feat: local LLM multi-model, session search, cron proactive types, notifications, docs overhaul

Local LLM:
- user_settings.py: per-user hosts/models config (local_llm.json)
- routers/local_llm.py + static/local_llm.html: dedicated settings page
- llm_client.py: local OpenAI-compatible backend via httpx
- config.py: LOCAL_API_URL/KEY/MODEL + per-backend timeouts
- Active model shown near backend toggle (amber hint text)

Memory distillation:
- memory_distiller.py: DISTILL_BACKEND_MID/LONG .env overrides
- scheduler.py + notification.py: notify NC Talk after mid/long distill
- notification.py: outbound channel abstraction (NC Talk, extensible)

Session search:
- routers/files.py: GET /sessions/search?q= with excerpts grouped by date
- static/index.html + app.js: search UI in file sidebar with highlight
- _esc() helper to prevent XSS in search results

Proactive cron:
- cron_runner.py: new job types — message (send directly) and brief (LLM + send)
- Both support optional per-job channel override

Channels:
- routers/nextcloud_talk.py: consolidated using notification._send_nct_message()
- routers/auth.py: local backend status in /auth/status
- routers/chat.py: /backend returns {primary, fallback, local_model} object

UI / UX:
- Copy button for user messages (matching assistant)
- Autocomplete disabled on sensitive form fields
- settings.html: local model section replaced with link to /settings/local

Docs overhaul:
- MASTER.md hub + ARCH__SYSTEM/BACKENDS/PERSONA/CHANNELS/FUTURE.md
- ARCH__Intelligence_Layer.md replaced with redirect table
- CORTEX.md trimmed to vision only; README updated
- OPEN_WEBUI_API.md added to docs/

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

cortex/cron_runner.py

@@ -10,16 +10,20 @@ Job schema:
     "id":         "c_abc123",
     "label":      "Human-readable name",
     "schedule":   "daily:09:00",   # see parse_schedule() for all formats
-    "type":       "remind" | "note",
-    "payload":    "Text to write when the job fires",
+    "type":       "remind" | "note" | "message" | "brief",
+    "payload":    "Text or prompt when the job fires",
+    "channel":    null | "nextcloud" | "google_chat",  # for message/brief types
     "enabled":    true,
     "created_at": "ISO 8601",
     "last_run":   null | "ISO 8601"
   }
 
 Job types:
-  remind  → appends to inara/REMINDERS.md  (auto-loaded into Inara's context)
-  note    → appends to inara/SCRATCH.md    (read on demand via scratch_read)
+  remind   → appends to REMINDERS.md  (auto-loaded into context at tier 2+)
+  note     → appends to SCRATCH.md    (read on demand via scratch_read)
+  message  → sends payload as-is to NC Talk notification_room
+  brief    → runs LLM with payload as the prompt, sends response to NC Talk
+             (good for morning briefings, summaries, proactive check-ins)
 """
 
 import logging
@@ -150,6 +154,39 @@ async def run_job(job: dict) -> None:
         p.write_text(existing.rstrip() + "\n" + section)
         logger.info("cron [note] fired: %s", label)
 
+    elif job_type == "message":
+        # Send payload text directly to the user's notification channel
+        from notification import notify
+        username = job.get("user") or "scott"
+        channel  = job.get("channel") or None
+        await notify(username, payload, channel=channel)
+        logger.info("cron [message] sent: %s", label)
+
+    elif job_type == "brief":
+        # Run LLM with payload as the prompt, send response to notification channel.
+        # Great for morning briefings, reminders, proactive check-ins.
+        from context_loader import load_context
+        from llm_client import complete
+        from notification import notify
+        from persona import set_context
+        from config import settings as _s
+
+        username   = job.get("user") or _s.user_name.lower()
+        persona_nm = job.get("persona") or _s.agent_name.lower()
+        channel    = job.get("channel") or None
+        set_context(username, persona_nm)
+
+        system_prompt = load_context(2)  # tier 2: identity + memory + user profile
+        try:
+            response_text, backend = await complete(
+                system_prompt=system_prompt,
+                messages=[{"role": "user", "content": payload}],
+            )
+            await notify(username, response_text, channel=channel)
+            logger.info("cron [brief] sent via %s: %s", backend, label)
+        except Exception as e:
+            logger.error("cron [brief] LLM error for %s: %s", label, e)
+
     else:
         logger.warning("cron: unknown type %r (job %s)", job_type, job.get("id"))
         return