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  • jonny (nonvenomous)jonny@neuromatch.social
    Oct 21, 2026, 12:04 PM

    the standing rule now is that for every bug i report to the bug bounty program i am withholding a bug of equal magnitude until meta decides to pay me a dime, so that in case they do not pay me then i have something to sell still.

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Replies

  • jonny (nonvenomous)jonny@neuromatch.social
    Oct 21, 2026, 12:09 PM

    my exit criteria for this is now "meta either pays me and i go and get my pilot's license and forget about computers or else they stop making new bugs that boggle the mind" and we all know that one of these outcomes is possible in this universe while the other is not.

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  • jonny (nonvenomous)jonny@neuromatch.social
    Oct 21, 2026, 12:24 PM

    one finding that i don't have the patience to chase down is that
    a) i am 95% sure that the network egress threat classifier has an LLM in the loop that evaluates the requested URL and other contextual metadata as part of the decision, and
    b) i am 25% sure that you can cause the classifier to itself receive a classifier refusal by giving it urls that look like prompt injections or biosafety triggers, and rather than either refusing the connection or opening an approval card, it causes a TLS failure.

    i have a ~60k/1 week sample confirming (a), (b) is flaky and trying to measure it locks up the instance

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  • jonny (nonvenomous)jonny@neuromatch.social
    Oct 21, 2026, 11:52 PM

    Q3 Earnings: yeah muse is ridiculously expensive to run, but all we have to do to fix that is make it 10x cheaper. By.. Running the models at night. Idk we're going to ask muse how to make muse profitable.

    In the near term, Muse is very compute intensive to run, but we expect to improve its cost efficiency by more than 10x in the next year, which will enable it to be profitable once the revenue lines are at scale. If compute efficiency continues improving quickly for another few years, which we think is highly likely, then Muse will be highly profitable over multiple years.
    Ross Sandler (Analyst) Great. Mark, you touched on this earlier, but could you elaborate a little bit on maybe some high-level metrics around the per-user cost structure of Muse today and then how you kind of see that cost curve coming down as you go forward, and what are some of the things you’re doing to bring that cost curve down?
    Susan Li (Chief Financial Officer) Thank you, I’m happy to answer that. And Mark, you should add anything that you feel like I’m missing. You know, so we don’t have specific numbers to share around the cost structure of Muse today. But of course it is, as Mark said, pretty compute-expensive. And making sure that we are focused on compute efficiency is going to be one of the key vectors towards being able to both scale Muse and grow it into the business that we want. I think there are a couple dimensions of achieving that efficiency. I think some of the nearer-term pieces include things like leveraging off-peak or spare computing capacity to run some sets of jobs, using smaller models. There’s a lot of work to be done across different inference efficiency initiatives, all in service effectively of higher GPU utilization, and other product efficiency work we can do also.
    And I think over the longer term, I think this is a place where we expect that we’re going to be able to, over time as LLMs scale, get more tasks done with fewer tokens and also rely on AI models themselves to help us improve the efficiency of the software that we use to run the model. I think it’s a place where we both feel like there is a lot more work on our efficiency roadmap that is going to help us bend the curve here. And we also expect that the sort of arc of how the AI-driven technological advances are going to apply here will also help us achieve larger savings over time.
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