diff --git a/winnow/diversity.py b/winnow/diversity.py
index 711846c..8976d0d 100644
--- a/winnow/diversity.py
+++ b/winnow/diversity.py
@@ -373,6 +373,8 @@ def _compute_adaptive_threshold(emb_normed: np.ndarray, entity_type: str) -> flo
     # Compute pairwise cosine distances for the sample
     pairwise = 1 - sample @ sample.T
     upper_tri = pairwise[np.triu_indices(len(sample), k=1)]
+    if len(upper_tri) == 0:
+        return 0.05
     median_dist = float(np.median(upper_tri))
 
     # Faces: 20% of median (tighter — want fewer, more distinct images)
@@ -421,7 +423,7 @@ def _cluster_aware_selection(
     target = Config.MAX_AUTO_IMAGES if limit == "auto" else limit
 
     # --- Stage 1: K-Medoids clustering ---
-    k = min(max(5, target // 4), n // 3, n)  # e.g., 5-20 clusters
+    k = min(max(5, target // 4), max(1, n // 3), n)  # e.g., 1-20 clusters
     logger.debug(f"Clustering {n} embeddings into {k} groups (K-Medoids)...")
 
     # Compute full cosine distance matrix
diff --git a/winnow/executor.py b/winnow/executor.py
index 2a56521..ab0402e 100644
--- a/winnow/executor.py
+++ b/winnow/executor.py
@@ -580,10 +580,12 @@ def upload_to_frigate(jobs: list[dict]) -> None:
                             )
                             try:
                                 error_detail = resp.json().get("message", resp.text[:100])
-                                progress.console.print(f"      [dim]{error_detail}[/dim]")
                             except Exception:
                                 error_detail = resp.text[:100]
+                            if resp.status_code == 400:
                                 progress.console.print(f"      [dim]{error_detail}[/dim]")
+                            else:
+                                logger.debug(f"{fname} HTTP {resp.status_code}: {error_detail}")
                             if resp.status_code == 400 and "face" in error_detail.lower():
                                 asset_id = asset_map.get(fname)
                                 if asset_id: