYLccQKZd
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
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
The grammar checkpoint capped num_predict at 1024, but qwen3.5:9b ignores
the prompt's "up to 5 issues" and emits ~17-20 suggestions (~2000 tokens)
on a 300+ word doc. The output hit the cap mid-array (done_reason=length),
the JSON never closed, and ParseCheckpoint found no parseable object -> a
502 in ~20s on every long doc (1024 tok @ ~50 tok/s, not a network
timeout). The repeated failures made the writing-assist helper look
permanently asleep.
Fix:
- Raise the cap to checkpointMaxTokens=4096. It is a ceiling, not a
target -- the model stops at its JSON close, so shorter docs are
unaffected; only genuinely long outputs use the headroom.
- Make ParseCheckpoint salvage the completed {...} suggestion objects
from a truncated array (refactor extractJSONObject onto a shared
firstBalancedObject scanner), so an over-long doc degrades to partial
feedback instead of a hard 502.
Verified live on millenia: 'The Missing Key' (382 words) now returns 200
with 17 suggestions in ~38s, previously 502 every time.
Claude-Session: https://claude.ai/code/session_016Yr6jELuRc7hyzYLccQKZd
A paste fires exactly one grammar checkpoint, and a failed one never retried
until the next keystroke — stranding the writer on "Petal's helper is resting"
after a paste. Long docs make it worse: their 15-25s checks have a wide window
to catch a transient 502 from the shared Ollama (co-tenant apps load other
models and evict the 9B). A failed pass also burned the per-document rate-limit
slot, so a retry within 30s hit the throttle path and got an empty set back.
- llm.RateLimiter.Release rolls back a slot when its pass fails; Allow now
returns the recorded timestamp so Release only frees its own slot.
- suggestions.runPass releases the slot on LLM failure before returning 502.
- useCheckpoint auto-retries a failed checkpoint with backoff (3/12/35s),
keeping the breathing dot up and only flagging "resting" once retries exhaust.
Claude-Session: https://claude.ai/code/session_016Yr6jELuRc7hyzYLccQKZd
Surface every outstanding suggestion as a card in the right-hand
whitespace, vertically aligned to the text it flags — so the writer sees
the whole queue at once instead of hovering each highlight. Cards stack
with collision avoidance, link both ways with their highlight (hover/click
↔ soft text wash, driven through the decoration plugin so it survives
edit repaints), and carry the same Accept / Dismiss / Ask Petal actions.
The rail is a progressive enhancement: it mounts only when there's room
beside the editor, otherwise the existing inline hover card is unchanged.
Stacked cards that reach the bottom-right corner tuck behind the
companion mascot (z-order).
When a card is expanded, the Ask Petal bubble now opens with the
Simplified-Chinese translation of the explanation (the English stays in
the card body) instead of repeating the same text twice — a new
POST /api/suggestions/{id}/translate one-shot LLM endpoint, loaded
lazily on open with an English fallback.
Verified live against the local LLM via the uitest harness: rail
stacking, hover↔text wash, expand/Ask Petal, accept-from-rail, narrow
fallback, and the Mandarin bubble.
Claude-Session: https://claude.ai/code/session_016Yr6jELuRc7hyzYLccQKZd
Replace the browser's robotic Web Speech API (espeak on Chromium) as the
primary read-aloud path with server-side Piper neural TTS, served by petal
and kept fully offline on millenia next to Ollama.
- internal/tts: proxy short passages to Piper, transcode WAV -> mp3/opus via
ffmpeg, content-addressed disk cache (instant re-taps). Each Piper instance
loads one voice, so language routes to its own endpoint:{voice}. Unknown
language -> 404 so the client falls back to Web Speech. UTF-8-safe truncation
for multibyte (Chinese) text.
- config: TTS_ENDPOINT / TTS_ENDPOINT_ZH / TTS_VOICE_EN / TTS_VOICE_ZH /
TTS_CACHE_DIR / TTS_TIMEOUT / TTS_AUDIO_FORMAT. Route mounts only when
TTS_ENDPOINT is set; otherwise unchanged behavior.
- web/audio/speech.ts: speak() hits /api/tts first, falls back to Web Speech on
any failure; rapid-tap-safe via a request token. Call sites unchanged.
- deploy/: Piper user systemd units (EN :5005, ZH :5006), setup script, README.
English (en_US-amy-medium) and Chinese (zh_CN-huayan-medium) are both live.
Claude-Session: https://claude.ai/code/session_016Yr6jELuRc7hyzYLccQKZd
Two fixes for the "accept Petal's change, then it nags about the same
sentence moments later" report:
- replacePending now suppresses any freshly-generated suggestion whose
original->replacement matches one the user already accepted or
dismissed for that doc. The model has no memory between passes, so
without this it re-proposes the identical edit on the next checkpoint.
- findRange anchored suggestions by exact string match, which missed
whenever the model echoed an `original` with plain ASCII (straight
quotes, --, ...) while the document held the Typography-converted
glyphs (curly quotes, em-dash, single-char ellipsis). The miss meant
no highlight AND a silent no-op on accept, which then fed the re-nag
above. foldTypography canonicalizes those variants (with a source
index map for length changes) so matching survives the mismatch.
Covered by a server-side regression test (accept+dismiss then re-check
returns nothing) and frontend unit tests for the fold and anchoring.
Claude-Session: https://claude.ai/code/session_016Yr6jELuRc7hyzYLccQKZd
Writer power-ups (Phase 11), plus the selection-bubble vs copy/paste fix.
- Find & Replace (Ctrl/Cmd+F): SearchHighlight decoration extension +
FindReplace bar (match-case, replace-all back-to-front, scroll without
popping the selection bubble).
- Read-aloud (Web Speech, offline) on the word card and selection bubble.
- Keyboard/touch access to the ESL helpers: Ctrl/Cmd+D look up word at caret,
Ctrl/Cmd+J rewrite selection, touch long-press lookup. Refactored the
right-click handler into a shared openWordLookup(pos).
- Whole-corpus backup: GET /api/docs/export-all zips every doc (md/docx),
de-dupes filenames, dated name; sidebar download links. TestExportAll.
- Smart typography input rules (curly quotes/em-dash/ellipsis), ASCII-only so
CJK is untouched.
- Duplicate doc, sidebar sort (Recent/Title/Longest), toolbar outline popover.
- English phonetic (chosen over pinyin for an English learner): ECDICT-built
phonetic.json.gz (46,579 words) + Result.Phonetic + WordCard IPA line;
scripts/build_phonetic.py (full build + --seed fallback).
- Selection bubble no longer blocks copy/paste: deferred to pointer-up and made
click-through except on its buttons.
Claude-Session: https://claude.ai/code/session_016Yr6jELuRc7hyzYLccQKZd
Two editor features that were in flight alongside the sound work:
- FontSize TipTap extension (rides on textStyle) with Small/Normal/Large/
Title presets in the toolbar; StatusBar + CSS support
- Image handling: internal/images handler, upload route + config, client
API, EditorCore wiring, and md/html/docx export support for images
Claude-Session: https://claude.ai/code/session_016Yr6jELuRc7hyzYLccQKZd
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
Inline Chinese gloss (offline) and a "say it more naturally" / tone-rewrite,
the two ESL features for the Mandarin-speaking writer.
Gloss: embedded English→Chinese dictionary (gloss.json.gz, 57k common words
built from ECDICT via scripts/build_gloss.py). lexicon gains Gloss()/Result.Gloss
and a lightweight GET /api/gloss/{word}; the right-click WordCard leads with the
中文; GlossTip shows it on a 350ms hover (reuses wordAt, so CJK is never glossed).
Offline + instant, works with the LLM down.
Rewrite: selecting text pops a SelectionBubble (✨更自然 + the tone vocabulary);
picking a style calls POST /api/docs/:id/rewrite (llm.RunRewrite, stateless,
owner-scoped) and shows a RewritePreview (original→rewrite, accept/cancel/retry).
Accept applies it in-editor.
Tests added in lexicon and suggestions. go build/vet/test, tsc, vite all clean;
live smoke vs a fake vLLM verified gloss + rewrite + 400/404/502 paths.
Claude-Session: https://claude.ai/code/session_016Yr6jELuRc7hyzYLccQKZd
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
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
Tier-1 voice-consistency pass: whole-document LLM review surfacing passages
that read tonally out of place (formal/over-polished/paraphrased-too-closely),
as honey-decorated `voice` flags with no correction (awareness-only).
- internal/llm/voice.go: RunVoice sends the whole document (no TruncateDoc),
MaxTokens 2048, 20s per-doc floor (VoiceInterval). Standalone voice prompt
in prompts.go (not bundled with the grammar checkpoint, per spec).
- internal/suggestions: POST /api/docs/:id/voice. replacePending is now
family-scoped (pendingScope) so grammar and voice never clobber each other's
pending flags; both passes return the unified pending set. check/voice share
one runPass helper. TestVoicePassCoexists covers both directions.
- Frontend: api.voiceDoc, useCheckpoint voicing/runVoice, honey "Check my
voice" toolbar pill, breathing honey dot in StatusBar.
Claude-Session: https://claude.ai/code/session_016Yr6jELuRc7hyzYLccQKZd
Conversational follow-up on a suggestion, streamed token-by-token.
Backend (interface-only; handlers never touch a concrete LLM client):
- internal/llm/chat.go: StreamAskPetal with conversational sampling
(max_tokens 512, temp 0.7, rep 1.15, top_p 0.92, stop "\n\n\n"),
reusing AskPetalSystemPrompt + TrimHistory.
- internal/suggestions/chat.go: POST /api/suggestions/:id/chat. One
user-scoped join loads the suggestion + parent content_text;
surroundingParagraph extracts the \n\n-bounded paragraph at from_pos
(whole-doc fallback when unlocated) and injects it server-side.
Streams event: token / event: done SSE frames with JSON-encoded data
so token newlines can't break framing; real http.Flusher per chunk.
LLM-unreachable -> 502 before SSE headers; unknown suggestion -> 404.
Frontend:
- streamSuggestionChat: fetch + ReadableStream SSE parser (not
EventSource, needs POST), abortable.
- AskPetal.tsx: whole conversation in component state (no persistence,
cleared on close), Petal's first bubble pre-seeded with the
explanation, rose/lavender bubbles, CJK font stack on the bubbles
only (Note #17), streaming caret.
- SuggestionCard "Ask Petal" pill pins the card open while chatting
(hover-close suppressed, click-away closes) and widens it to 340px.
Tests: chat_test.go covers streamed-text concat + done event,
server-side context injection on the system message, sampling params,
404, and surroundingParagraph. go build/vet/test clean, tsc clean,
vite build OK. Live SSE smoke-tested against a fake streaming vLLM:
tokens flushed individually through the chi middleware stack, done
terminator, 502 on LLM-down, 404 on unknown suggestion.
Claude-Session: https://claude.ai/code/session_016Yr6jELuRc7hyzYLccQKZd
Qwen 3.5 — the spec's recommended model — is a reasoning model. With
thinking on, Ollama streams its chain-of-thought into a separate
`thinking` field and hits num_predict before emitting any answer into
`content`, so Complete() got an empty string and the checkpoint failed
with "no JSON object in model output". Sending `"think": false` on every
/api/chat request fixes it; non-thinking models (qwen2.5) ignore the flag.
Validated end-to-end on deployment hardware (Ollama, qwen3.5:9b): the
grammar checkpoint now caught all five ESL errors in a 3-sentence sample
with correct JSON and string-anchoring, ~8.5s warm.
Claude-Session: https://claude.ai/code/session_016Yr6jELuRc7hyzYLccQKZd
Backend (internal/llm): backend-agnostic LLMClient interface + factory
with vLLM (OpenAI-compat) and Ollama (native) clients, each Complete +
Stream. prompts.go holds the checkpoint and Ask Petal templates;
checkpoint.go salvages JSON from model output (brace-matched), enforces a
per-doc 30s RateLimiter, and truncates the doc to a latency cap.
internal/suggestions: POST /api/docs/:id/check runs a checkpoint and
replaces the doc's pending suggestions in one tx (accepted/rejected kept
as history); GET /api/docs/:id/suggestions lists pending;
POST /api/suggestions/:id/{accept,dismiss} resolves one. Throttled checks
return the current set rather than erroring.
Frontend: useCheckpoint (4s debounce, loads existing on open, stale-guard
tokens); SuggestionHighlight renders ProseMirror decorations re-anchored
by the `original` string on every doc change (not stored marks), with
precise textblock-offset→PM-position mapping; SuggestionCard shows the
type tag + diff + explanation and applies the replacement in-editor on
accept; breathing rose checkpoint dot in the StatusBar; fade-float +
breathe animations.
Tests: llm parse/rate-limit/truncate; suggestions full flow + rate-limit
over httptest with a stub client. Smoke-tested end-to-end against a fake
vLLM endpoint (anchoring verified) and the LLM-unreachable 502 path.
Claude-Session: https://claude.ai/code/session_016Yr6jELuRc7hyzYLccQKZd
Backend internal/docs: chi sub-router (list/create/get/update/delete)
mounted at /api/docs, scoped to the local user. Create uses RETURNING;
update is a COALESCE partial-update so rename and full editor save share
one PUT. JSON 404/400 errors; handlers_test.go walks the lifecycle.
Frontend: api/client.ts, useAutoSave (1.5s debounce + saveNow flush
before doc switch), EditorCore (Tiptap StarterKit/Underline/TextAlign/
Placeholder/CharacterCount) + Toolbar, DocList/DocListItem, StatusBar,
and an App.tsx that orchestrates load/select/create/delete with
optimistic sidebar patching. content + content_text + word_count are
emitted together on every edit. .petal-prose styling (Lora body, Nunito
headings).
Verified: tsc clean, vite build, go build, full CRUD smoke test incl.
CJK title round-trip and SPA serve.
Claude-Session: https://claude.ai/code/session_016Yr6jELuRc7hyzYLccQKZd
- Go module + chi server with embedded SPA serving and /api/health
- internal/config env loader (local-dev defaults; auth/copyleaks deferred)
- React 19 + Vite 6 + Tailwind v4 frontend with full Petal design tokens
- Frontend embedded into the binary via web/embed.go (go:embed all:dist)
- README dev workflow, .env.example, BUILD_PLAN progress tracker
- Verified end-to-end: binary serves health, embedded SPA, and SPA fallback
prosolis