kami
5fe8f228c1
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>
30 lines
2.7 KiB
Markdown
30 lines
2.7 KiB
Markdown
# Plan: Behavioral Memory — How I Do Stuff
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**Goal:** Maven builds a rich behavioral model of the user over time: habits, routines, preferences, recurring tasks, deadlines, and commitments. She uses this model to proactively propose plans, surface reminders, and adjust her behavior — all grounded in the existing fact/event/note stores.
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**Done when:**
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- `internal/memory/behavior.go` — behavioral model builder reads facts, events, notes, nudges, reminders, tools, calendar events
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- Model is exposed as a structured profile: `{"routines": [...], "preferences": {...}, "recurring_tasks": [...], "typical_schedule": {...}}`
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- LLM generates this profile periodically (daily) and stores it as a note/fact
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- Proactive loop uses the profile to propose daily plans: "сегодня ты обычно делаешь X, Y, Z. напомнить?"
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- Profile is queryable via voice: "что я обычно делаю по вторникам?"
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- Profile updates on fact write — not just periodic — so a new "walk" fact immediately adjusts the walking schedule
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**Scope:**
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- `internal/memory/behavior.go` — behavior builder
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- `internal/memory/profile.go` — profile data structures (routines, preferences, schedule, commitments)
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- Reuses `internal/llm.Client` for profile generation
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- Reuses `internal/pattern/detector.go` for interval detection on behavioral data
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- Extends `internal/router/intent.go` — `IntentQuery` extended with behavioral sub-queries
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- Reuses `internal/delivery.Dispatcher` for surfacing proposals as nudges
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**Steps:**
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1. Design profile schema: `BehaviorProfile` struct with `Routines []Routine`, `Preferences map[string]string`, `RecurringTasks []Task`, `WeeklySchedule map[string][]Activity`
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2. Create `internal/memory/behavior.go` — `Builder` that reads `store.RecentFacts(1000)`, `store.EventsFor` (all action+object combos), `store.RecentNotes(500)`, `store.ListReminders`
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3. Implement profile generation — prompt for `llm.Client` that takes raw facts and outputs a structured JSON profile; stores result as a fact (`kind=config, key=behavior_profile`)
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4. Create `internal/memory/planner.go` — reads the profile each morning (via routine cron `"0 8 * * *"`) and proposes a daily plan through `dispatcher.DispatchNudge`
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5. Add real-time updates — when a fact is written via `WriteFact`, the behavior builder incrementally updates the relevant profile section (append-only, no full rebuild)
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6. Wire voice query — `"что я обычно делаю?"` routes to `IntentQuery` → behavior profile lookup → LLM-phrased answer
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7. Add IPC read method `MethodGetBehaviorProfile` so mavweb can display it on `/dash`
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8. Test with synthetic fact history — verify weekly schedule is correctly inferred
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