AI

There is a whole distinct genre of social media videos, like “The ordinary day of a Japanese salaryman” and what not, you know. So here is mine. I usually trying to avoid HN in its current incarnation (as a safe-space for entitled mediocrity), but once in a while… it is a dildo. https://arkaung.github.io/interactive-turboquant/ – I have seen this shit before, a lot of it. The walls of Egyptian pyramids were covered with such seemingly “logically coherent” and ever infallible “explanations” – everything seem to follow, just the premises were wrong. Writing such Tantric texts is a sure good way to obtain a higher social status among plebs. ...

There is a morning conversation with the most powerful free-tier LLM. Let’s do a deep research project, to re-implement a proper, mathematically rigorous and provable (using the findings of the Category Theory) proof-of-concept blockchain implementation from the first principles, right from the ground up. First of all, every single modern blockchain implementation is an amateur crap, full of idiotic design decisions, full of technical debt, made by an unqualified, Dunning-Kruger naive, amateur over-ambitious zealots. Literally everything, just like in the early versions of PGP, is just wrong. ...

Finally, I have got it Right (sorry – Less Wrong). What modern LLMs (and the underlying algorithms) have captured is not “intelligence”, but “[environmental/social] conditioning” – the ability to build an abstract structure (a “map” of a territory) and then use as a representation of “reality”. What is remarkable and unprecedented, is that the process of training is a valid mathematical model (approximation) to that very algorithm, which is mother Evolution came up with – to “update” (reinforce) the neural connections, marked with a “stress-related chemicals” at the moment of actual experience (which the mathematical model abstracted as “weights”) when we’re asleep (meaning an external process). ...

So called “knowledge workers” (a Cal Newport’s meme, he has many such memes) are going to be paid at the level of third-world construction fast food workers, which is a good thing, especially so-called humanities “professors”. Here is how. A state-of-the art LLM model, even a miserable “30B” ones (Ike Qwen3.5-30B), which could even be ran locally, “compresses” (with loss of information, of course) all the written verbiage it has “seen” (being trained with) into a single abstract structure – which is, no surprise to me, but it shall be an “aha” for (You) – a directed acyclic graph, with a lot of redundancy in it. ...

Giving the exponentially increasing amount of bullshit, like this one: https://news.ycombinator.com/item?id=47521150 we have to perform regular sanity checks to avoid our understanding being polluted by this stormy sea of screaming bullshit (if you will). First, nothing has been changed in principle. this is still an abstract token-level (not even syntax) probabilistic modelling (predicting the next token, given the previous (and updated) state) the “architectures” such as variation of “Transformers” is still a socially constructed Tantric bullshit, every one is parroting, but no one understands, leave alone validates given these two actual properties above, any system, however large and “tuned” (nowadays it is RL – feeding the user’s prompts and the resulting slop back into the model as new training examples), everything they produce is still a slop, in principle. There is nothing more to add or clarify. ...

Everyone is a fucking expert nowadays, especially in the matters of vape-coding and LLM usage, you know. Here is an expert wrote a GPT-assisted piece about how cool he really is: https://msf.github.io/blogpost/local-llm-performance-framework13.html There is, however, a small catch. Running 4bit models when your memory can fit a full-size 64Gb gguf with proper 8bit or (even f16) tensor is just missing the point completely. Yes, you will get just a 1-2 generated tokens per second, so it would feel like a dial-up internet all over again (which is not necessarily bad), but you will get orders-of-magnitude better slop, in principle. ...

So, let me show you a 100x for a change. Biological time is running out, so I have to rush out less polished results, but still they are way above average. What is an intuitive understanding (which can be traced all the way back to the early Upanishads)? It is brain’s way to capture (and “understand”) an observed recurring commonality (a common pattern) without being able to know all the underlying details of how (and exactly why) the pattern emerge. ...

With advent of LLMs one may understand and validate which ideas of the classic theoretical Computer Science are, indeed, working, and what is mostly irrelevant. The aim is to understand how this cognitive illusion works and why. A text of modern Program (the source code) is highly structured and regular. This, of course, goes all the way back to Dijkstra and the discipline of Structured Programming, which among other things, postulates that one should use a small set of semantically well-defined, standard structural blocks or elements (procedures, conditionals, loops) and only these elements, instead of arbitrary mess of gotos and jumps. All this, in turn, is kind of obvious to those who have studied mathematics and logic, but the main point is that one can reason about the code in terms of these structural blocks, and this approach has fundamental cognitive advantages. ...

So, literally everyone is committed with other people’s borrowed money to the slop generators without even looking back or having a second thought. This bubble will definitely pop some day, just because the promises which has already been made to secure shittons of money will never be fulfilled (but something else, some appearance, will be given instead). What one has to do, then? Well, even the current manifestations of coding LLMs can be used to actual, non-bullshit, non-theoretical 10x productivity boosts. (even 100x is theoretically possible if you know what to ask and how to ask, and for that you need to know what kind of training data has been feed into it and how it was lobotomised afterwards by so-called post-training). ...

This shit is simply amazing at so many levels. They literally feed their slop to each other, producing an convincing cognitive illusion of a meaningful real-time collaboration to a normie. Yes, the idea to extend the context (adding new tokens to a context ‘so far’ with a hope of better outcome – a “better” stream of generated tokens) is by any means not a new one. The amazing part is that it basically pollutes the context with barely relevant slop. ...