Replace hardcoded 0.7 relevance with computed token-overlap scores

- bird_x: parse_bird_response now accepts query param and computes
  token_overlap_relevance against tweet text
- reddit: _normalize_post computes relevance from query vs title+selftext
- hackernews: blends 60% Algolia rank + 40% token overlap + engagement

This makes the 45%-weight relevance factor in score.py actually
differentiate results instead of being a constant.

scripts/last30days.py

@@ -353,7 +353,7 @@ def _search_x(
             raw_response = {"error": str(e)}
             x_error = f"{type(e).__name__}: {e}"
 
-        x_items = bird_x.parse_bird_response(raw_response or {})
+        x_items = bird_x.parse_bird_response(raw_response or {}, query=topic)
 
         # Check for error in response (Bird returns list on success, dict on error)
         if raw_response and isinstance(raw_response, dict) and raw_response.get("error") and not x_error:
@@ -508,7 +508,7 @@ def _search_hackernews(
     except Exception as e:
         return [], f"{type(e).__name__}: {e}"
 
-    hn_items = hackernews.parse_hackernews_response(response)
+    hn_items = hackernews.parse_hackernews_response(response, query=topic)
 
     if response.get("error"):
         hn_error = response["error"]