diff --git a/prototype/cluster.py b/prototype/cluster.py
index 0bc7517..f1e2cff 100644
--- a/prototype/cluster.py
+++ b/prototype/cluster.py
@@ -307,24 +307,54 @@ class Clusterer:
 
 
 # ---------------------------------------------------------------------------
+
+
 # Rank
+
+
 # ---------------------------------------------------------------------------
 
+
+# Simple per-source authority (higher = more weight in ranking).
+# Extend this map for real sources; unknown sources default to 1.0.
+SOURCE_AUTHORITY = {
+    "reuters": 2.0,
+    "associated press": 2.0,
+    "apnews": 2.0,
+    "bbc": 1.8,
+    "nytimes": 1.7,
+    "the new york times": 1.7,
+    "cnn": 1.5,
+    "theverge": 1.3,
+    "techcrunch": 1.3,
+    "arstechnica": 1.3,
+}
+
+
+def _get_authority(members: list) -> float:
+    """Return max authority from member sources (or 1.0)."""
+    if not members:
+        return 1.0
+    auth = 1.0
+    for m in members:
+        s = (m.source or "").lower()
+        for key, val in SOURCE_AUTHORITY.items():
+            if key in s:
+                auth = max(auth, val)
+    return auth
+
+
 def compute_scores(stories: list[Story], now: float, alpha: float = 1.8) -> list:
     """score = authority * (coverage_count + 1) / (age_h + 2)^1.8  (HN-style decay)"""
     scored = []
     for s in stories:
         age_h = max(0.0, (now - s.first_published) / 3600.0)
-        rank = 1.0 * (s.coverage_count + 1) / (age_h + 2.0) ** alpha
+        authority = _get_authority(s.members)
+        rank = authority * (s.coverage_count + 1) / (age_h + 2.0) ** alpha
         scored.append((rank, s))
     scored.sort(key=lambda x: -x[0])
     return scored
 
-
-# ---------------------------------------------------------------------------
-# LLM summary of top-k only (capped; host llama.cpp/Hermes, never DeepSeek)
-# ---------------------------------------------------------------------------
-
 def llm_summarize_topk(scored, topk: int) -> None:
     base = os.environ.get("LLM_BASE_URL", "http://127.0.0.1:8080/v1").rstrip("/")
     model = os.environ.get("LLM_MODEL", "hermes")