The chat generation pipeline previously forced think=True unconditionally
to match qwen3's combined think+tools template, locking the system into
that model family. Bench data (2026-05-21, qwen3:30b-a3b/qwen3:32b on
CPU) showed thinking adds 1-2 minutes per turn for unclear quality
benefit — qwen3:30b-a3b even produced more rambling with think on.
This decouples think from the model family by reading a per-user
`think_enabled` setting (default `false`). Non-qwen3 models can now run
through the same pipeline without the silent-generation failure mode
that content-gated thinking would have caused — they just don't think.
qwen3 users who still want thinking can opt in via the Settings UI.
Settings UI:
- New "Enable model thinking" checkbox in General → Assistant section.
- Help text explains the default-off rationale and when to opt in.
- Persists via the existing settings API; no schema migration needed
(Setting is key/value text).
Telemetry to confirm whether this regresses tool-call reliability on
qwen3 (the current model) is in a follow-up commit (generation_tool_log).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Prep prose (services/journal_prep.py):
- Emit explicit "WEATHER: none available — do NOT mention weather"
absent-marker so a small model can't invent partly-cloudy/temperature
prose when both configured locations have empty addresses.
- Replace negative-only system rule with positive-anchored guidance
forbidding weather/temp/precip mentions unless a numeric WEATHER
section is present; also bans echoing parenthetical labels verbatim.
- Reword overdue header to "(past their due date, still open — backlog,
not today's work)" and render lines as "was due <date>, N day(s)
overdue" with correct singular/plural. Supersedes the wording noted
in Fable task #159.
- Deterministic fabricated-weather reconciler: low-false-positive regex
detects fabricated weather phrasing; on trip with an empty section,
regenerate once with a corrective. Persistent fabrication logs ERROR
rather than mangling prose.
Journal route (routes/journal.py):
- Override message_count with len(messages) in _day_payload. The chat
path already does this; the journal path was hitting the
Conversation.to_dict() fallback to 0 because messages aren't
eager-loaded on that instance.
Tests:
- tests/test_journal_message_count.py — pins the model-level trap and
the override contract (3 cases).
- tests/test_journal_prep_hardening.py — 11 cases covering the
fabricated-weather reconciler and absent-marker rendering.
- tests/test_journal_prep_filtering.py — updated one stale assertion.
Tracks Fable task #171.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Both export paths emit pin_kind and pin_label per note_version row.
Restore reads them via .get() so backups predating the schema still
import cleanly (defaults to None → rolling).
BackgroundScheduler with a single CronTrigger fires scan_all_users_for
_auto_pins via asyncio.run_coroutine_threadsafe (mirrors the journal-
scheduler pattern). Wired into app startup/shutdown alongside the other
schedulers.
_promote_stable_versions_for_note is the pure-function core: walks
versions chronologically and pins any with a >= AUTO_PIN_STABILITY_DAYS
(2-day) gap to the next version (or to now, for the latest). Auto-
generated label describes the stability window.
_scan_one_note loads versions for one note, runs the promotion, commits
mutations to the attached rows, then calls prune_auto_pins to cap the
auto bucket. scan_user_for_auto_pins fans out across the user's notes;
scan_all_users_for_auto_pins is the top-level entrypoint for the cron.
Per-note and per-user errors are caught and logged.
Auto-pinned versions live in their own bucket with MAX_AUTO_PINS=25 cap.
The scan job calls this after each note's promotions complete; the
oldest auto-pinned rows are dropped past the cap. Manual pins and
rolling rows are untouched.
pin_version sets pin_kind='manual' and pin_label on the target row.
Accepts already-pinned rows (promotes auto→manual, updates label).
Labels are capped at PIN_LABEL_MAX_LEN=500 chars; longer values raise
ValueError before any DB access.
unpin_version clears both fields, downgrading the row to rolling. Does
NOT delete — if the row is past the rolling FIFO depth, the next
autosave's prune will drop it.
The DELETE inside create_version now filters pin_kind IS NULL so pinned
rows (auto or manual) aren't counted toward MAX_VERSIONS=50 and aren't
candidates for deletion. Pinned versions live indefinitely regardless
of how heavy rolling autosave traffic gets on the same note.
log_work description now mentions that logs feed the task's auto-summary,
nudging the LLM toward specific log content (commands, decisions, failures)
rather than vague entries.
create_note description gains a runbook-shape clause: code blocks, numbered
procedures, and explicit 'save this as a note/runbook' signals should
spawn standalone notes. Task-specific work-in-progress routes to log_work
instead.
create_note tool:
- New 'description' parameter accepted and forwarded to the service.
- When status is set (creating a task), 'body' is dropped before the
service call. Task bodies are owned by the consolidation pipeline.
update_note tool:
- New 'description' parameter; routed through update_fields.
- When the resolved target has is_task=True and 'body' is in the
arguments, the call errors with a message nudging toward log_work or
description. Knowledge notes are unaffected.
HTTP routes (POST/PATCH/PUT /api/notes) accept body freely — the
restriction is only at the LLM tool layer.
log_work tool now invokes maybe_consolidate(reason='log_added') after a
successful create_log. The gate inside the consolidation service handles
threshold + setting checks.
update_note service snapshots old_status before mutation and fires
maybe_consolidate(reason='task_closed') when the status transitions into
'done' or 'cancelled'. Re-saving an already-terminal status doesn't
retrigger — only transitions count.
consolidate_task reads the task title, description (read-only context),
and chronological work logs; builds a prompt via _build_consolidation_prompt;
calls generate_completion with the user's background_model setting; on a
non-empty result, writes back to Note.body, stamps consolidated_at, and
re-runs the embedding pipeline.
Errors are caught and logged. LLM failures leave body untouched so the
next trigger retries cleanly. Per-task asyncio lock prevents simultaneous
passes for the same task.
New services/consolidation.py module with maybe_consolidate() — the
debounced trigger gate. Two reasons:
- log_added: gated by DEFAULT_LOG_THRESHOLD (3) counted since the task's
consolidated_at timestamp.
- task_closed: bypasses the count gate; fires whenever status flips to
done/cancelled.
Both reasons gated by the auto_consolidate_tasks user setting (default
on). Per-task asyncio.Lock prevents two simultaneous passes for the same
task. consolidate_task is a stub here — full implementation in the next
commit.
create_note service accepts a new description kwarg and forwards it to the
Note constructor. PUT/PATCH/POST routes include description in the field
whitelist. update_note already passed **fields through setattr, so the new
column is reachable without touching that signature.
CI surfaced three issues:
- 'famous supply project' didn't substring-match 'Famous-Supply Work topics'
because the trailing filler word 'project' blocked the substring tier.
Strip {project, projects} from the query before the substring check.
- SequenceMatcher fallback against `combined` (title + description +
summary) diluted ratios to ~0.5 for plausible matches. Use title
directly; the 0.70 tier already handles description/summary mentions.
- Test patches used patch.object on a consumer module where
list_projects is imported locally — patch the source module instead.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Two related gaps in the journal weather panel:
1. Saving locations via PUT /journal/config didn't trigger a weather
fetch, so newly-entered sites had no cache row (or a stale one) until
the user manually clicked the panel's refresh button. The panel
rendered "two sites with empty values" against pre-existing cache
rows that no longer matched what the user had configured.
2. get_cached_weather_rows returned every WeatherCache row for the user
regardless of whether the location was still in journal_config.
Briefing-era rows survived migration 0040 (which only deleted the
briefing_config setting, not the cache table) and showed up as
ghost tabs in the UI.
Changes:
- get_cached_weather_rows accepts an optional valid_keys filter; rows
whose location_key is not in the set are excluded.
- routes/journal.py:
- put_config kicks off a background refresh_location_cache for any
saved location with valid lat/lon.
- GET /weather and POST /weather/refresh both pass valid_keys derived
from the current config so orphaned rows don't surface.
- services/journal_prep.py filters the weather section to currently-
configured locations as well; uses a lazy import of get_journal_config
to avoid a cycle (journal_scheduler imports journal_prep).
153 tests pass.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Root cause of the 2026-04-29 dentist-appointment incident: the model
called update_event(query="Appointment") when two events had
"Appointment" in their titles. find_events_by_query returned both,
upcoming-first ordered by start_dt — matches[0] was id=2 (a stale
pre-existing event with garbage end_dt), not id=15 (the one the user
just created via the journal flow). update_event_tool silently took
matches[0] and mutated the wrong event.
Fix: a new resolver helper `_resolve_event_for_action` funnels both
update_event_tool and delete_event_tool through one disambiguation
path. Lookup precedence:
- `event_id` → exact get_event lookup, no query at all
- `query` matching exactly one event → proceed
- `query` matching zero → return success=False, "no event found"
- `query` matching 2+ events → return success=False with a
`candidates` array of {id, title, start_dt, location} so the
model can pick one and call again with `event_id`
The candidates list is capped at 8 to keep the model's context tight.
The error message names the count and the next-step ("pass event_id
or refine the query") so the model can self-correct in one turn.
For delete_event, the disambiguation is even more important — the
silent-matches[0] path would have deleted the wrong event outright
rather than just mutating it. The tool description leans into that:
"Deleting the wrong event is a costly user error; never guess."
Tool surface change: `query` and `event_id` are now both optional;
the tool errors clearly when neither is supplied. The model already
knows id values from prior tool results (returned in `data.id`),
which is the natural feeder for the disambiguation flow.
5 new tests in test_calendar_tool_tz.py cover:
- ambiguous query → success=False with candidate list, no mutation
- event_id supplied → bypasses query lookup entirely
- non-existent event_id → clear "no event found" error
- neither identifier → "query or event_id required" error
- same disambiguation enforced for delete_event_tool
46 calendar/events tests pass; ruff clean.
Closes Fable #161.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Structural fix for the "end before start" bug class observed on prod
2026-04-29. Bad data became inexpressible at the schema level instead
of getting trapped in defensive read-path filters.
The hotfix that landed earlier today (94b169f) is reverted by the
preceding revert commit; this commit supersedes it cleanly with a
proper data-model change.
## Schema (migration 0043)
- Add `duration_minutes INTEGER NULLABLE` column on `events`.
- CHECK constraint: ``duration_minutes IS NULL OR duration_minutes >= 0``.
- Backfill from existing `end_dt`:
- end_dt valid (end > start) → duration_minutes = total minutes
- end_dt == start → duration_minutes = 0 (zero-duration point)
- end_dt NULL or end_dt < start → duration_minutes = NULL
(the corrupt prod row collapses cleanly to a point event)
- Drop the `end_dt` column. The wire format is preserved — `to_dict()`
emits `end_dt` as a derived `start_dt + duration_minutes`. Existing
API consumers (Flutter app, web frontend, CalDAV sync) keep
receiving the same response shape; they just no longer have a way
to PUT a stored `end_dt` that disagrees with `start_dt`.
## Service layer
- `Event.end_dt` becomes a `@property`. Setting it would require a
setter we deliberately don't define — writes always go through
`duration_minutes`.
- `_normalize_duration` is the single source-of-truth for input
reduction. Accepts (start, end_dt, duration_minutes), returns the
canonical `duration_minutes`, raises `ValueError` for negative
durations, end-before-start, or end/duration disagreement.
- `create_event` and `update_event` accept either `end_dt` or
`duration_minutes` for ergonomic compat; both convert via
`_normalize_duration`. Update validates the post-update state when
the patch includes either.
- `list_events` filter is simpler now: a coarse SQL prefilter
(`start_dt <= date_to`) plus Python-side refinement using the
derived `end_dt`. Avoids Postgres-specific interval arithmetic in
the WHERE clause; refinement runs over a per-user result set so
there's no scan-cost concern at personal scale.
- Recurring-event expansion uses `event.duration_minutes` directly
instead of computing `end - start`. No more negative-timedelta
hazard.
## CalDAV sync (incoming + outgoing)
- `caldav_sync.py` (pull) and `calendar_sync.py` (Radicale upsert)
both convert iCal `DTEND` → `duration_minutes` on the way in.
Outbound iCal still emits `DTEND` as `start_dt + duration_minutes`
via the model's derived property. iCal interop is unchanged.
## Behavioral upgrade for `update_event`
Pure end_dt model: moving start past the existing end_dt would either
silently corrupt or hard-reject. Duration model: the duration is
preserved by default, so moving start slides the effective end
forward — which is what users mean when they "move" an event.
Explicit clear is still possible via `end_dt=None`.
## Tests
`tests/test_events_service.py`:
- 6 new `_normalize_duration` unit tests (sugar conversion, zero
duration valid as point event, end-before-start rejected, negative
duration rejected, inconsistent end+duration rejected, none → None)
- New behavioral test: `update_event` preserves duration when only
start_dt changes (sliding semantics)
- New: clearing `end_dt=None` on update collapses to point event
- New: list_events surfaces a point event in the upcoming window
- New: list_events excludes a timed event whose effective end has
already passed
- Existing mock-event helper updated to use `duration_minutes`
instead of stored `end_dt`.
44 event-related tests pass; ruff clean.
## Out of scope (separate task)
Fable #161 — `find_events_by_query` returning multiple matches and
silently picking matches[0]. The exact root cause of how event id=2
got mutated in the first place; orthogonal to the storage model.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
A prod event surfaced today with `start_dt=2026-05-01T12:00Z` and
`end_dt=2026-03-30T12:00Z` — end was 32 days BEFORE start, almost
certainly from an earlier tool-call mishap (Fable #161). The
list_events filter trusted the bogus end_dt and excluded the event
from every read path that hit the upcoming window, even though
start_dt was correctly in range. The event stayed visible in the
calendar grid (different range) but vanished from "Upcoming",
search, briefings, and journal prep events list.
This is the hotfix half of the response. The structural follow-up is
Fable #160 — replace end_dt with a duration column so invalid state
becomes inexpressible.
## A. Filter robustness in list_events
Treat `end_dt <= start_dt` as if no end_dt exists. The filter now
splits into two branches:
- valid duration: end_dt IS NOT NULL AND end_dt > start_dt AND
end_dt >= date_from
- no/invalid duration: (end_dt IS NULL OR end_dt <= start_dt) AND
start_dt >= date_from
Same change applied to the recurring-event expansion's `duration`
calculation, which was producing negative timedeltas for corrupted
rows and computing nonsensical occurrence end times.
## B. Write-side validation in create/update
`create_event` and `update_event` now raise ValueError when the
resulting state would have end_dt <= start_dt. Update validates
against the *post-update* state, not just the field being changed —
so pushing start_dt past an existing end_dt also fails loudly. Bad
data shouldn't be persistable from any write path.
Surfaced cleanly:
- Calendar tool wrappers (create_event_tool / update_event_tool)
catch ValueError and return `{success: false, error: ...}`, which
the model can read and self-correct.
- Route handlers (POST /api/events, PATCH /api/events/<id>) catch
and return HTTP 400 with the validator's message instead of
letting it bubble to a 500.
4 new tests in test_events_service.py:
- create rejects end before start
- create rejects equal start/end (zero duration)
- update validates the post-update state (start pushed past existing end)
- list_events surfaces an event whose end_dt is before its start_dt
34 event-related tests pass; ruff clean.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Reproducer (2026-04-29 dentist appointment): user said "this Friday,
I have an appointment" with no other details. The model immediately
called create_event with title="Appointment", description="User
mentioned an appointment this Friday but hasn't provided details
yet.", all_day=true. THEN it asked the user for time/location in
its reply. When the user came back with "8am at my dentist for
permanent crown fitting", the model called update_event — but never
updated the title, leaving the placeholder "Appointment" in the
calendar permanently.
The bug isn't about the tool surface, it's that the model created
an event before it had real content. The system prompt had no rule
against this, so the model hedged: "log a placeholder, ask for
details, then update". That pattern pollutes the calendar with
garbage titles and forces immediate update_event calls.
create_event tool description now includes an explicit anti-pattern:
record a moment, ask for the missing pieces, and only call create_event
once you have actual title + time + location. Stand-in titles like
"Appointment" / "Meeting" / "Event" with "details TBD" descriptions
are explicitly named as the failure mode.
Pure prompt change. 18 tests pass; ruff clean.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Two filtering issues that made the daily prep noisy and trained the
user to ignore it.
## Tasks: bucket into due-today / upcoming / overdue
The prep was calling `list_notes(due_before=day_date)` and labeling the
result as "tasks due today". That filter is strictly less-than, so it
returned only OVERDUE tasks (a single 68-day-stale task in this user's
case), while the prompt still framed them as fresh today's work. Each
day of the prep treated the same overdue task as new — the user
learned to ignore the line entirely.
`gather_daily_sections` now runs three queries:
- `tasks_due_today` — `due_after=day_date AND due_before=day_date+1`
- `tasks_upcoming` — next 7 days, exclusive of today
- `tasks_overdue` — strictly before today
Overdue entries carry a `days_overdue` count. `_render_sections_for_prompt`
emits three labeled headers ("TASKS DUE TODAY", "UPCOMING TASKS",
"OVERDUE TASKS (still on the list, not currently due)"). The system
prompt has a new TASK BUCKETS rule telling the model: don't call
overdue items "due today"; surface them with their staleness duration
("still on the list 68 days") and frame as a backlog reminder rather
than today's work.
Backwards-compat: `sections["tasks"]` still exists, now as the union
of all three buckets — strictly more useful than the prior overdue-
only behavior any frontend consumer was getting before.
## Events: tz-aware window + proximity filter
The user's "Birthday — 2026-09-29 (FREQ=YEARLY)" event was surfacing
in every daily prep, 5 months out. Root cause: `gather_daily_sections`
built `day_start`/`day_end` as NAIVE datetimes; `list_events` then
called `rrulestr(...).between(naive_from, naive_to)` against an
aware `dtstart`, which throws TypeError, hits the `except Exception`
fallback, and appends the canonical event row — regardless of whether
today is anywhere near a recurrence.
Fix:
1. Construct the day window as TZ-aware in the user's local timezone
and convert to UTC before the query. RRULE expansion now runs
correctly.
2. Defense-in-depth `_filter_proximate_events` drops events whose
start_dt is more than 7 days from `day_date` (in the user's local
TZ — not UTC, so a Friday 23:00 NY event isn't misclassified as
Saturday). If list_events ever leaks a far-future row again, the
prep doesn't surface it.
10 new tests in `tests/test_journal_prep_filtering.py` cover task
bucketing (overdue marker, due-today no-marker, no-due-date), the
proximity filter (the 4/29 reproducer, in-window keeps, local-vs-UTC
boundary, unparseable dates kept rather than suppressed), and the
rendering (overdue staleness shown, due-today doesn't repeat the date,
correct section ordering).
53 tests pass across journal_prep + journal_search + record_moment +
calendar_tool + events. Ruff clean.
Closes Fable task #159.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Belt-and-suspenders to the prompt-layer changes in 6c309f1. Even when
the model emits bogus task or place links, the server now refuses to
persist them.
## Task auto-linking guard
Reproducer (2026-04-27): a moment about restaging Docker on the swarm
ended up with `task_ids: [2]` (Weston's ADHD Evaluation) — the only
task in that day's prep. The model picked it up as filler.
`_filter_task_ids_by_keyword_overlap` now runs after id resolution: it
fetches each linked task's title, tokenizes both content and title
through `_content_keywords` (lowercased, stopwords stripped, <3-char
tokens dropped), and drops any link whose title shares no meaningful
keyword with the moment content. The drop is logged at INFO so we can
observe how often it fires post-deploy.
The guard runs against the merged id list, so it covers both the
preferred `task_titles` resolution path and the discouraged explicit
`task_ids` path.
## Place placeholder guard
Reproducer (2026-04-27): `place_names=["work"]` got passed to
`record_moment`. "work" / "home" / "office" aren't places — they're
role-labels for already-known geocoded locations.
`_filter_placeholder_places` drops a small set of generic single-word
labels before name resolution. Real user-named places that happen to
be one word (e.g. "Akron") pass through.
## Tests
9 new unit tests in `tests/test_record_moment_guards.py` cover:
- keyword tokenization & stopword stripping
- placeholder place filtering (generic, case-insensitive, real-place
pass-through)
- keyword-overlap filtering (the exact 4/27 reproducer, the genuine-
reference case, mixed/partial relevance, empty input)
13 tests pass; ruff clean.
Closes Fable task #158.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Three related rough edges in the journal voice surfaced from real
journal usage 2026-04-27 → 2026-04-29:
1. **Persona overhelps.** When the user logged "today I'm prepping for
an ISP migration at Branch 14 Bedford for work at famous supply",
the assistant came back with "ISP migrations can be tricky. Are you
handling the network configuration yourself, or is there a team
supporting you? Also, are there any specific tasks or checks you
need to complete before the switch?" — pushing IT-helpdesk advice
the user didn't ask for. The user had to push back. JOURNAL_PERSONA
now leads with "CAPTURE first, advise only if asked" and the
RESPONSE STYLE block has an explicit anti-pattern banning
troubleshooting / checklist / process-advice follow-ups unless the
user explicitly invites them.
2. **Moments stored in third-person observer voice.** The dentist
appointment beat got written as "The user mentioned having an
appointment this Friday but hasn't provided details yet." — reads
like an LLM transcript annotation, not a journal jot. The
record_moment tool's `content` description previously said "in the
user's voice or third-person", which was the literal source of the
bug. New phrasing requires first-person/imperative with concrete
GOOD/BAD examples, and the JOURNAL_CALIBRATION block reinforces it.
3. **Inconsistent emoji use.** 4/27 was clinical, 4/29 had 😊 and 🛠️
in the appointment confirmation. RESPONSE STYLE now bans emojis
outright — the journal is a thinking-companion surface and the
emoji warmth reads as out-of-register chat-bot tone.
Bonus while in here:
- New MOMENT ENTITY LINKING section explicitly forbids attaching a
task_titles link unless the user references the task by name (the
4/27 Docker→ADHD auto-link bug; rest of that fix is in #158).
- Same section rejects generic place placeholders ("work" / "home" /
"office") in favor of letting the user name the real place.
22 tests pass (4 journal + 18 calendar tool); ruff clean.
Closes Fable task #157.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
A user asked Fable to schedule "this Friday at 8am" on Wednesday 4/29
2026. The model picked 4/30 (Thursday) and confidently labeled it
"Friday." The TZ pipeline did everything correctly given the model's
date — the bug was upstream: the model was guessing weekdays from ISO
dates without an anchor, and the calendar tools had no way to verify.
Three layered fixes:
1. **System prompts now name the weekday alongside the ISO date.**
Both the journal-conversation prompt and the general chat prompt
used to say "Today is 2026-04-29 (America/New_York)." They now say
"Today is Wednesday, 2026-04-29 (...)." LLMs are unreliable at
deriving weekday names from ISO dates; supplying the name removes
the guess.
2. **`expected_weekday` parameter on create_event / update_event.**
When the model passes `expected_weekday="friday"`, the backend
computes the resolved start_date's weekday in the user's local
timezone and rejects mismatches with a self-correcting error
("Date 2026-04-30 falls on Thursday, not Friday. Recompute..."),
without creating the event. The check is local-aware: a Friday
23:00 event in Tokyo crosses midnight UTC but the local view
stays Friday, and the validator respects that.
3. **Tool descriptions instruct echo-and-confirm.** create_event and
update_event descriptions now tell the model: when the user names
a weekday, state the resolved date in the reply BEFORE calling
the tool, and pass `expected_weekday`. Costs nothing in code,
reinforces the validator.
6 new tests — match success, mismatch rejection (with create/update
not invoked), omitted-param backcompat, invalid weekday name, local-
not-UTC weekday computation, and the update_event variant. All 18
calendar-tool tests + 33 event-related tests pass; ruff clean.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
A user reported "next Friday at 8am" landing on the wrong day. The
current `start` parameter accepts a combined ISO datetime string — when
the model emits something like `"2026-05-01T00:00:00Z"`, the parser
correctly honors the UTC tag and stores `2026-05-01 00:00 UTC`, which
displays as `2026-04-30 19:00` for a UTC-5 user. The bug isn't in our
parser; it's that we let the model TZ-tag the calendar day at all.
The fix moves the foot-gun: `create_event` and `update_event` now
prefer split fields (`start_date` + `start_time`, plus end variants).
A `YYYY-MM-DD` string carries no TZ metadata for a model to mis-tag,
and the backend builds the local datetime explicitly via
`datetime.combine(date, time, tzinfo=user_tz).astimezone(UTC)`. Strict
regex validation rejects anything with a TZ suffix on either field.
The legacy combined `start` / `end` fields are kept as a fallback so
saved tool-call payloads in conversation history still replay; new
calls are steered toward the split shape via the tool description.
7 new regression tests cover Eastern, Pacific, Tokyo (positive offset),
all-day inference, strict-shape rejection on both fields, backcompat
with the legacy `start` field, and the same fix for `update_event`.
27 of the event-related tests pass; ruff clean.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
services/projects.py:get_project_summary built task_counts dynamically
from a GROUP BY query, so a project with no done tasks would omit the
'done' key entirely. Frontend's TypeScript interface declares all three
lifecycle keys as required, and ProjectView.vue summed them to render
the Tasks tab counter — undefined + N = NaN.
Two fixes:
1. Backend: initialise task_counts with {todo: 0, in_progress: 0,
done: 0} so the service returns the contract its consumers expect.
Catches the same problem for HomeView's project widget and any
other consumer.
2. Frontend: defensive ?? 0 on the tab-counter sum, so the existing
deploy renders correctly even before the backend rolls.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The /api/journal/weather route was filtering out cache rows older than 24
hours via parse_weather_card_data, while journal_prep.py read the same
rows raw without freshness checking. Result: the daily prep referenced
"home" and "work" temperatures while the right-rail UI showed nothing —
two surfaces, same backing data, inconsistent visibility.
Two changes:
1. parse_weather_card_data no longer returns None for stale data.
WeatherCard already exposes fetched_at and gracefully hides
today_high / forecast fields when they're absent, so old data renders
with whatever fields the cached forecast still covers.
2. The /weather route opportunistically schedules a background refresh
for any cache row older than 4 hours. If the user's journal_config
has lat/lon for that location_key, the refresh runs and the next
page load gets fresh data; if no usable config, the refresh is a
silent no-op and the stale cache is still served.
This makes prep and UI consistent. It also self-heals over time — once
locations are configured, stale caches get refreshed on the next page
load instead of waiting indefinitely.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Inspection showed only ONE record_moment call across the entire day's
journal — and that one had hallucinated person_ids. Multiple clear beats
went uncaptured: AP installation, going to watch a show with daughter,
decompressing-with-game.
The prior calibration said "use record_moment freely for meaningful
beats" — too soft. The model treated it as optional, especially when
already in chatbot-reply mode.
Rewritten: record_moment is now framed as the model's PRIMARY JOB. The
calibration includes an explicit checklist of what counts as a beat
(event, encounter, decision, observation, plan, feeling, accomplishment)
and an explicit instruction to call record_moment FIRST, before composing
the reply. Multiple beats → multiple calls. The ONLY skip case spelled
out: purely meta-conversational messages (acknowledgements, meta-asks
about prior tool results).
Tests on a fresh conversation will tell us if this moves the needle —
today's journal is poisoned by ten prior chatbot-flavored turns that
the model is pattern-matching against in its own history.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Per user clarification: previous over-rotation dropped the LLM-generated
prep prose entirely (just a phase greeting) and made the chat persona
extremely sparse ("you are a place where words go down"). User actually
wanted only the chat replies pulled back, NOT the prep dropped, and the
chat to behave largely like normal /chat — asking follow-ups and
verifying earlier details.
services/journal_prep.py — restored:
- _render_sections_for_prompt
- _PREP_SYSTEM_PROMPT (the direct, briefing-style prompt from 590a07b)
- _generate_prep_prose
- _fallback_prep_text
- ensure_daily_prep_message now calls _generate_prep_prose again
- removed _phase_for_now / _phase_prompt helpers (no longer needed)
services/journal_pipeline.py — persona rewritten:
- Old: "You are the user's journal. Be quiet. Listen. You are not helpful."
- New: "You are the user's assistant. Behave like the rest of the app's
chat: respond conversationally, ask follow-up questions, verify details
from earlier turns, use tools naturally."
- Calibration block reorganized: PEOPLE/PLACES (ask first), MOMENTS
(silent + use *_names), STATE-CHANGING TOOLS (confirmation flow),
OTHER, RESPONSE STYLE.
- RESPONSE STYLE keeps the no-apologizing / no-option-menus /
no-verbatim-repetition / match-user-length rules but drops the "be
quiet, one short sentence" framing.
Net behavior:
- Open journal → LLM-generated prep prose with today's tasks/events/weather
- Reply → assistant responds conversationally like /chat, asks follow-ups,
verifies details, uses tools
- Background: silently records moments via *_names, asks before creating
new people/places
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The prep was generating a multi-sentence recap of tasks/events/weather/
projects/recent moments via an LLM call. Per user direction, that's
redundant — the right-side widgets already show today's data — and the
verbosity made the journal feel chatty when the user wanted quiet.
Replaces the prep prose generator with a single phase-aware check-in
question (drawn from a static map: morning="How are you starting the
day?", midday="How's it going so far?", evening="How did the day shake
out?"). No LLM call. The structured `sections` are still gathered and
persisted on msg_metadata for provenance and possible future tooling
(e.g., search), they just don't render in the prep message.
Also pulls the journal persona way back. The prior framing pushed the
model toward stock therapy-template patterns ("I'm sorry you're feeling…"
+ numbered option lists). The new persona is "you are the user's journal —
listen, be quiet, stay out of the way." RESPONSE STYLE rules now lead the
calibration block and explicitly forbid:
- apologizing for the user's feelings
- offering to help / pitching tools
- multi-option menus
- verbatim repetition of prior replies
- padding short replies into paragraphs
Most replies should be one short sentence. Sometimes the right reply is
"Got it" + a record_moment tool call.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Three real bugs surfaced from inspecting today's journal turns:
1. record_moment was getting fed hallucinated person_ids (the LLM passed
[1, 2] instead of the IDs save_person had just returned). Result: the
moment was linked to two random old test-data notes ("test task 2",
"Tell a joke"), not the people the user actually mentioned.
2. The calibration rule "ask before save_person" was being silently
ignored — model just called save_person on first mention of Victoria
and Mother without asking the user.
3. The model produced a verbatim-identical reply to its previous turn when
the user mentioned "overwhelmed" twice — same numbered-list of 4
options, same closing line. The "warm listener / ask gentle questions"
persona was pushing toward stock therapy-template patterns.
Fixes:
services/tools/journal.py — record_moment now accepts *_names parameters
(person_names, place_names, task_titles, note_titles). Server resolves
each name to a note ID via case-insensitive title match, scoped by
note_type or task-status. *_ids parameters still exist but are now
documented as DISCOURAGED. The LLM physically cannot invent the wrong ID
when using names — names with no match are silently dropped. Resolution
happens via _resolve_entity_ids_by_name helper.
services/journal_pipeline.py — JOURNAL_PERSONA tightened (no more
"warm/curious listener" framing that pushed toward stock comfort
patterns). JOURNAL_CALIBRATION rewritten as scannable sections with
imperative language: PEOPLE/PLACES require asking before save_person;
TASK/NOTE state changes use the confirmation flow; MOMENTS are silent
but MUST use *_names not *_ids; OTHER notes the no-set_rag_scope and
no-auto-notes invariants. Added a RESPONSE STYLE section that explicitly
forbids verbatim repetition and stock multi-option menus.
After deploy, force-regenerate today's prep via fable_trigger_journal_prep
to also pick up the tighter prep prompt from 590a07b.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Previous system prompt asked for "warm, conversational, like a friend
writing a letter" which produced flowery preludes that buried the actual
data. Rewritten to:
- Lead with practical data (tasks, events, weather) — concrete and specific
- 4-7 sentences total, tight prose, no padding
- Recent moments / open threads mentioned briefly at the END as context,
not as the lead
- Voice: "competent assistant briefing the user" not "friend writing a letter"
- Close with a short journal invitation under 8 words
Also dropped max_tokens 600 -> 400 to bias toward concision.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
bvandeusen