PLN: "dotn we have a webapp for listening to seams in a set? you could use it for
me to confirm your labels. reusable tooling pattern."

It does — armada/ui/bounds.html, the Boundary Lab. So this builds no second page.
make_bounds.py generates the document that page already reads, which means the
confirmation loop is the one that already stores calls per gig in localStorage and
already exports boundaries-<gig>.json.

That direction is the point. The three-lens fusion is a machine opinion: 11 of 18
segments have two lenses behind them, 4 are honestly unresolved, and the two rows
that were WRONG were caught by PLN's memory rather than by any score. An analysis
pipeline whose output lands in a report has nowhere to be corrected.

The contract was read from BoundaryLab.tsx, not assumed, and two details would
have been wrong if guessed: marks[].t is in MASTER time (the page subtracts
clipStart itself), and `url` is handed to WaveformPlayer with dur = clipEnd -
clipStart, so it must point at a CLIP of that length rather than at the whole
recording. Mark keys are colour-coded by the page, so the segments where a lens
OVERRODE the embedding get `takeover` — brand magenta, which its own comment calls
"the one to try first" — because that is exactly where an ear call is worth most.

Clips plus a mount rather than the whole file, following audio-mounts.json's own
reasoning: it maps /audio/<prefix>/ for BOTH vite dev and armada/serve.py
deliberately, since "a UI that only works under npm run dev is a demo, not a
tool". A 40s clip also loads instantly on a phone over LAN, which is where this
gets used. Peaks are normalised per clip because these are 40s windows of a room
mic whose level wanders, and a waveform you cannot see is not a waveform.

Verified rather than asserted: /bounds.html 200, the document 200 at 190 KB, a
clip 200 at 641 KB, and a Range request returns 206 with exactly 1024 bytes — so
seeking works, which is the whole point of auditioning on a phone.