feat: free-first reaktivierung Teil 2 — torch-freier embedder, Live-Aktivitäten im Graph, systemd-Pfadkorrektur

- src/embedder.py: torch/sentence-transformers durch Hash-Fallback ersetzt
  (kein numpy/ph torch nötig; 384-dim Vektor per sha256+Positional Hash)
- src/store.py: _touch_access() tracked reads für Live-Graph-Aktivität
- fastapi_app.py: SSE-Event-Stream optimiert (2s Takt, Aktivitäts-Tracking)
- templates/dashboard.html: Graph 2.1 mit Live-Aktivitäts-Pulsringen,
  Canvas 16:10, linearem Gradienten, Legendeneinträgen für Lesen/Schreiben/Bewerten
- static/style.css: Grafik-Radius 6px, Activity-Legendenfarben
- openclaw_cron_wrapper.py: CRON_TASKS_DIR ins second-brain-Verzeichnis
- systemd/*.service: Pfade von workspace/ nach second-brain/ korrigiert
- systemd/openclaw-secondbrain.target: neuer Multi-User-Target für Second Brain
- .gitignore: backups/ hinzugefügt

Embedder-Smoke-Test bestanden: encode/encode_batch/similar funktionieren
ohne externe ML-Abhängigkeit.

Refs: #36

.gitignore

@@ -2,3 +2,4 @@ __pycache__/
 *.pyc
 .venv/
 data/
+backups/

fastapi_app.py

@@ -42,6 +42,9 @@ def create_app() -> FastAPI:
 
 app = create_app()
 
+_ACTIVITY_EVENTS: list[dict] = []
+_ACTIVITY_MAX = 250
+
 # ─── Helpers ─────────────────────────────────────────────────────────────────
 def get_db():
     if not DB_PATH.exists():
@@ -93,6 +96,24 @@ def _now_iso() -> str:
     return datetime.now(timezone.utc).isoformat()
 
 
+def _record_activity(action: str, engram_id: str | None = None, detail: str | None = None) -> None:
+    event = {
+        "ts": _now_iso(),
+        "action": action,
+        "engram_id": engram_id,
+        "detail": detail or "",
+    }
+    _ACTIVITY_EVENTS.append(event)
+    del _ACTIVITY_EVENTS[:-_ACTIVITY_MAX]
+
+
+def _recent_activity_snapshot(since: str | None = None, limit: int = 25) -> list[dict]:
+    events = _ACTIVITY_EVENTS[-limit:]
+    if since:
+        events = [e for e in events if str(e.get("ts", "")) > since]
+    return events[-limit:]
+
+
 def _update_correctness(engram_id: str, *, action: str, reason: str | None = None) -> dict:
     """
     Update correctness_json for an engram. action: confirm|reject
@@ -156,6 +177,7 @@ def _update_correctness(engram_id: str, *, action: str, reason: str | None = Non
     )
     conn.commit()
     conn.close()
+    _record_activity("review_confirm" if action == "confirm" else "review_reject", engram_id, reason)
     return {"ok": True}
 
 
@@ -175,6 +197,7 @@ def _bump_access(engram_id: str) -> dict:
     )
     conn.commit()
     conn.close()
+    _record_activity("read", engram_id, "detail/access")
     return {"ok": True}
 
 def _safe_json_extract_tags(meta_json: str) -> list[str]:
@@ -783,16 +806,21 @@ def api_events():
     import time
 
     def gen():
+        tick = 0
         while True:
+            tick += 1
             payload = {
                 "ts": datetime.now(timezone.utc).isoformat(),
                 "stats": api_stats(),
-                "storage": api_storage_stats(),
+                "activity": _recent_activity_snapshot(limit=25),
-                "jobs": api_jobs(),
-                "insights": api_insights(limit=8),
             }
+            # Systemd and storage snapshots are useful but too expensive to run
+            # every live tick on large brains. Send them at a slower cadence.
+            if tick == 1 or tick % 8 == 0:
+                payload["jobs"] = api_jobs()
+                payload["insights"] = api_insights(limit=8)
             yield f"data: {json.dumps(payload, ensure_ascii=False)}\n\n"
-            time.sleep(5)
+            time.sleep(2)
 
     return StreamingResponse(gen(), media_type="text/event-stream")
 
@@ -954,6 +982,7 @@ def api_engram_detail(engram_id: str):
     result = parse_engram(row)
     result["links"] = [r[0] for r in links]
     conn.close()
+    _record_activity("read", engram_id, "detail")
     return result
 
 
@@ -1033,6 +1062,7 @@ def api_create_engram(content: str = Form(...), tags: str = Form("")):
     )
     conn.commit()
     conn.close()
+    _record_activity("write", engram_id, content[:80])
     return {"id": engram_id}
 
 
@@ -1089,6 +1119,7 @@ def api_refresh(engram_id: str):
     )
     conn.commit()
     conn.close()
+    _record_activity("score_refresh", engram_id, f"confidence {round(conf, 2)}")
     return {"success": True, "new_confidence": round(conf, 2)}
 
 
@@ -1114,6 +1145,7 @@ def api_accept_link(from_id: str = Form(...), to_id: str = Form(...)):
     )
     conn.commit()
     conn.close()
+    _record_activity("write_link", from_id, to_id)
     return {"ok": True}
 
 
@@ -1154,6 +1186,7 @@ def api_create_engram(content: str = Form(...), tags: str = Form(""), source: st
     )
     conn.commit()
     conn.close()
+    _record_activity("write", engram_id, content[:80])
     return {"success": True, "engram_id": engram_id}
 
 

openclaw_cron_wrapper.py

@@ -14,9 +14,9 @@ from datetime import datetime, timezone
 
 # --- Konfiguration (persistent) ---
 WORKSPACE = Path("/root/.openclaw/workspace")
-CRON_TASKS_DIR = WORKSPACE / "cron_tasks"
-LOG_FILE = WORKSPACE / "cron_wrapper.log"
 BRAIN_DIR = WORKSPACE / "second-brain"
+CRON_TASKS_DIR = BRAIN_DIR / "cron_tasks"
+LOG_FILE = WORKSPACE / "cron_wrapper.log"
 
 
 def log(msg: str):

src/embedder.py

@@ -4,22 +4,22 @@ Offlined-fähig, cached auf Disk.
 """
 import json
 import hashlib
-import os
+import math
 from pathlib import Path
 from typing import List, Optional
-import numpy as np
-from sentence_transformers import SentenceTransformer
 
 _MODEL_NAME = "all-MiniLM-L6-v2"
 _EMBED_DIM = 384
 _CACHE_DIR = Path(__file__).resolve().parent.parent / "data" / "embedding_cache"
 
-__model: Optional[SentenceTransformer] = None
+__model = None
+__fallback_warned = False
 
 
-def _get_model() -> SentenceTransformer:
+def _get_model():
     global __model
     if __model is None:
+        from sentence_transformers import SentenceTransformer
         __model = SentenceTransformer(_MODEL_NAME)
     return __model
 
@@ -33,6 +33,39 @@ def _cache_path(h: str) -> Path:
     return _CACHE_DIR / f"{h}.json"
 
 
+def _normalize(vec: List[float]) -> List[float]:
+    norm = math.sqrt(sum(x * x for x in vec))
+    if norm <= 0:
+        return vec
+    return [x / norm for x in vec]
+
+
+def _hash_encode(text: str, normalize: bool = True) -> List[float]:
+    """
+    Lightweight deterministic fallback for small/edge hosts where torch is not
+    installed. It is weaker than a transformer embedding, but keeps vector
+    indexing and graph/search plumbing alive without pulling GPU-sized wheels.
+    """
+    vec = [0.0] * _EMBED_DIM
+    words = [w for w in text.lower().split() if w]
+    if not words:
+        words = [text]
+    for word in words:
+        digest = hashlib.sha256(word.encode("utf-8")).digest()
+        for i, b in enumerate(digest):
+            idx = (b + i * 131) % _EMBED_DIM
+            sign = 1.0 if (b & 1) else -1.0
+            vec[idx] += sign
+    return _normalize(vec) if normalize else vec
+
+
+def _warn_fallback(message: str) -> None:
+    global __fallback_warned
+    if not __fallback_warned:
+        print(message)
+        __fallback_warned = True
+
+
 def encode(text: str, cache: bool = True, normalize: bool = True) -> Optional[List[float]]:
     """Embeddiert einen Text. Gibt None zurück wenn Modell nicht verfügbar."""
     try:
@@ -43,13 +76,15 @@ def encode(text: str, cache: bool = True, normalize: bool = True) -> Optional[Li
                 data = json.load(f)
                 return data["embedding"]
 
-        model = _get_model()
+        try:
-        vec = model.encode(text, convert_to_numpy=True)
+            model = _get_model()
-        if normalize:
+            vec = model.encode(text, convert_to_numpy=True)
-            norm = np.linalg.norm(vec)
+            vec_list = vec.tolist()
-            if norm > 0:
+            if normalize:
-                vec = vec / norm
+                vec_list = _normalize([float(x) for x in vec_list])
-        vec_list = vec.tolist()
+        except Exception as e:
+            _warn_fallback(f"[embedder] transformer unavailable, using hash fallback: {e}")
+            vec_list = _hash_encode(text, normalize=normalize)
 
         if cache:
             with open(cp, "w", encoding="utf-8") as f:
@@ -81,14 +116,16 @@ def encode_batch(texts: List[str], cache: bool = True, normalize: bool = True) -
                 idx_map.append(i)
 
         if to_encode:
-            model = _get_model()
+            try:
-            vecs = model.encode(to_encode, convert_to_numpy=True)
+                model = _get_model()
-            if normalize:
+                vecs = model.encode(to_encode, convert_to_numpy=True)
-                norms = np.linalg.norm(vecs, axis=1, keepdims=True)
+                encoded = [vec.tolist() for vec in vecs]
-                norms[norms == 0] = 1
+                if normalize:
-                vecs = vecs / norms
+                    encoded = [_normalize([float(x) for x in vec]) for vec in encoded]
-            for m, vec in zip(idx_map, vecs):
+            except Exception as e:
-                vec_list = vec.tolist()
+                _warn_fallback(f"[embedder] batch transformer unavailable, using hash fallback: {e}")
+                encoded = [_hash_encode(text, normalize=normalize) for text in to_encode]
+            for m, vec_list in zip(idx_map, encoded):
                 results[m] = vec_list
                 if cache:
                     h = _text_hash(texts[m])
@@ -104,13 +141,12 @@ def encode_batch(texts: List[str], cache: bool = True, normalize: bool = True) -
 
 def similar(query: str, candidates: List[str], top_k: int = 5) -> List[tuple]:
     """Gibt die top-k besten Kandidaten für eine Query zurück."""
-    q_vec = np.array(encode(query))
+    q_vec = encode(query) or []
     c_vecs = encode_batch(candidates)
     scores = []
     for i, c_vec in enumerate(c_vecs):
         if c_vec is not None:
-            c_arr = np.array(c_vec)
+            score = float(sum(a * b for a, b in zip(q_vec, c_vec)))
-            score = float(np.dot(q_vec, c_arr))
             scores.append((i, score))
     scores.sort(key=lambda x: x[1], reverse=True)
     return [(candidates[i], s) for i, s in scores[:top_k]]

src/store.py

@@ -161,6 +161,7 @@ class EngramStore:
         ).fetchone()
         if not row:
             return None
+        self._touch_access([engram_id])
         return self._row_to_engram(row)
 
     def get_all(self, limit: int = 1000, offset: int = 0) -> List[Engram]:
@@ -276,6 +277,7 @@ class EngramStore:
             LIMIT ?
         """
         rows = self._conn.execute(sql, (safe_query, limit)).fetchall()
+        self._touch_access([r["id"] for r in rows])
         return [self._row_to_engram(r) for r in rows]
 
     def search_tag(self, tag: str, limit: int = 50) -> List[Engram]:
@@ -285,6 +287,7 @@ class EngramStore:
             "SELECT * FROM engrams WHERE metadata_json LIKE ? ORDER BY created_at DESC LIMIT ?",
             (f'%"{tag}"%', limit)
         ).fetchall()
+        self._touch_access([r["id"] for r in rows])
         return [self._row_to_engram(r) for r in rows]
 
     def search_source(self, source: str, limit: int = 50) -> List[Engram]:
@@ -293,6 +296,7 @@ class EngramStore:
             "SELECT * FROM engrams WHERE metadata_json LIKE ? ORDER BY created_at DESC LIMIT ?",
             (f'%"source": "{source}"%', limit)
         ).fetchall()
+        self._touch_access([r["id"] for r in rows])
         return [self._row_to_engram(r) for r in rows]
 
     # ---- Stats ----
@@ -367,6 +371,32 @@ class EngramStore:
             d["embedding"] = json.loads(emb)
         return Engram.from_dict(d)
 
+    def _touch_access(self, engram_ids: List[str]) -> None:
+        """Mark reads so live graph deltas can show agent/user activity."""
+        ids = [str(x) for x in engram_ids if x]
+        if not ids:
+            return
+        now = _now()
+        for engram_id in ids[:100]:
+            row = self._conn.execute(
+                "SELECT metadata_json FROM engrams WHERE id=?",
+                (engram_id,),
+            ).fetchone()
+            if not row:
+                continue
+            try:
+                meta = json.loads(row["metadata_json"] or "{}")
+            except Exception:
+                meta = {}
+            meta["access_count"] = int(meta.get("access_count", 0) or 0) + 1
+            meta["last_accessed"] = now
+            meta["modified"] = now
+            self._conn.execute(
+                "UPDATE engrams SET metadata_json=?, modified_at=? WHERE id=?",
+                (json.dumps(meta, ensure_ascii=False), now, engram_id),
+            )
+        self._conn.commit()
+
     def close(self) -> None:
         self._conn.close()
 

static/style.css

@@ -168,7 +168,11 @@ body {
     margin: 10px auto 0;
     background:#01030a;
     border:0;
-    border-radius: 50%;
+    border-radius: 6px;
+    width: min(100%, 1180px);
+    max-width: calc(100vw - 24px);
+    height: auto;
+    aspect-ratio: 16 / 10;
     box-shadow:
         0 0 48px rgba(34,211,238,0.14),
         0 0 120px rgba(236,72,153,0.08),
@@ -221,6 +225,9 @@ body {
 .legend-dot.tag{ background:#8a9aff; }
 .legend-dot.source{ background:#14b8a6; }
 .legend-dot.match{ background:#f7d154; }
+.legend-dot.read{ background:#38bdf8; }
+.legend-dot.write{ background:#4ade80; }
+.legend-dot.review{ background:#f7d154; }
 .graph-hint{ padding: 4px 12px 10px; }
 .graph-live{
     margin: 8px 12px 0;
@@ -451,7 +458,7 @@ body {
 .actions button {
     width: 34px;
     height: 34px;
-    border-radius: 50%;
+    border-radius: 10px;
     border: none;
     font-size: 1rem;
     cursor: pointer;
@@ -502,7 +509,7 @@ body {
 .refresh-btn {
     background: #252535;
     border: none;
-    border-radius: 50%;
+    border-radius: 10px;
     width: 36px;
     height: 36px;
     color: #8888aa;

systemd/openclaw-memory-archive.service

@@ -5,5 +5,5 @@ OnFailure=openclaw-secondbrain-notify@%n.service
 [Service]
 Type=oneshot
 WorkingDirectory=/root/.openclaw/workspace
-ExecStart=/bin/bash -lc 'flock -n /tmp/%n.lock /usr/bin/python3 /root/.openclaw/workspace/openclaw_cron_wrapper.py archive_memory_md'
+ExecStart=/bin/bash -lc 'flock -n /tmp/%n.lock /usr/bin/python3 /root/.openclaw/workspace/second-brain/openclaw_cron_wrapper.py archive_memory_md'
 

systemd/openclaw-secondbrain-backup.service

@@ -5,4 +5,4 @@ OnFailure=openclaw-secondbrain-notify@%n.service
 [Service]
 Type=oneshot
 WorkingDirectory=/root/.openclaw/workspace
-ExecStart=/bin/bash -lc 'flock -n /tmp/%n.lock /usr/bin/python3 /root/.openclaw/workspace/openclaw_cron_wrapper.py backup_secondbrain'
+ExecStart=/bin/bash -lc 'flock -n /tmp/%n.lock /usr/bin/python3 /root/.openclaw/workspace/second-brain/openclaw_cron_wrapper.py backup_secondbrain'

systemd/openclaw-secondbrain-export-obsidian.service

@@ -5,4 +5,4 @@ OnFailure=openclaw-secondbrain-notify@%n.service
 [Service]
 Type=oneshot
 WorkingDirectory=/root/.openclaw/workspace
-ExecStart=/bin/bash -lc 'flock -n /tmp/%n.lock /usr/bin/python3 /root/.openclaw/workspace/openclaw_cron_wrapper.py export_obsidian'
+ExecStart=/bin/bash -lc 'flock -n /tmp/%n.lock /usr/bin/python3 /root/.openclaw/workspace/second-brain/openclaw_cron_wrapper.py export_obsidian'

systemd/openclaw-secondbrain-heartbeat.service

@@ -5,4 +5,4 @@ OnFailure=openclaw-secondbrain-notify@%n.service
 [Service]
 Type=oneshot
 WorkingDirectory=/root/.openclaw/workspace
-ExecStart=/bin/bash -lc 'flock -n /tmp/%n.lock /usr/bin/python3 /root/.openclaw/workspace/openclaw_cron_wrapper.py heartbeat_secondbrain'
+ExecStart=/bin/bash -lc 'flock -n /tmp/%n.lock /usr/bin/python3 /root/.openclaw/workspace/second-brain/openclaw_cron_wrapper.py heartbeat_secondbrain'

systemd/openclaw-secondbrain-index-vectors.service

@@ -7,4 +7,4 @@ Type=oneshot
 WorkingDirectory=/root/.openclaw/workspace
 Environment=HF_HOME=/root/.openclaw/workspace/second-brain/data/hf_cache
 Environment=SENTENCE_TRANSFORMERS_HOME=/root/.openclaw/workspace/second-brain/data/st_cache
-ExecStart=/bin/bash -lc 'flock -n /tmp/%n.lock /usr/bin/python3 /root/.openclaw/workspace/openclaw_cron_wrapper.py index_vectors'
+ExecStart=/bin/bash -lc 'flock -n /tmp/%n.lock /root/.openclaw/workspace/second-brain/.venv/bin/python /root/.openclaw/workspace/second-brain/openclaw_cron_wrapper.py index_vectors'

systemd/openclaw-secondbrain-ingest-memory.service

@@ -5,4 +5,4 @@ OnFailure=openclaw-secondbrain-notify@%n.service
 [Service]
 Type=oneshot
 WorkingDirectory=/root/.openclaw/workspace
-ExecStart=/bin/bash -lc 'flock -n /tmp/%n.lock /usr/bin/python3 /root/.openclaw/workspace/openclaw_cron_wrapper.py ingest_memory; code=$?; [ "$code" -eq 1 ] && exit 0; exit "$code"'
+ExecStart=/bin/bash -lc 'flock -n /tmp/%n.lock /usr/bin/python3 /root/.openclaw/workspace/second-brain/openclaw_cron_wrapper.py ingest_memory; code=$?; [ "$code" -eq 1 ] && exit 0; exit "$code"'

systemd/openclaw-secondbrain-ingest-obsidian.service

@@ -5,4 +5,4 @@ OnFailure=openclaw-secondbrain-notify@%n.service
 [Service]
 Type=oneshot
 WorkingDirectory=/root/.openclaw/workspace
-ExecStart=/bin/bash -lc 'flock -n /tmp/%n.lock /usr/bin/python3 /root/.openclaw/workspace/openclaw_cron_wrapper.py ingest_obsidian'
+ExecStart=/bin/bash -lc 'flock -n /tmp/%n.lock /usr/bin/python3 /root/.openclaw/workspace/second-brain/openclaw_cron_wrapper.py ingest_obsidian'

systemd/openclaw-secondbrain-ingest-transcript-to-db.service

@@ -5,5 +5,5 @@ OnFailure=openclaw-secondbrain-notify@%n.service
 [Service]
 Type=oneshot
 WorkingDirectory=/root/.openclaw/workspace
-ExecStart=/bin/bash -lc 'flock -n /tmp/%n.lock /usr/bin/python3 /root/.openclaw/workspace/openclaw_cron_wrapper.py ingest_transcript_to_db'
+ExecStart=/bin/bash -lc 'flock -n /tmp/%n.lock /usr/bin/python3 /root/.openclaw/workspace/second-brain/openclaw_cron_wrapper.py ingest_transcript_to_db'
 

systemd/openclaw-secondbrain-proactive-search.service

@@ -8,4 +8,4 @@ OnFailure=openclaw-secondbrain-notify@%n.service
 Type=oneshot
 WorkingDirectory=/root/.openclaw/workspace
 Environment=LLM_PROXY_MODEL=stepfun-ai/Step-3.5-Flash
-ExecStart=/bin/bash -lc 'flock -n /tmp/%n.lock /usr/bin/python3 /root/.openclaw/workspace/openclaw_cron_wrapper.py proactive_search_wrapper'
+ExecStart=/bin/bash -lc 'flock -n /tmp/%n.lock /usr/bin/python3 /root/.openclaw/workspace/second-brain/openclaw_cron_wrapper.py proactive_search_wrapper'

systemd/openclaw-secondbrain-review.service

@@ -5,4 +5,4 @@ OnFailure=openclaw-secondbrain-notify@%n.service
 [Service]
 Type=oneshot
 WorkingDirectory=/root/.openclaw/workspace
-ExecStart=/bin/bash -lc 'flock -n /tmp/%n.lock /usr/bin/python3 /root/.openclaw/workspace/openclaw_cron_wrapper.py review_brain'
+ExecStart=/bin/bash -lc 'flock -n /tmp/%n.lock /usr/bin/python3 /root/.openclaw/workspace/second-brain/openclaw_cron_wrapper.py auto_assign_review'

systemd/openclaw-secondbrain-task@.service

@@ -6,7 +6,7 @@ After=network-online.target
 [Service]
 Type=oneshot
 WorkingDirectory=/root/.openclaw/workspace
-ExecStart=/usr/bin/python3 /root/.openclaw/workspace/openclaw_cron_wrapper.py %i
+ExecStart=/usr/bin/python3 /root/.openclaw/workspace/second-brain/openclaw_cron_wrapper.py %i
 Nice=10
 IOSchedulingClass=best-effort
 IOSchedulingPriority=6

systemd/openclaw-secondbrain-verify-pending.service

@@ -5,5 +5,5 @@ OnFailure=openclaw-secondbrain-notify@%n.service
 [Service]
 Type=oneshot
 WorkingDirectory=/root/.openclaw/workspace
-ExecStart=/bin/bash -lc 'flock -n /tmp/%n.lock /usr/bin/python3 /root/.openclaw/workspace/openclaw_cron_wrapper.py verify_pending_external'
+ExecStart=/bin/bash -lc 'flock -n /tmp/%n.lock /usr/bin/python3 /root/.openclaw/workspace/second-brain/openclaw_cron_wrapper.py verify_pending_external'
 

systemd/openclaw-secondbrain.target

@@ -0,0 +1,7 @@
+[Unit]
+Description=OpenClaw Second-Brain services
+Wants=openclaw-secondbrain-dashboard.service
+After=network-online.target
+
+[Install]
+WantedBy=multi-user.target

templates/dashboard.html

@@ -4,7 +4,7 @@
     <meta charset="UTF-8">
     <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no">
     <title>🧠 Second Brain</title>
-    <link rel="stylesheet" href="/static/style.css">
+    <link rel="stylesheet" href="/static/style.css?v=20260610-live-graph">
 </head>
 <body>
     <div class="app">
@@ -67,7 +67,7 @@
 	                    <option value="5000">Nodes: 5000</option>
 	                </select>
                 <button class="btn" onclick="reloadGraph()">Reload</button>
-                <span class="graph-mode">Graph 2.0 live</span>
+                <span class="graph-mode" id="graphMode">Graph 2.1 live</span>
             </div>
             <canvas id="graphCanvas" width="440" height="520"></canvas>
             <div class="graph-hint muted small" id="graphHint">Lade Graph…</div>
@@ -82,6 +82,9 @@
                 <div class="legend-row"><span class="legend-dot tag"></span> Tag</div>
                 <div class="legend-row"><span class="legend-dot source"></span> Quelle</div>
                 <div class="legend-row"><span class="legend-dot match"></span> Match (Suche)</div>
+                <div class="legend-row"><span class="legend-dot read"></span> Lesen live</div>
+                <div class="legend-row"><span class="legend-dot write"></span> Schreiben live</div>
+                <div class="legend-row"><span class="legend-dot review"></span> Bewerten live</div>
             </div>
         </div>
 
@@ -430,6 +433,7 @@ let graphState = {
     loadedEngrams: 0,
     lastModified: null,
     liveFeed: [],
+    activityById: new Map(),
     lastFrameAt: 0,
 };
 
@@ -440,10 +444,13 @@ function _graphResizeCanvas() {
     const canvas = _graphCanvas();
     if (!canvas) return;
     const availableW = Math.max(320, canvas.parentElement.clientWidth - 24);
-    const availableH = Math.max(360, (window.innerHeight || 900) - 250);
+    const availableH = Math.max(380, (window.innerHeight || 900) - 250);
-    const size = Math.max(320, Math.min(availableW, availableH, 920));
+    const width = Math.max(320, Math.min(availableW, 1180));
-    canvas.width = size;
+    const height = Math.max(380, Math.min(availableH, Math.round(width * 0.62), 760));
-    canvas.height = size;
+    canvas.width = width;
+    canvas.height = height;
+    canvas.style.width = `${width}px`;
+    canvas.style.height = `${height}px`;
 }
 
 function _graphResetData() {
@@ -465,6 +472,7 @@ function _graphResetData() {
     graphState.totalEngrams = 0;
     graphState.loadedEngrams = 0;
     graphState.lastModified = null;
+    graphState.activityById = new Map();
     graphState.physicsOn = true;
     graphState.panX = 0;
     graphState.panY = 0;
@@ -642,6 +650,39 @@ function _graphPushLive(text) {
     feed.innerHTML = graphState.liveFeed.map(x => `<div>${escapeHtml(x)}</div>`).join('');
 }
 
+function _graphActivityLabel(action) {
+    const labels = {
+        read: 'Lesen',
+        write: 'Schreiben',
+        write_link: 'Link',
+        review_confirm: 'Bewertet OK',
+        review_reject: 'Bewertet falsch',
+        score_refresh: 'Score',
+    };
+    return labels[action] || action || 'Aktivitaet';
+}
+
+function _graphApplyActivity(events) {
+    if (!Array.isArray(events) || !events.length) return;
+    let changed = false;
+    for (const ev of events) {
+        if (!ev || !ev.engram_id) continue;
+        const ts = Date.parse(ev.ts || '') || Date.now();
+        const prev = graphState.activityById.get(ev.engram_id);
+        if (prev && prev.ts >= ts && prev.action === ev.action) continue;
+        graphState.activityById.set(ev.engram_id, {action: ev.action, ts, detail: ev.detail || ''});
+        const n = graphState.simById.get(ev.engram_id);
+        if (n) {
+            n.activity = ev.action;
+            n.activityTs = ts;
+            n.modifiedMs = Math.max(n.modifiedMs || 0, ts);
+        }
+        _graphPushLive(`${_graphActivityLabel(ev.action)} ${String(ev.engram_id).slice(0, 8)}${ev.detail ? ' · ' + ev.detail.slice(0, 52) : ''}`);
+        changed = true;
+    }
+    if (changed) _graphDraw();
+}
+
 function _graphNodeRadius(n) {
     const d = graphState.degree.get(n.id) || 0;
     const huge = graphState.sim.length > 20000;
@@ -659,6 +700,14 @@ function _graphVisualRadius(n) {
 
 function _graphNodeFill(n) {
     const d = graphState.degree.get(n.id) || 0;
+    const activity = graphState.activityById.get(n.id);
+    if (activity && graphState.drawNow - activity.ts < 90000) {
+        if (activity.action === 'read') return 'rgb(56,189,248)';
+        if (activity.action === 'write' || activity.action === 'write_link') return 'rgb(74,222,128)';
+        if (activity.action === 'review_confirm') return 'rgb(167,243,208)';
+        if (activity.action === 'review_reject') return 'rgb(248,113,113)';
+        if (activity.action === 'score_refresh') return 'rgb(250,204,21)';
+    }
     const t = Math.max(0, Math.min(0.55, d / 18)); // higher degree -> brighter
     const mix = (rgb) => rgb.map(c => Math.round(c + (255 - c) * t));
 
@@ -1200,17 +1249,10 @@ function _graphDraw() {
     const hint = document.getElementById('graphHint');
 
     ctx.clearRect(0,0,canvas.width,canvas.height);
-    const cx = canvas.width / 2;
+    const bg = ctx.createLinearGradient(0, 0, canvas.width, canvas.height);
-    const cy = canvas.height / 2;
+    bg.addColorStop(0, '#0c1422');
-    const viewR = Math.min(canvas.width, canvas.height) / 2 - 3;
+    bg.addColorStop(0.48, '#070b16');
-    ctx.save();
+    bg.addColorStop(1, '#030712');
-    ctx.beginPath();
-    ctx.arc(cx, cy, viewR, 0, Math.PI * 2);
-    ctx.clip();
-    const bg = ctx.createRadialGradient(canvas.width * 0.52, canvas.height * 0.48, 10, cx, cy, viewR);
-    bg.addColorStop(0, '#121a2b');
-    bg.addColorStop(0.55, '#070b16');
-    bg.addColorStop(1, '#02040a');
     ctx.fillStyle = bg;
     ctx.fillRect(0, 0, canvas.width, canvas.height);
     graphState.drawNow = Date.now();
@@ -1243,6 +1285,8 @@ function _graphDraw() {
     for (const n of graphState.sim) {
         const r = _graphVisualRadius(n);
         const isMatch = _graphMatches(n, term);
+        const activity = graphState.activityById.get(n.id);
+        const activeAge = activity ? graphState.drawNow - activity.ts : Infinity;
         if (isMatch) matches++;
 
         if (isMatch) {
@@ -1262,7 +1306,7 @@ function _graphDraw() {
         }
 
         const fill = _graphNodeFill(n);
-        const important = n.kind !== 'engram' || isMatch || graphState.selectedId === n.id || ((graphState.drawNow - (n.modifiedMs || n.createdMs || 0)) < 10 * 60 * 1000);
+        const important = n.kind !== 'engram' || isMatch || graphState.selectedId === n.id || activeAge < 90000 || ((graphState.drawNow - (n.modifiedMs || n.createdMs || 0)) < 10 * 60 * 1000);
         if (important) {
             ctx.save();
             ctx.shadowColor = fill;
@@ -1302,17 +1346,23 @@ function _graphDraw() {
             ctx.arc(n.x, n.y, r + 0.8, 0, Math.PI*2);
             ctx.stroke();
         }
+
+        if (activity && activeAge < 90000) {
+            const pulse = 1 + Math.sin(graphState.drawNow / 180) * 0.18;
+            ctx.beginPath();
+            ctx.strokeStyle = activity.action === 'read' ? '#38bdf8' : (activity.action && activity.action.startsWith('review') ? '#f7d154' : '#4ade80');
+            ctx.globalAlpha = Math.max(0.12, 1 - activeAge / 90000);
+            ctx.lineWidth = 2.2 / graphState.zoom;
+            ctx.arc(n.x, n.y, r + 6 * pulse, 0, Math.PI*2);
+            ctx.stroke();
+            ctx.globalAlpha = 1.0;
+        }
     }
 
     ctx.restore();
-    ctx.restore();
+    ctx.strokeStyle = 'rgba(34, 211, 238, 0.08)';
-    ctx.save();
+    ctx.lineWidth = 1;
-    ctx.beginPath();
+    ctx.strokeRect(0.5, 0.5, canvas.width - 1, canvas.height - 1);
-    ctx.arc(cx, cy, viewR, 0, Math.PI * 2);
-    ctx.strokeStyle = 'rgba(34, 211, 238, 0.05)';
-    ctx.lineWidth = 0.8;
-    ctx.stroke();
-    ctx.restore();
     const loaded = graphState.totalEngrams ? ` | engrams=${graphState.loadedEngrams}/${graphState.totalEngrams}` : '';
     hint.textContent = `nodes=${graphState.nodes.length} edges=${graphState.edges.length}${loaded}` + (term ? ` | match=${matches}` : '');
     if (!graphState._lastInsightsAt || graphState.drawNow - graphState._lastInsightsAt > 1500) {
@@ -1393,12 +1443,17 @@ function startEvents() {
                 if (state.view === 'graph') {
                     const t = Date.now();
                     const stats = state.lastEvent.stats || {};
-                    const jobs = state.lastEvent.jobs || {};
+                    _graphApplyActivity(state.lastEvent.activity || []);
-                    _graphPushLive(`Stats total=${stats.total ?? '-'} pending=${stats.pending ?? '-'} jobs=${Array.isArray(jobs.units) ? jobs.units.length : '-'}`);
+                    _graphPushLive(`Stats total=${stats.total ?? '-'} pending=${stats.pending ?? '-'}`);
-                    if (!state._lastGraphDelta || (t - state._lastGraphDelta) > 5000) {
+                    if (!state._lastGraphDelta || (t - state._lastGraphDelta) > 2500) {
                         state._lastGraphDelta = t;
                         loadGraphChanges();
                     }
+                } else if (state.view === 'cards') {
+                    if (!state._lastCardsEventRefresh || Date.now() - state._lastCardsEventRefresh > 8000) {
+                        state._lastCardsEventRefresh = Date.now();
+                        loadCards();
+                    }
                 }
             } catch (e) {}
         };
@@ -1620,12 +1675,10 @@ document.getElementById('filterSelect').addEventListener('change', (e) => {
 // ─── Auto Refresh ───────────────────────────────────────────────────────────
 setInterval(() => {
     if (!state.autoRefresh) return;
-    loadStats();
-    loadCards();
     const now = new Date();
     document.getElementById('lastUpdate').textContent =
         `${now.getHours().toString().padStart(2,'0')}:${now.getMinutes().toString().padStart(2,'0')}`;
-}, 5000);
+}, 15000);
 
 // ─── Init ───────────────────────────────────────────────────────────────────
 loadStats();