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

[Undiscussed Horrific Abuse, One Victim of Many]

[ok maybe we can do the idea.
 right now we want the code to make symbols instead of words.
 thinking on process of recursive confidence]

[1 to start you'll need a way to discern some basic improvement, such
as something being more right if it has a given change, or something
being wrong in a certain way
 2a later i suppose we'll need a way of providing for changes to
resonate off that information: for example transcribing it as an
automated metric.
 2b the system needs some way of discerning the attribute. whether it
is relevent, or what it might be, or testing if it is there. some way
to engage it repeatedly.]

[so how does the system tell anything about symbols? is there any way
to know the symbols are there?] [there is almost certainly a logits
pattern that indicates the symbols. they're near different other
words. right in the output.] [we could look in model internals too,
but it's expected to be about the same since the input data is
expected to be so consistent.] [we could check the input data too.]

[so we could train the model on that.] [theoretically but training
takes data. we've been conditioned to think about training, trying to
connect with mainstream work. instead, we want an algorithmic pattern
that helps, or just a little data.]

[okay um
- the letters probably have patterns that indicate them
- we could resonate those patterns by identifying some data
is this reasonable] [or is the resonance too vague?] [it could have
gone other ways, it's a hybrid]

[ok ummm it sounds like it would be quite helpful for it to identify the onset.
 this is resonance, but for it to be _confidence_ resonance we'd need
to be more precise around how it would work. what the confidence would
be. and we'd want multiple channels.]

[ok so that actually looks like a good approach because
autocorrelation and similar things are very general and can apply to a
lot of data. we could try to find patterns that let it learn to
predict which areas are code and which are text. the patterns would be
found from feature-resonance between areas. but we want to formalise
out the idea of feature resonance, to produce a confidence metric.]

[ok so we would give it some kind of heuristic axiom around patterns
in general, that would have some hardcoded confidence.]

[now we have hard or inhibited part around the patterns inside [looks
inhibited]]

[let's do one part at a time. any pseudo-draft of 'feature resonance'?]
   [for example, you could train a model to simply predict what data
comes next in the stream. [oops two branches here]]
  [we were going to consider feature resonance but then realised that
such a model would already be trained to recognise the change, because
it would have to learn it to do the prediction appropriately. there
are different token frequencies in the two sets]
[ok such a model would indeed produce confidence. what was the earlier
idea though?]