Fetch a second copy of an episode to subtract from the first

Subtraction measures itself at 92-104% against the length gap and finds every
pod, but it needs a publisher's transcript, and only 18 of 336 mirrored episodes
have a usable one. So the best detector in the project covers about five per
cent of what Peter listens to. This is Peter's idea for the other ninety-five.

Advertising is inserted per request, which this project measured the hard way:
the same Radiolab episode came back 7:29 of advertising on one fetch and 5:34 on
another. So two fetches of an episode agree on the programme and differ on the
advertising, and subtracting one from the other says where the advertising in
the other one is. Inserted needs no change at all -- it already takes two cue
lists and reports what the second has that the first does not.

Confirmed before building the pass: a second fetch of Tangle's "The corruption
is still the story" came back at 986 seconds against the mirrored copy's 1056,
with both declaring 690. Seventy seconds of different advertising, from a
request made minutes apart.

What it cannot see is a campaign both copies happened to carry. That is the
exact opposite blind spot to the audio scan, whose entire method is finding what
a batch of episodes shares -- so between them the two cover each other, which is
worth more than either being better.

The second copy's audio is deleted the moment it is recognised, and it is
fetched into its own directory under its own id, so nothing can mistake it for
the bytes the phone plays even for the moment it exists. Only episodes whose
served length exceeds what the feed declares are fetched at all: without a gap
nothing was inserted, and the second copy would be the same recording.

Bounded to twenty minutes a cycle, abandoned after three failures in a row with
the attempts given back, and ordered newest first -- the same shape as
recognition, for the same reasons.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_018AwnALZL6bD4VBtATDVv3B

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