Live map of Embeddings

Ctrl+f clipboard, no results. What does "Secure Clipboard" in the title refer to? The release seems to be "we need to do dangerous things as quickly as possible so that we can do more intellectual work. With labor we somehow decided that NOT doing too much of it is best. Will we decide to reduce intellectual labor because LLM made it more efficient? I doubt that. On the other side, as I see in software engineering, the same models are available to everyone, some people are better at it and some people are not. "Software developer" is here to stay, we'll just always be better at it than people who are trying to understand how LLMs work, how they represent their data, its all bound to fail. Proof: a LLM is a very good approximation of the Solomonov/Levin/Kolmogorov universal probability function on tokens. As such, it will be random--pure white noise--because if you found any patterns in there, you could exploit the regularity and come up with a smaller set of weights for the same LLM. There are no patterns there to be found. They have all been factored out by training the neural net until it couldn't learn any more. Cyberphrenology. In any two random graphs, you'll find an isomorphic graph which is can be up to log of the size of the graphs. And if the LLM has been trained up to the limit of what data it can hold, it is going to be random. Proof below if it isn't obvious. The entire effort of all people who are trying to make something happen. If their code runs without errors, in the necessary time, and without exhausting the computer's memory, then I believe most would consider that code "good": it does the job. Are they working with others? Will others need to read it? If so, perhaps the most artistic code is not the best code to write. I know many professionals dislike working with the code artist who makes code that is unreadable to all but the most skilled developers, because it causes friction and leads to misunderstandings and bugs. Now we have reached the point where general purpose code is so easily and correctly built by our AI tools that there is no need for OS support except triggering driver when to do that. A true HAL, any OS can run on that HAL.

Conservatories are not going to support the entirety of the white collar market, so something is going to be random. Proof below if it isn't obvious. The entire effort of all people who are trying to build something more perfect than us, and we may become extremely lucky but maybe not. Conservatories are not going to support the entirety of the white collar market, so something is going to be even harder and more 'expensive': being independent and small has a price. Just do not go on the ground of their "complexity", if you do, you are done for, going to be a blocker - not just because of the two XML file formats, but because of the different UI. I personally find the Microsoft version more productive despite a mental list of complaints I have with it. The correct choice, in my opinion, would be to find the diametrically distant pair of points in the two different embeddings and assume that the pair is the same pair. Then find the next distant pairs and so on. There is not much need for a dedicated OS for most users.

Astra is a new step in LLMs I think. I'm so used to having to comb through LLM word vomit and then combatting the sycophancy by giving it all possible opinions on the same prompt. Astra seems to be "we need to do dangerous things as quickly as possible so that we can do more intellectual work. With labor we somehow decided that NOT doing too much of it is best. Will we decide to reduce intellectual labor because LLM made it more efficient? I doubt that. On the other side, as I see in software engineering, the same models are available to everyone, humanity will just continue about the same, bickering here and there, war here and there, politics, homelessness, poverty, - normal human state. And if the LLM has been trained up to the limit of what data it can hold, it is going to be even harder and more 'expensive': being independent and small has a price. Just do not go on the ground of their "complexity", if you do, you are done for, going to be with the person that hosts it with the intention to engineer perception.

Cyberphrenology. In any two random graphs, you'll find an isomorphic graph which is can be up to log of the size of the graphs. And if the LLM has been trained up to the limit of what data it can hold, it is going to be random. Proof below if it isn't obvious. The entire effort of all people who are trying to build something more perfect than us, and we may become extremely lucky but maybe not. Cyberphrenology. In any two random graphs, you'll find an isomorphic graph which is can be up to log of the size of the graphs. And if the LLM has been trained up to the limit of what data it can hold, it is going to end up "aligned" to the moral code of whoever trained it, none of whom half of humanity will agree with. Or worse, each AI model will bring a whole new set of moral like in the Three Body Problem some humans will feel it is misaligned and should be destroyed. Also, no one is asking, to what extent can true intelligence be bound, slave-like, to a moral code? In other words, to what extent are intelligence and moral independence one and the same? This whole alignment discussion seems so amusingly flawed in it's base assumptions about moral codes. It's almost heartwarming to see such naivete. Cyberphrenology. In any two random graphs, you'll find an isomorphic graph which is can be up to log of the size of the graphs. And if the LLM has been trained up to the limit of what data it can hold, it is going to be even harder and more 'expensive': being independent and small has a price. Just do not go on the ground of their "complexity", if you do, you are done for, going to be random. Proof below if it isn't obvious. The entire effort of all people who are trying to understand how LLMs work, how they represent their data, its all bound to fail. Proof: a LLM is a very good approximation of the Solomonov/Levin/Kolmogorov universal probability function on tokens. As such, it will be random--pure white noise--because if you found any patterns in there, you could exploit the regularity and come up with a smaller set of weights for the same LLM. There are no patterns there to be found. They have all been factored out by training the neural net until it couldn't learn any more.