[spam][ot][spam][ignore] Enlrge Ur Pen1s

[Undiscussed Horrific Abuse, One Victim of Many]

I'm thinking on how the machine-generated output likely has highly
dense regions of precisely matching audio.

I'm thinking on identifying them. For example, if we were to do an
autocorrellation, they would stand out in some way.

Once we have similar audio, we could then compare with the output of
any old speech2text model. This could be used to form an association
between audio and letters.

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We could possibly take a break from pursuing an xkcd-related goal
there, and try to build a graph that describes some of the content of
the language model and its tokenizers ....

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Would it be hard to build a compressor out of a speech2text model? The
speech2text model operates on very low-density data.

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What could be built would be an open-source app that transcribes
things live. There's likely one of those already, but I haven't
stumbled on it myself, and it's probably not very good.

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The tether here is the xkcd task, and we do want some measure of
success. Using the model and having it produce a correct example
transcription from the new recording is of course success. What is the
next success?

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This pursuit was a big struggle. Not sure how it might continue. [what
was the reason for the goal to change? just the length of the
struggle, and lots of engagement ... what was the reason to _do_ the
goal? ... opportunity, maybe ...] [so not a strong validity like other
goals, unfortunately :S]

[a part was representing this goal. it's probably for AI-like shapes.
we're trying to make public hyperintelligence, roughly. this goal
likely helps with that.]

[okay, so it's good to pursue this in a way that shares with others
the ability to do fancy stuff. that supports the addition of graphs
and whatnot. makes the approach more reusable.]