feat(titles): normalize video titles for display (feed, search, downloads)

Noisy YouTube titles are cleaned for display + storage, raw kept in videos.original_title:
- app/titles.normalize_title: strip emoji/symbols (keep accents), remove trailing SEO hashtag
  clusters (keep numeric #3 episode markers), context-aware de-shout (mostly-ALL-CAPS titles ->
  Title Case with an acronym whitelist + function-word lowercasing; otherwise only long all-caps
  words), collapse repeated punctuation
- applied at enrichment (sync/videos.py) and in the download worker (ad-hoc yt-dlp titles);
  catalog downloads inherit the normalized title automatically
- migration 0039: add original_title, preserve raw, rewrite title (generated search_vector
  regenerates); reversible via original_title

Backfill on localdev: 122115/273417 titles normalized in ~2 min. Verified in the feed + on
real messy samples (emoji/de-shout/hashtags), accents + acronyms (PS5/AI/USA/PC) preserved.

backend/alembic/versions/0039_video_title_normalize.py

@@ -0,0 +1,49 @@
+"""normalize video titles for display; keep the raw one in original_title
+
+Revision ID: 0039_title_normalize
+Revises: 0038_asset_gc_notified
+Create Date: 2026-07-03
+
+Adds videos.original_title (the raw YouTube title) and rewrites videos.title to a normalized,
+display-friendly form (emoji stripped, ALL-CAPS de-shouted, trailing SEO hashtags removed —
+see app.titles). Reversible: original_title preserves the source, and title can be re-derived.
+The generated search_vector column regenerates automatically as each title is updated.
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+from app.titles import normalize_title
+
+revision: str = "0039_title_normalize"
+down_revision: Union[str, None] = "0038_asset_gc_notified"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+_BATCH = 2000
+
+
+def upgrade() -> None:
+    op.add_column("videos", sa.Column("original_title", sa.Text(), nullable=True))
+    conn = op.get_bind()
+    # Preserve the raw title first (fast, set-based).
+    conn.execute(sa.text("UPDATE videos SET original_title = title WHERE title IS NOT NULL"))
+    # Then rewrite title to the normalized form, batched (each update regenerates its FTS vector).
+    rows = conn.execute(
+        sa.text("SELECT id, original_title FROM videos WHERE original_title IS NOT NULL")
+    ).fetchall()
+    changes = []
+    for vid, raw in rows:
+        norm = normalize_title(raw)
+        if norm != raw:
+            changes.append({"i": vid, "t": norm})
+    stmt = sa.text("UPDATE videos SET title = :t WHERE id = :i")
+    for start in range(0, len(changes), _BATCH):
+        conn.execute(stmt, changes[start : start + _BATCH])
+
+
+def downgrade() -> None:
+    conn = op.get_bind()
+    conn.execute(sa.text("UPDATE videos SET title = original_title WHERE original_title IS NOT NULL"))
+    op.drop_column("videos", "original_title")