diff --git a/_includes/code/howto/search.boost.py b/_includes/code/howto/search.boost.py
index cb91f51d..4caa5f3f 100644
--- a/_includes/code/howto/search.boost.py
+++ b/_includes/code/howto/search.boost.py
@@ -81,7 +81,7 @@
     query="transformer architectures",
     limit=5,
     # highlight-start
-    boost=Boost.property(
+    boost=Boost.numeric_property(
         "likes",
         modifier=Boost.Modifier.LOG1P,
         weight=0.7,
@@ -167,8 +167,10 @@
     limit=5,
     # highlight-start
     boost=Boost.blend(
-        Boost.time_decay("published", origin="now", scale=timedelta(days=30), weight=2.0),
-        Boost.property("likes", modifier=Boost.Modifier.LOG1P, weight=1.0),
+        [
+            Boost.time_decay("published", origin="now", scale=timedelta(days=30), weight=2.0),
+            Boost.numeric_property("likes", modifier=Boost.Modifier.LOG1P, weight=1.0),
+        ],
         weight=0.4,
         depth=200,  # rescore the top 200 vector matches
     ),
@@ -222,8 +224,10 @@
     limit=5,
     # highlight-start
     boost=Boost.blend(
-        Boost.filter(Filter.by_property("category").equal("research"), weight=1.0),
-        Boost.filter(Filter.by_property("draft").equal(True), weight=-2.0),
+        [
+            Boost.filter(Filter.by_property("category").equal("research"), weight=1.0),
+            Boost.filter(Filter.by_property("draft").equal(True), weight=-2.0),
+        ],
         weight=0.3,
     ),
     # highlight-end
diff --git a/docs/weaviate/search/boost.md b/docs/weaviate/search/boost.md
index 7bc9ecd7..7fe9c60c 100644
--- a/docs/weaviate/search/boost.md
+++ b/docs/weaviate/search/boost.md
@@ -16,7 +16,7 @@ import BoostPreview from '/_includes/feature-notes/boost.mdx';
 
 **Boost** soft-ranks search results — promote or demote matching documents without removing them from the result set. Matching documents move up. Non-matching documents stay in the results but rank lower.
 
-Apply boost to vector, hybrid, BM25, near-text, near-vector, near-object, and aggregate queries.
+Apply boost to vector, hybrid, BM25, near-text, near-vector, near-object, near-image, and near-media queries.
 
 ## How it works