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>
The structured prep card was data-rich but voiceless. Replaced with an
LLM-generated conversational opener — same shape the briefing's compilation
slot had — that renders as a normal assistant chat bubble at the top of
the day's conversation.
Backend (services/journal_prep.py):
- Renamed generate_daily_prep -> gather_daily_sections (still pure data
fetching, no LLM); kept the old name as a backwards-compat alias.
- New _generate_prep_prose: hands the gathered sections to generate_completion
with a warm-conversational system prompt; returns prose. Falls back to a
plain greeting if the LLM call fails or no model is configured.
- ensure_daily_prep_message now persists the prep as role='assistant' with
the prose as content. Structured sections stay on msg_metadata for
provenance. Auto-upgrades legacy system-role preps in place on next call.
Frontend:
- Drop the <article class="daily-prep"> structured block from JournalView.
The prep is now just the first chat bubble — picks up the existing
Illuminated Transcript styling automatically.
- Drop dayMessages / prepMessage / prepSections / asArray helpers — no
longer needed.
- ChatMessage hideMessage filter: comment refined to clarify it only
catches LEGACY system-role prep rows. Current preps are assistant-role
and render normally.
Net effect: open /journal -> first thing you see is a warm assistant bubble
that talks about your day -> input bar below to reply.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Removes the entire RSS feature surface — feeds, items, embeddings, reactions,
discussion-note flow, briefing news context, settings, env-vars, and DB
tables. Keeps the URL-generic article-reader (the read_article LLM tool)
under a clean module so the LLM can still fetch arbitrary article content
from URLs the user provides.
Backend:
- New services/article_fetcher.py — single source of trafilatura URL→text
- New services/tools/article.py — read_article tool (was nested under tools/rss)
- Delete services/rss.py, rss_classifier.py, rss_filtering.py, article_context.py
- Delete services/tools/rss.py
- Delete models/rss_feed.py (RssFeed, RssItem), models/rss_item_embedding.py
- services/embeddings.py: drop upsert/semantic_search/backfill RSS helpers
- services/llm.py: remove _build_briefing_article_context, briefing-conv branch,
ARTICLE_DISCUSS_SEED skip-RAG branch; drop get_rss_items / add_rss_feed from
the actions list
- services/generation_task.py: drop _maybe_save_article_discussion_note + caller
- routes/chat.py: drop /api/chat/from-article/<id> endpoint
- routes/journal.py: re-import via web.py refactor (article_fetcher path)
- services/tools/__init__.py: register `article`, drop `rss`
- services/tools/_registry.py: drop the requires=='rss' check
- app.py: drop backfill_rss_item_embeddings + backfill_rss_article_content tasks
- config.py: prose-only edit (no env var change — RSS env vars were never first-class)
Frontend:
- stores/settings.ts: drop rssEnabled
- SettingsView.vue: drop the RSS-classification mention
- api/client.ts: drop openArticleInChat (the from-article endpoint is gone)
Tests:
- Delete tests/test_rss_service.py, test_news_api.py, test_article_reading.py
Migration:
- 0042_drop_rss: DROP TABLE rss_item_embeddings, rss_item_reactions, rss_items,
rss_feeds; DELETE settings rows for rss_enabled / briefing_*_topics
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The journal UI was over-stripped earlier — weather panel, current-conditions
poll, and the upcoming-events sidebar were all dropped. Restored those (calls
the new /api/journal/weather, /api/journal/weather/current,
/api/journal/weather/refresh, /api/journal/weather/geocode endpoints).
Also: the daily-prep system message was rendering as flat text inside
ChatPanel because there's no .role-system bubble styling. Added a hideMessage
guard in ChatMessage so daily-prep system messages don't render in the chat
stream — they're already shown above as a structured prep card.
News / RSS reactions / article-discuss are intentionally NOT in the journal
(scoped out per user direction). The broader RSS infrastructure cleanup is
a separate, larger task.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- services/tools/journal.py — record_moment + search_journal tool handlers
- services/tools/_registry.py: add `journal` flag on ToolDef + tool() decorator
- get_tools_for_user(user_id, conversation_type='chat'|'journal') —
exclude journal-only tools from chat sessions; exclude set_rag_scope
from journal sessions
- services/tools/__init__.py: register the new journal module; drop the
unused get_briefing_tools export
- services/llm.py build_context: short-circuit for journal conversations,
using journal_pipeline.build_journal_system_prompt and skipping all
notes-RAG injection (preserves the journal/notes isolation invariant)
- services/generation_task.py: pass conversation_type into get_tools_for_user
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Also rename services/tz.user_briefing_date → user_day_date with a backwards
compat alias (briefing modules using the old name will be deleted in the
upcoming briefing tear-down stage). Update services/chat.py to_dict to use
day_date.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Updates user-visible branding across frontend (PWA manifest, page title,
Settings, push fallback), backend (email templates and subjects, LLM
system prompt, CalDAV displayname, SMTP from-name default), README,
quickstart compose, and MCP server description.
Also updates the CI image path and quickstart image reference to
git.fabledsword.com/bvandeusen/fabledscribe in preparation for the
Forgejo repo rename. Internal Python package, env vars, and database
schema unchanged.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
trafilatura.extract dispatched via run_in_executor isn't safe to run
concurrently — two parallel calls can crash the process with a
libxml2-level double free. The top-level Wikipedia+SearXNG gather is
fine; only the inner per-article extraction needs to stay sequential,
matching the pre-parallelization behavior.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Runs the Wikipedia summary and SearXNG search concurrently and returns
both when available, so current-event questions aren't masked by a
generic role article from Wikipedia. When the Wikipedia summary includes
a thumbnail, it is cached through the existing image pipeline and
surfaced as an embeddable markdown snippet alongside the extract.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Runs wiki_search in parallel with SearXNG queries; Wikipedia results
(which already carry content via their extract field) are merged into
the source pool before outline generation, skipping a separate fetch
step. Also fixes a pre-existing F811 ruff violation in the test file.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Add a refresh button (↻) to the weather section header in BriefingView so
users can manually re-fetch weather data (needed after deploying the hourly
precip changes to populate the cache). Update the existing POST
/api/briefing/weather/refresh endpoint to return full card data instead of
just location keys.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Fetch hourly precipitation probabilities from Open-Meteo alongside daily
forecasts. Generate human-readable precip summaries ("Rain likely 2–5 PM",
"Rain likely all day") for today and each forecast day. Display today's
summary as a styled callout and show peak precipitation hour in forecast rows.
Also fix briefing pipeline to parse all weather location rows (not just first).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
RSS is now off by default. When disabled:
- Scheduler skips RSS feed sync during compilation slot
- Briefing pipeline skips RSS item gathering
- RSS LLM tools (get_rss_items, add_rss_feed) are hidden
- API routes return empty results for feeds/news
- Frontend hides News nav link, RSS Feeds and News Preferences in settings
- Briefing view hides news sidebar section
Toggle in Settings > Briefing > RSS / News.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add framing preamble to auto-injected notes so the model treats them as
reference material rather than user input. Remove RSS semantic search
injection from all chat conversations — the discuss tool handles that need.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add fonts.googleapis.com to style-src and fonts.gstatic.com to
font-src in Content-Security-Policy header.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add 'wasm-unsafe-eval' to script-src and blob: to worker-src in
Content-Security-Policy header. Required by onnxruntime-web to compile
the Silero VAD ONNX model. Also surface VAD init errors as a toast
instead of silent console log.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The system prompt listed phantom tools (create_task, delete_task, get_note)
that don't exist, causing the model to spiral when users asked to create
tasks under a project. Replaced the stale hardcoded string with a
dynamically-built actions list matching all registered tools, and added
conditional searxng/caldav extensions.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Content-based gating (_should_think) was introduced in 87fcaa6 to cut
TTFT on simple prompts, but it has no way to tell that short prompts
like "create a task titled X" are going to trigger a tool call — and
qwen3:14b's tool-call template is unreliable at think=False, producing
intermittent silent generations where output tokens burn but nothing
parses into content or tool_calls.
Reverting to always-on thinking restores the pre-87fcaa6 reliability
of tool emission at the cost of TTFT latency on short conversational
prompts. This also lets us delete the silent-round retry loop (which
can no longer fire) along with its bookkeeping.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Retry attempts were previously conflated with the initial call,
making prompt_tokens and headroom look cumulative and useless for
diagnosing the silent-round behavior. Move start-of-attempt captures
inside the retry loop and emit attempt_start / attempt_end lines so
each attempt's numbers stand alone.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Log num_ctx, message count, prompt/output tokens, headroom, and a
silent flag per round so we can correlate silent generations against
context pressure on the dev instance.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Qwen3's tool-call tokens sometimes fail to parse into either content
or tool_calls, burning output tokens and producing empty bubbles.
Detect the signature within a round (empty content, no tool calls,
eval_count > 0) and re-run the same round once with reasoning mode
enabled, which emits more reliable output. The post-loop fallback
remains as the final catch.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Qwen3:14b sometimes burns output tokens on tool-calling attempts whose
emission doesn't parse into any field we read — eval_count > 0 but no
thinking/content/tool_calls ever stream to the caller. Generation
completes "successfully," the user sees an empty assistant bubble, and
no error is logged. Seen in conv 220 today.
Two safety rails:
- stream_chat_with_tools now tracks whether it yielded anything; when
Ollama's done frame reports eval_count > 0 with zero yields, log a
warning including the last ~5 raw frames so the next occurrence leaves
breadcrumbs for diagnosis.
- run_generation checks the same post-condition after the tool loop
exits and, if content is empty with no tool calls but output_tokens
> 0, substitutes a visible fallback message and streams it as a chunk
so the user gets something readable instead of a blank bubble.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Discuss flow was hallucinating unrelated content when article
extraction returned empty or RAG pulled in orphan notes that looked
more relevant than the generic seed prompt.
- seed_article_discussion raises EmptyArticleError on empty body;
briefing and /news routes return 422 instead of staging an empty
synthetic tool result.
- build_context skips RAG auto-injection when user_message matches
ARTICLE_DISCUSS_SEED so the article IS the context on turn one;
follow-up turns keep RAG on.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Both the /news discuss button and the briefing discuss button now call a
shared seed_article_discussion() helper that stages the synthetic
read_article tool exchange and the conversational seed prompt — behavior
stays byte-identical across entry points. /news also auto-starts
generation so the chat screen lands on an in-flight stream.
First assistant reply in a seeded article conversation is persisted as a
Note (tags: article-summary + article topics) and backlinked via
rss_items.discussion_note_id, so the knowledge base stops being amnesiac
about articles the user has engaged with.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The Discuss button on news cards was producing one-shot replies because
the model got the whole trafilatura blob dropped into history with a
canned "summarize and discuss this article" prompt — no length guard, no
prep, no invitation to converse. Large articles got silently truncated by
Ollama; small articles got a tepid reply.
This reworks discuss_article around a three-layer cache:
context_prepared → content_full → fresh trafilatura fetch
First click on a small article fetches once, writes through to both
caches, and passes the body straight into the synthetic read_article
tool-result. First click on a large article additionally runs a parallel
map step (services/article_context.py) that chunks the body on paragraph
boundaries, summarizes each ~8k chunk to ~300 words of dense factual
prose via the background model, and concatenates the summaries under
section headers — all pinned to num_ctx=16384 so the map step doesn't
itself fall victim to silent truncation. Repeat clicks on either path
skip straight to the chat turn.
The canned summary prompt is replaced with a conversational seed that
invites the user into an actual discussion rather than a one-shot
synopsis, matching the goal of "have a conversation about an article,
not just read it."
discuss_topic is intentionally left untouched — it's the multi-article
aggregation path and needs a separate rework. Follow-up task will decide
whether to retire it or rework it on the cached-context approach.
Closes task #106.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Non-streaming generate_completion was the only LLM entry point that
didn't default num_ctx — stream_chat and stream_chat_with_tools both
fall back to Config.OLLAMA_NUM_CTX (16384). When a caller omitted the
argument, Ollama silently used the model's default window (~4k on
qwen3) and truncated the prompt.
That footgun was masked by fallback paths in the research pipeline:
_generate_outline's prompt carries ~12 sources × 2000 chars (~6k
tokens) of source material plus a system prompt, so the prompt got
chopped, the model never saw the sources, JSON parsing failed twice,
and run_research_pipeline dropped into the single-note "monolith"
fallback (research.py:251). The user reported chat 215 producing such
a monolith note for a multi-source research topic.
Two-layer fix:
- Default num_ctx to Config.OLLAMA_NUM_CTX inside generate_completion,
matching the streaming entry points. Any current or future caller
that forgets the argument stops silently losing input.
- Pin num_ctx=16384 explicitly in _generate_outline and
_generate_executive_summary with comments pointing at the failure
mode, so a refactor of the generate_completion default can't
silently regress the research pipeline.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
_generate_title was receiving the full messages list from build_context,
which prepends RAG snippets, RSS excerpts, URL content, and briefing
article dumps INTO the user-role message string. The role=="user" filter
inside _generate_title then handed that composite blob (capped at 300
chars) to gemma3:4b as "the user's message", so the background model
was titling conversations based on article excerpts instead of what the
user actually typed — producing wildly wrong titles like "Briefing
Profile Preferences & Schedule" for a plain calendar query. See #109.
Pass the raw history + user_content + assistant reply instead.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Two related bugs where the server defaulted naive datetimes to UTC instead
of the configured user timezone, causing all-day events to land on the
previous day and briefings to "disappear" at UTC midnight.
- New services/tz.py helpers: get_user_tz, user_today, user_briefing_date
(the briefing day flips at 4am local to align with the compilation slot,
so the 00:00-04:00 local window still shows yesterday's briefing until
the new one is generated).
- calendar create/list/update tools now parse naive datetimes in the
user's TZ before converting to UTC for storage, and tool descriptions
tell the model to pass plain local dates.
- briefing_conversations.get_or_create_today_conversation and the
reset-today route use user_briefing_date so the in-progress briefing
doesn't get replaced at 19:00 NY / UTC midnight.
- _run_profile_closeout targets user-local "yesterday" for consistency.
Regression tests added for the TZ helpers and the calendar tool.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Adds 38 parametrized tests for the _should_think classifier covering the
explicit-override path, empty/whitespace content, short/medium/long length
boundaries, case-insensitive keyword matching, and a chatty-message negative
set. These pin the content-based semantics so future tweaks to the keyword
list or length thresholds surface regressions immediately instead of going
unnoticed behind subtle latency changes.
Also drops the `think=True` overrides from the briefing /discuss-article
and /discuss-topic entry points. With `"discuss"` added to _THINK_KEYWORDS,
those canned prompts trip the classifier naturally, so the overrides were
redundant — keeping a uniform "classifier is authoritative" rule makes the
code easier to reason about.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The frontend hardcoded think=true on every chat send (ChatPanel full +
widget variants, KnowledgeView minichat), which defeated the _should_think
gate on the backend and made qwen3:14b spend 5-20s on chain-of-thought
reasoning for every turn — even "hi". This was the root cause of the
warm-path TTFT variance tracked in followup_ttft_variance.md: the logged
ttft_ms was really prefill + full thinking phase, bouncing with the depth
of the model's reasoning, not with cache or eviction.
All three frontend callers now pass think=false and let _should_think be
authoritative. The classifier is now a real content-based gate: explicit
think_requested=True still forces on as an override (briefing discuss
actions, future UI toggles, MCP callers), otherwise messages <80 chars
without reasoning keywords skip thinking, messages >=400 chars or
containing keywords like why/explain/analyze/debug/review/etc. get it.
Generation timing now separately records think_requested, the final
think decision, first_token_ms (first any chunk), and thinking_ms
(duration of the thinking phase). ttft_ms keeps its existing semantic
(first content token) so existing log analysis still works. The timing
log line surfaces all four fields so the old "just a big ttft number"
ambiguity is gone.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Two small hardening fixes from the mistral-nemo testing round:
1. stream_chat / stream_chat_with_tools now read the Ollama response
body and log it before raising on non-2xx. Previously all we saw
was 'HTTP 400 Bad Request' — the gemma3-no-tools failure would
have been diagnosed in one step if we'd been logging the body,
which says e.g. 'model does not support tools'.
2. backfill_project_summaries() now also targets summaries stamped
before 2026-04-12 (the gemma3:4b cutover). The remaining projects
still carrying the broken qwen2.5:3b output (token repetition,
hallucinated topics) will regenerate on next startup on the
better model.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Ollama's /api/tags returns whatever casing was used at pull time
(e.g. 'gemma3:12B' if the user ran 'ollama pull gemma3:12B'), but
/api/chat rejects mixed-case tags with a 400. The two code paths
are inconsistent, which surfaces the capitalized tag in the model
dropdown and then silently kills every chat request against it.
Lowercase on read (get_installed_models), on settings write
(update_settings_route), and on ensure_model() input so a legacy
mixed-case user setting can't trigger a spurious re-pull at
startup. The dropdown and stored settings are now always in the
form Ollama will actually accept.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
qwen2.5:3b produced broken auto-summaries (misspellings, token repetition,
hallucinated topics) — its synthesis ceiling is too low for free-form
summarization. Gemma 3 4B is stronger on summarization at similar size
and still fits comfortably alongside the main chat model in VRAM, so it
preserves the KV-cache-separation strategy that keeps chat TTFT fast.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Project auto-summaries were using the 3B background model, but the
task — synthesizing a coherent paragraph over ~10 notes — is well past
what 3B can do reliably. Evidence on dev: "doging conversation
hygiene", "MCPview). MCP).", trailing stray quotes, and hallucinated
topics ("AI regulation").
Route through the user's default chat model instead. Project summary
regeneration is rare (only when a project changes) so the KV cache
eviction cost on the main model is negligible.
Title generation, tag suggestions, and RSS classification continue to
use the background model — those tasks are within what 3B handles.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Three related fixes uncovered while benchmarking qwen3:14b against 8b:
- pick_num_ctx was only counting message content, missing the ~15K
tokens of tool schemas. num_ctx=8192 was being selected while actual
prompt_tokens hit 14K+, causing silent prompt truncation on every
tool-using request. Now includes json.dumps(tools) in the estimate.
KV cache priming in app.py and routes/settings.py also fetches tools
so the primed num_ctx matches what real chat requests will use.
- _should_think's heuristic classifier was overriding explicit
think=true requests from the frontend toggle and MCP, gating on
message length and regex patterns. Now a pass-through — the caller
is the source of truth. quick_capture hardcodes think=False since
it's a fast classification path that was relying on the old gating.
- delete_note description only mentioned "note or task", so the model
refused to call it for entries created by save_person / save_place /
create_list. Description now explicitly lists all five note_types it
handles.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Merge create_task into create_note (set status='todo' for tasks, omit
for notes), merge delete_task into delete_note, consolidate entity
tools (create/update_person → save_person, create/update_place →
save_place), rename get_note → read_note with clearer descriptions,
move calculate out of rag.py into utility.py, and extract shared
duplicate detection into check_duplicate() helper.
Updates all downstream references in generation_task.py, quick_capture.py,
ToolCallCard.vue, and WorkspaceView.vue.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Some exceptions (e.g. connection errors) produce empty str(e),
resulting in "Research failed: " with no explanation. Fall back to
the exception class name when the message is blank.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Research pipeline now produces an index note with:
- Executive summary (2-3 paragraphs synthesized from sections)
- Clickable links to each section note (/notes/{id})
- Section notes have parent_id pointing to the index
Also improves outline resilience: lowered minimum sections from 3
to 2, retries once on failure before falling back to monolithic note.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Split 2566-line tools.py into a tools/ package with @tool decorator
registration. Each tool's schema, metadata, and implementation live
together. Briefing eligibility is now a briefing=True flag instead of
a separate frozenset allowlist. Conditional inclusion (CalDAV, SearXNG)
uses requires= metadata. Public API (get_tools_for_user, execute_tool)
unchanged.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Three briefing quality fixes surfaced by reading today's 2026-04-11
compilation output:
- **Stale weather**: get_weather was returning 48h-old cache data
after a missed scheduler run. Tool now auto-refreshes any cached
location older than 6h (fetching fresh data from Open-Meteo), and
stamps each location with cache_age_hours + is_stale so the model
can hedge instead of faithfully relaying old numbers.
- **Cancelled tasks leaking into prose**: briefing loop now defaults
list_tasks calls to status=["todo","in_progress"] when the model
doesn't specify, so cancelled/done items stop showing up in the
summary. Localized to the briefing path — chat still sees full
history.
- **Overdue in-progress tasks missed by midday check-in**: tightened
the check-in prompt to explicitly require two list_tasks calls —
one for in_progress (catches items dragging past their due date)
and one filtered by due date — so long-running tasks stop getting
silently dropped.
The compilation prompt mentioned "news themes" but didn't name the
tool, and the model was never calling get_rss_items. Result: today's
briefing had zero news coverage despite the tool being wired up and
in the allowlist.
- Explicitly list the tools to call in the compilation prompt so
get_rss_items gets invoked alongside list_tasks/list_events/get_weather.
- When the model calls get_rss_items during a compilation run,
intercept and return the already-scored/filtered items (topic prefs
+ reaction-weighted) instead of the raw feed dump execute_tool
would return. Aligns the model's view of news with the sidebar's
rss_item_ids metadata.
Deletes ~760 lines of legacy briefing code: format_task, compute_task_hash,
upsert_task_snapshots, _gather_internal, _gather_weekly_review,
_llm_synthesise, and the unified prompt helpers. run_compilation and
run_slot_injection are now agentic-tool-use-loop only.
briefing_scheduler and user_profile migrated from the deleted helper to
services.llm.generate_completion (retry + keep_alive baked in).
routes/briefing.manual_trigger now persists agentic tool-call receipts
via _persist_agentic_messages (previously silently dropped them) and
adds POST /api/briefing/reset-today to wipe today's briefing messages.
BREAKING: briefing_mode setting no longer honored; no legacy fallback.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
bvandeusen