☆ Yσɠƚԋσʂ ☆

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Cake day: January 18th, 2020

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  • I’d argue that full on central planning was the wrong model. I can highly recommend reading Thinking in Systems to understand the problem. But the gist is that any large system will have noise, as information travels through it. So, when you do central planning the way USSR was, it becomes very difficult to balance things. You make a plan, the directives travel down to the factories and farms where production happens, there’s delay in the information arriving, mistakes in transcription, and so on. Then the same thing happens on the way back up, so the picture at the top is divergent from the situation on the ground. And that leads to things like overcorrections, and plans based on false data, so the system becomes unbalanced over time. And that’s what we saw happen in USSR.

    Now, that doesn’t mean that the idea of central planning itself is wrong. The problem is with micromanagement which just doesn’t work. And this is the problem China figure out by using markets as allocators within the centrally planned system. The planners still set five year plans, they still control the commanding heights of the economy, and give overall direction on what types of things should be produced, but the details are left to market mechanisms to work out dynamically at the appropriate level. And this type of a hybrid system is proving to be highly effective.


  • Yes, and that’s literally where power comes in. The more powerful the laser, the further it stays coherent. Again, these are literally being used in combat right now. And they’re not intercepting missiles, they’re intercepting slow flying drones. What I was saying is that it’s much cheaper to power a laser than to make an interceptor.

    Americans also can’t figure out how to make hypersonics, but Russia, China, Iran, and DPRK can. Just because the reich can’t do something, doesn’t mean it’s not possible.






  • So the big difference between Paperclip and Osoaviakhim comes down to the US welcoming German scientists to the team versus Soviet get in the van approach. In the US, Operation Paperclip was basically a recruitment drive where guys like Wernher von Braun and his crew got sweet deals to come work for the military and later NASA, and over time they ended up as big shots with real political sway. For example, von Braun famously became the faces of the space race. The US government scrubbed their Nazi pasts and they were treated like VIPs, they got to live normal lives and integrated into American society and power structures.

    On the flip side, the Soviet Union’s Operation Osoaviakhim was more like a forced relocation program where over 2,000 German specialists and their families were rounded up in the middle of the night and shipped off to the USSR. They weren’t exactly thrown into Gulag labor camps, but they were effectively put in isolation, like on the island of Gorodomlya, and kept separate from the general population under strict supervision to work on Soviet rocket and tech projects in what was basically a gilded cage. While the Americans were rolling out the red carpet, the Soviets were treating these guys like captive assets rather than future citizens.





  • I do open source software and have been doing it for many years. I understand open source licenses very well. DeepSeek assigns all rights of the outputs to you, which makes you the legal entity with the ownership of the code:

    4.2 Subject to applicable law and our Terms, you have the following rights regarding the Inputs and Outputs of the Services: (1) You retain any rights, title, and interests—if any—in the Inputs you submit; (2) We assign any rights, title, and interests—if any—in the Outputs of the Services to you.

    Meanwhile, your confusion when you talk about stealing the code stems from your lack of understanding of how LLMs actually work. People tend to confuse the output looking similar to training data with the mechanism being simple retrieval. There is no database or a lookup table, and there is no copy of any text they were trained on tucked away somewhere inside the weights. What they have is a compressed statistical model of the patterns that exist across trillions of tokens the text was broken down into. When you ask an LLM a question it does not go find the relevant code and regurgitate it. What actually happens is that it generates text token by token based on the probability distribution it learned during training. It’s more akin to the way state machines like Markov chains work.

    If anything, the way LLMs work is actually much closer to how humans improvise or combine ideas than to how a search engine or database works. When you write a sentence, you don’t pull up a specific piece of code you memorized from some repository and copy it. You’ve absorbed the patterns from countless examples of language over your lifetime and internalized them. Coding is a process of generating novel combinations based on those patterns.

    Similarly, the weights of a model encode statistical regularities about word co occurrence, syntactic structures, semantic relationships, and higher level conceptual patterns. When it generates text it is combining those patterns in ways that were never present in the training data. That’s how LLMs produce entirely novel sentences that have never been written before, and why they can be prompted to combine ideas from different domains in creative ways.

    LLMs can reproduce training data verbatim in some cases, but this happens because of overfitting on rare or repeated sequences. When a specific string appears many times in the training data like a famous quote or a common error message the model learns to assign an extremely high probability to each token in that exact sequence. So when it generates it follows the most likely path to reproduce the original text. It is similar to how a human can memorize a song or a poem by hearing it many times but that does not mean our general language ability works by pulling up stored copies from memory.

    We’re talking about the worker, not the bourgeoisie. Their labour was exploited to benefit a corporate entity. And you’re consuming stolen proceeds from the bourgeoisie and defending their contributions.

    You can certainly make this argument when it comes to closed corporate models. However, it completely falls apart when applied to open models that aren’t owned by any corporation. Open models are available for everyone to use, and you running Qwen on your machine isn’t benefiting any corporate entity in the slightest. And the whole stolen goods angle is basically the RIAA anti-piracy argument, you should be deeply ashamed of yourself for parroting it.

    You’re also confusing tools with the actual capitalist system here which makes your entire argument deeply incoherent. It is simply not possible to avoid using anything produced by exploited labour while living in a capitalist society. And nobody is defending capitalism here.

    I would urge you to spend a bit of time trying to actually understand the subject you’re attempting to discuss here to form a coherent position on it.