Behavioural memory, narrowed on purpose. internal/memory/behavior.go builds a
profile out of self-facts — distinct days per weekday, median time of day — and
reads it back in RU; router.ParseHabitQuery finds the weekday deterministically;
a `habits` query source answers the question.
Three things the plan doc asks for are deliberately absent, and the doc now
records why:
- The profile is COUNTED, not LLM-generated. A 1.7B asked to summarise a year of
habits writes fluent claims about the owner's life that no row supports, and a
wrong claim about him is the most expensive kind of wrong maven can be.
- No cached profile fact, so no "update on fact write" machinery. It is
recomputed on the question; a cache that can disagree with its own rows is two
truths.
- No proactive daily plan nudge. A dispatcher proposal at 08:00 every day is the
definition of a nag. The path from "she noticed a pattern" to "she acts on it"
already exists in internal/pattern with the proposal queue on /routines, and it
goes through him.
A one-off is not a habit: an activity needs two distinct days before she will
call it usual, and until then she says she does not know yet. Only self-facts
count — env rows are the world, config rows are her own tuning state. The typical
time is a median so one 03:00 outlier cannot move a morning habit into the night.
An unrecognised fact key is read back verbatim rather than glossed into something
she made up.
The source sits before "calendar" in querySources, and its matcher requires a
habit marker, so "что я делаю в среду?" still reaches the calendar — answering a
question about this coming Wednesday with a statistical average would be
answering a different question.
Verified: make build and make test both exit 0.
Ships the real, local, testable part of the memory-evaluation plan
(docs/plans/03-memory-evaluation.md): Maven reads back her own recent
memory on a slow ticker, asks the resident model what it notices, and
records the confident answers as notes.
internal/memeval — not internal/memory/eval.go as the plan says, because
internal/store imports internal/memory for the vector backend and an
evaluator has to read store.Fact/Note/Nudge, which would close the
cycle. Evaluate() gathers RecentFacts/RecentNotes/RecentNudges, prompts
under a GBNF grammar bounded to three {observation, confidence,
suggested_action} objects, drops anything under min_confidence,
deduplicates against what earlier runs wrote, and writes the rest as
notes with source infer:memory-eval. /dash already renders notes with
their source, so the output is visible with no UI change.
cmd/mavend/memoryeval.go drives it on its own goroutine and ticker, not
on the 60s tick: an evaluation is a multi-second round-trip on the same
llama-server that answers voice turns, and it runs hourly at most. The
memory_eval config block is absent by default and absence means the
goroutine does not exist. No llama-server phraser also means no loop —
there is no template fallback, because a "memory evaluation" assembled
from templates is a fixed sentence pretending to be an observation.
What it deliberately cannot do, since this is the feature most likely to
turn Maven into a nag:
- It cannot speak. No dispatcher reference, no channel, no nudge. An
observation is a thought she wrote down and he reads on /dash.
Announcing them is a separate decision with its own opt-in.
- It cannot act. suggested_action is recorded as text and interpreted
by nobody — no reminder, routine or fact is created from it.
- It says nothing about an empty store: no memory means no LLM call,
so there are no observations invented out of two facts.
- Its own notes are excluded from the next evaluation's input, and are
written with a nil embedding so they stay out of the recall pool.
The plan's remaining items (dispatching observations, an /eval IPC
method and trace view, RecentEvents) and the fact that output quality is
entirely unmeasured are written up at the bottom of the plan doc.
Add a read-only /ecosystem page that consumes the sibling services'
JSON APIs (Nexus entities, Praxis attention, Hexis capabilities),
fetched concurrently with honest per-panel error states. Siblings stay
headless — mavweb is their human surface (arch §16). Wired via mavweb
-nexus/-praxis/-hexis flags; mavweb joins the ecosystem compose network.
Fix mobile horizontal overflow across all pages: .content is a flex
child with default min-width:auto, so it refused to shrink below the
tables' intrinsic width. min-width:0 lets wide tables pan inside .scroll
instead of dragging the page sideways. Verified via CDP geometry check
(scrollWidth === clientWidth at 430px).
Also includes in-progress Ethos UI redesign, ecosystem deploy compose,
and planning docs.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Route contract is now a JSON array of action objects (one per ask) so
compound utterances route all their intents, not just the first. Grammar
root emits `[{intent...},...]`; parseActions tolerates a bare object.
Cascade still returns one Decision — full N-action dispatch lands with the
engine turn-on (marked in-code).
Router prompt rewritten shorter + decision-ordered (prompt-guy feedback),
fact redefined as "implicit update" not "trackable state", kept in Russian
to match the CPT base + phraser. "интент" → "намерение".
CLAUDE.md: routing-architecture section + refreshed open items.
docs/plans: route-data generation plan.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017GMrVfuYN3nE4L1vEiFYC9
Daemon side of Decision B: parse {"response","mood"} across the 4 consumers
(replier, nudges, reminders, chat), fall back to legacy formats. Drop the
LLM router — the classifier handles routing; replier/phraser share one
llm.Client (timeout 20s->60s). llm.Client reads reasoning_content when
content is empty (thinking models).
Docs: TTS piper-student plan (OmniVoice teacher -> piper student, from
scratch, phoneme-first). CLAUDE.md training guide.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- Add reactive_notes_test.go: tests for context-aware reactive nudge
generation using LLM phraser with dialogue history.
- Add docs/plans/2026-07-10-router-lfm-foundation.md: architecture research
on replacing classifier cascade with LFM-based router.
kami