Perf: skip needless dedup re-cluster + interlock word-search grids

Two things found while chasing the recurring ~15min slowness:

- dedup.py: cluster_duplicates re-ran an O(n²) cosine pass over ALL ~3.7k
  articles and rewrote duplicate_of for every one of them EVERY cycle — even
  when nothing new arrived (embedded=0) — ~53s CPU + a large WAL commit that
  starved live API reads (/api/brief 2-7s). Now skip the re-cluster entirely
  when nothing new was embedded (clusters can't have changed). Verified: cycle
  drops from ~53s to ~1s and /api/brief stays at 20ms through a cycle, vs 2-7s
  before. (A real new article still triggers a full re-cluster.)

- games.py _build_grid: word placement took the first random valid spot, so
  words rarely crossed. Now gather valid placements and PREFER ones that cross
  an already-placed word (shared matching letter), falling back to any valid
  spot — so the grid interlocks like a real word search. Every word still
  placed (tests green). NOTE: changes today's grid layouts, so an in-progress
  word search resets once.

Also added a systemd drop-in (Nice=19/CPUWeight=20/IOWeight=10/ionice-idle) to
deprioritize the batch cycle — minor, the dedup skip is the real fix.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

goodnews/dedup.py

@@ -166,6 +166,16 @@ def dedup(
     embed_limit: int | None = None,
 ) -> dict:
     embedded = ensure_embeddings(conn, client, limit=embed_limit)
+    if embedded == 0:
+        # Nothing new entered the corpus → the clusters and duplicate_of links are
+        # unchanged, so skip the full re-cluster. It was re-running an O(n²) cosine
+        # pass over EVERY article and rewriting duplicate_of for all ~3.7k of them
+        # every cycle (~53s + a large WAL commit), which starved live API reads
+        # (/api/brief 2-7s). Most cycles find no new articles, so this makes the
+        # cycle near-instant and keeps reads fast. A real new article re-runs it.
+        dups = conn.execute("SELECT COUNT(*) FROM articles WHERE duplicate_of IS NOT NULL").fetchone()[0]
+        return {"embedded": 0, "articles": 0, "clusters": 0, "duplicate_clusters": 0,
+                "duplicates": dups, "skipped": True}
     stats = cluster_duplicates(conn, threshold=threshold, window_days=window_days)
     stats["embedded"] = embedded
     return stats