Keep the vectors beside the atoms instead of rebuilding them per query

The dense leg used to be a pair of files next to the database: a full .npy
re-encoded from every atom, re-normalized on every query, and stale the moment
anything was ingested. It is now a vec0 table inside the same SQLite file,
written in the same transaction as the atom it describes, so a memory is
queryable the instant it is remembered.

- vector_index.py maintains the table; the writer inserts, the reader never
  rebuilds. A repair scan (ely-eye build-dense-index) catches what a crash hid,
  and it is not on the search path.
- Ranking stays in the engine; every boundary (closed, retired, not yet true,
  another holder's private memory, the mounted cartridge) is checked once against
  the atoms table afterwards. A visibility subquery inside the KNN cost 48.9s on
  153,857 vectors; the same check over 200 candidates costs 0.2ms. When the
  boundaries reject enough candidates the ask widens once, never twice.
- The Go mirror and the web panel carry the new status fields.
- bench/dense_scale.py and bench/quant_probe.py measure the leg at corpus size;
  bench/scale_check.py now works on a copy so the frozen baseline stays frozen.

Measured on the frozen 165,973-atom corpus. bench/reports/DENSE.md, QUANT.md and
SCALE.md carry the numbers and the machine they were taken on.

Gates: 285 pytest, ruff clean, pyright 0 on the touched files, go build/vet/test
and gofmt clean, frontend build and eslint clean. mypy is 25 findings against the
parent's 24: the new one is "Skipping analyzing sqlite_vec: missing library stubs",
the same class as the two existing qwen_vl_utils lines, and the repo keeps no
type: ignore anywhere.

backend/src/ely_eye/cli_senses.py

@@ -18,7 +18,7 @@ from .cli_common import json_dump
 from .config import Settings
 from .db import Database
 from .describe import DEFERRED, VisionDescriber
-from .embeddings import EmbeddingService
+from .vector_index import VectorIndex
 from .visual_index import VisualIndex
 
 
@@ -73,7 +73,5 @@ def register(app: typer.Typer, settings: Callable[[], Settings]) -> None:
 
     @app.command("build-dense-index")
     def build_dense_index() -> None:
-        """Encode every atom into the dense index the text retriever falls back to."""
-        index = EmbeddingService(settings()).build_index()
-        dimensions = int(index.vectors.shape[1]) if index.vectors.size else 0
-        typer.echo(json_dump({"atoms": len(index.atom_ids), "dimensions": dimensions}))
+        """Encode the memories the dense index is missing; its vectors live beside the atoms."""
+        typer.echo(json_dump(VectorIndex(settings()).sync()))