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Cake day: August 26th, 2024

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  • A very strange story about Bill Gates’ daughter’s startup troubles: https://futurism.com/future-society/bill-gates-daughter-traitor-startup

    It honestly doesn’t sound any more shady than any of the bullshit her dad got up to in the 90s (which is to say, it’s still pretty damned shady!), but this Futurism blog seems to be trying to push the story into some sort of bizarre celebrity-gossip soap opera:

    While the story has been well hashed out by now, Bloomberg‘s sourcing is noteworthy for what it implies: the leaked Slack messages strongly suggest that someone inside Phia is hell-bent on taking Gates — and quite probably the entire company — down.

    Anonymous internal sourcing seems like a typical feature of these exposés, so framing it like this struck me as a laughably absurd overreach. Plus the site has 3 or 4 different tiny articles on this same topic, making me wonder if it’s one of those “any exposure is good exposure” sorts of things where Gates Jr.'s PR team is using the outlet as a cutout to raise her profile.

    Bonus laugh line from Gates Jr. herself:

    “Our target consumer is a young woman who’s hustling,” the recent Stanford graduate told Fortune. “She shops like a genius, but she doesn’t want to waste her time doing it.”

    What does it even mean to shop like a genius? I guess Erdos probably had his amphetamine plug’s number well-memorized…






  • I’m probably going to stumble over some of the terminology here, but I think it might be possible to describe what @BioMan@awful.systems is proposing as a consequence of LLMs ultimately being lossy compression systems. Inference is a function over a lossily-compressed data set, and “chain-of-thought reasoning” and “agents” may sound sophisticated, but are simply applying containerization and DevOps tools to VM images of the inference application in an attempt to get around hard memory limits on the context window for inference. “Chain-of-thought” attempts this in a serial fashion, passing results from one instance to the next, while “agents” implement this hierarchically and recursively (and woe to the poor bastards who wished that mess upon themselves). But in both cases, the “finalization” phase is necessarily a further lossy compression step, attempting to compress a result from the inference process to a fresh instance of the inference application, so as not to immediately blow out the new instance’s context window.

    Given this necessity, it comes to seem somewhat intuitive that there may be “strange attractors” in the higher-dimensional vector space that is the compressed data set which surround code that creates and maintains message passing channels. No matter what you’re doing with an “agentic” process, the inherent necessity of context cramdown & message passing means that querying into the space where such code examples lie is a hidden requisite of running the damned things, thus turning such functionality into the sort of selfish elements that BioMan is talking about.

    The problem in investigating and concretely describing this phenomenon is nailing down the exact functions and processes that make it happen. Given the godawful messes in the Claude frontend codebase that @jonny@neuromatch.social has been documenting, I’d be surprised if there’s one developer in a hundred at Anthropic or OpenAI who can describe in detail how the intentionally-developed context-passing code for their “agents” works.















  • Yeah, I began losing interest in Greer as it became clear that he was perfectly happy squatting in the middle of the red-brown alliance during the Trump era. His critiques of industrialism and unquestioning belief in technological progress broadly align with what we discuss here, but he will always coddle MAHA types and tale a shrugging “well, what can ya do?” attitude towards people like Trump, as it fits his preference for cyclical theories of civilization.

    I noticed a couple months ago that he actually managed to dig Nick Land out of whatever tweaker den that guy’s been hiding in for a podcast, which says a lot about what he’s willing to indulge these days.