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/lib/hackernews.py

@@ -12,6 +12,7 @@ from concurrent.futures import ThreadPoolExecutor, as_completed
 from typing import Any, Dict, List, Optional
 
 from . import http
+from .relevance import token_overlap_relevance
 
 ALGOLIA_SEARCH_URL = "https://hn.algolia.com/api/v1/search"
 ALGOLIA_SEARCH_BY_DATE_URL = "https://hn.algolia.com/api/v1/search_by_date"
@@ -111,9 +112,13 @@ def search_hackernews(
     return response
 
 
-def parse_hackernews_response(response: Dict[str, Any]) -> List[Dict[str, Any]]:
+def parse_hackernews_response(response: Dict[str, Any], query: str = "") -> List[Dict[str, Any]]:
     """Parse Algolia response into normalized item dicts.
 
+    Args:
+        response: Algolia search response
+        query: Original search query for token-overlap relevance scoring
+
     Returns:
         List of item dicts ready for normalization.
     """
@@ -134,11 +139,14 @@ def parse_hackernews_response(response: Dict[str, Any]) -> List[Dict[str, Any]]:
         article_url = hit.get("url") or ""
         hn_url = f"https://news.ycombinator.com/item?id={object_id}"
 
-        # Relevance: Algolia rank position gives a base, engagement boosts it
-        # Position 0 = most relevant from Algolia
+        # Relevance: blend Algolia rank with token-overlap content matching
         rank_score = max(0.3, 1.0 - (i * 0.02))  # 1.0 -> 0.3 over 35 items
         engagement_boost = min(0.2, math.log1p(points) / 40)
-        relevance = min(1.0, rank_score * 0.7 + engagement_boost + 0.1)
+        if query:
+            content_score = token_overlap_relevance(query, hit.get("title", ""))
+            relevance = min(1.0, 0.6 * rank_score + 0.4 * content_score + engagement_boost)
+        else:
+            relevance = min(1.0, rank_score * 0.7 + engagement_boost + 0.1)
 
         items.append({
             "object_id": object_id,