Sync repo to deployed state: SEO recovery, Publishing Desk, Play games, emoji picker

The deploy pipeline runs from the working tree, so a wave of shipped features
had never been committed. This snapshots git to what's actually running.

SEO impression recovery (live + verified):
- Duplicate /a/{id} now 301-redirect to their canonical twin instead of 404
  (a hard 404 silently dropped already-indexed URLs and tanked impressions).
- Dedup representative selection reworked: accepted/serveable -> established
  rep (URL stability) -> quality score, so an accepted page never retires to a
  rejected rep and an indexed canonical doesn't churn when a newer twin arrives.
- HEAD /a/{id} returns the same status as GET (api_route GET+HEAD) instead of
  falling through to the static mount and 404ing.
- `dedup --force-recluster`: cycle-locked, model-free re-cluster to re-apply the
  policy to the existing corpus (shared cycle_lock context manager).
- CLI honors GOODNEWS_DB for its default --db (was silently ignored).

Publishing Desk (admin tool to post highlights to X via Web Intents):
- publishing.py queue/rank/handle-resolution; admin UI; full searchable emoji
  picker (bundled data, no CDN) for the blurb editor.

Play games + site:
- Bloom (word-wheel), Memory Match, daily ritual set, Zen Den (dev-gated).
- English-only language gate; source prospecting; paywall + dedup hardening.

Tests: full suite green (349). Ignores tightened (node_modules, data/*.db).

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

goodnews/dedup.py

@@ -102,7 +102,8 @@ def cluster_duplicates(
             (COALESCE(s.constructive_score,0) + COALESCE(s.agency_score,0)
              + COALESCE(s.human_benefit_score,0) + src.trust_score
              - COALESCE(s.cortisol_score,0) - COALESCE(s.ragebait_score,0)
-             - COALESCE(s.pr_risk_score,0)) AS rank_score
+             - COALESCE(s.pr_risk_score,0)) AS rank_score,
+            COALESCE(s.accepted, 0) AS accepted
         FROM articles a
         JOIN article_embeddings e ON e.article_id = a.id
         JOIN sources src ON src.id = a.source_id
@@ -114,7 +115,8 @@ def cluster_duplicates(
     items = []
     for r in rows:
         vec = _unit(array("f", r["vector"]).tolist())
-        items.append({"id": r["id"], "ord": _day_ordinal(r["dt"]), "vec": vec, "score": r["rank_score"]})
+        items.append({"id": r["id"], "ord": _day_ordinal(r["dt"]), "vec": vec,
+                      "score": r["rank_score"], "accepted": bool(r["accepted"])})
 
     clusters: list[dict] = []  # {anchor_vec, anchor_ord, members:[item]}
     for it in items:
@@ -130,6 +132,14 @@ def cluster_duplicates(
         if not placed:
             clusters.append({"anchor_vec": it["vec"], "anchor_ord": it["ord"], "members": [it]})
 
+    # Which articles are CURRENTLY a representative (something points at them)? Captured
+    # BEFORE we reset, so we can keep an established canonical stable across runs.
+    prior_reps = {
+        row[0] for row in conn.execute(
+            "SELECT DISTINCT duplicate_of FROM articles WHERE duplicate_of IS NOT NULL"
+        )
+    }
+
     # Reset prior decisions for everything we considered, then re-apply.
     considered = [it["id"] for it in items]
     conn.executemany(
@@ -142,7 +152,19 @@ def cluster_duplicates(
         if len(cl["members"]) < 2:
             continue
         dup_clusters += 1
-        rep = max(cl["members"], key=lambda m: (m["score"], -m["id"]))
+        # Representative priority (highest wins), in order:
+        #   1. accepted/serveable — an accepted page must never be retired to a REJECTED
+        #      rep (that page would 404 with nothing to redirect to).
+        #   2. established rep — if a member is already the cluster's canonical, keep it,
+        #      so an indexed URL doesn't churn when a newer twin arrives.
+        #   3. quality score — decides genuinely-new clusters.
+        #   4. -id — deterministic final tiebreak (older wins).
+        rep = max(cl["members"], key=lambda m: (
+            1 if m["accepted"] else 0,
+            1 if m["id"] in prior_reps else 0,
+            m["score"],
+            -m["id"],
+        ))
         for m in cl["members"]:
             if m["id"] != rep["id"]:
                 conn.execute(