Universal information states I.E a big eye is approximated by functions or algorithms such as k, complexity, cosmagarov or whatever complexity. So also, calculus calculus is an approximation of universal big eye as as a function. So these are all models that can be relied on to approximate the ineffability of big eye information since it’s a part of a. Since all of these things are subsets of information, they can’t describe it fully, but they can approximate it and different methods May approximate different things. So there’s we’re having kind of a an ensemble approach or can be helpful? Okay, so math is one approximation of of big eye universal information, but that might also be represented by computer code or by an analog dial. Let’s say that could approximate some information I’m trying to. This is unclear to me at the moment, but I’m trying to differentiate between the measurement collapse that results from I hat meaning observed values + what we can best use to approximate universal information. So actually we may actually have an intermediate information there too. Then we’ve got our models and our constructs okay and so numbers themselves are a constrict. So we’ve got Universal information and then we’ve got this. This intermediate layer of construx + this collection of of of things that we use to understand and organize and that’s where things like math and the number system itself would come in. Because otherwise, how do we represent then that observed data because that that itself could be represented by many different ways. We could represent it by you know notches on a stick or or stones or or you know grunts anything so so we’ve got now multiple layers of of information. Then we’ve got our constructs that approximate information. Then we’ve got the observed data that relies on the construx I.E that define what we’re talking about in these these conventions. Maybe we’ll call them construx or conventions like what numbers mean and then from our observed data then we’ve got these. What synthetic information, right? What’s your models and things? And now there’s where there’s a key distinction because observe data can now be reason logic is a kind of observed data right? And so we we shouldn’t just get too hung up on the number system as observed. Data or values in a spreadsheet is observed data because there are different ways of thinking about this. These constructs are what define it. So, for example the way that we have these we we have binary digital computers. That’s a construct within which we we operate and can also at each level we can. We introduce error so one big error at the construct or convention level is zero because now we get that cause of singularity so that that ripples down the down the food chain if you will. The I think the key is that Universal information isn’t immeasurable value and then so math and numbers become a framework to to explain it and things like you know what we call code or algorithm or a function like calculus. But it’s not the only one. So there may be other frameworks that can that can define this. For example, calculus you know the flaw there is you have to in order to understand the function you have to plug and chug values to get that integral okay so right there that leaves it open to the flaws of say zero. You know? How do you how you know? How do you go from zero? These are the these these these have ripple effects we’ve got at the top. The ineffability of the universe’s information. Big eye. This is the population that you can never directly measure if you want to look at statistics and then we’ve got our tools like statistics that try to wrap our heads around these constructs or these frameworks and like statistics itself. We we recognize that these are tools right? These are conventions or constructs like the number system like like mathematics adding numbers together. Okay then another layer of abstraction. These are all layers of abstraction away from the ineffability of of the population or the unknown population. Then that other layer of abstraction are observed data. So now we now we have these measurement instruments and we have these tools like logic and and things like that that we then used to construct synthetic information knowledge. Synthetic information is equivalent to knowledge.