sigbottle

I've been toying around with nebula, self host your lighthouse on a cheap VPS (which even if it gets hacked, the most it can do is lie about traffic, not lie about node certs). Basically you're just paying for the punchthrough (you may even be able to do some duckdns shenanigans?) The main issue is that updating nodes is a pain in the butt, which is the main advantage of tailscale IMO - centralized control plane. Keeps it simple.

I was trying to do some kind of insane workflow where you as the operator have a privileged hardware yubikey that admins the nodes when you plug it in, and the point is the nodes would self-propogate the changes outwards. I immidiately ran into classic issues with distributed security in distributed systems (even with only one central operator trying to admin everything). Tailscale is also a bit more secure in its oauth layer - with this architecture, you need to set proper time limits for expiring so one node compromise doesn't get access to the whole fleet. Couldn't really come up with better ideas than forcing the operator to reuse some kind of hardware key (the idea trying to be avoiding maintaining a cloud control plane as much as possible)

Fun foray but creating such a system requires a lot of expertise that I unfortunately lack

Terminal state machine parsing is a very tricky thing, especially when you have people defining arbitrary protocols to interact with it. So having one implementation simplifies the development a lot. Of course this buys you into their ecosystem and whatever ghostty specifically supports etc.

A lot of other terminal emulators can expose similar reusable things (alacritty comes to mind) but libghostty is the most well supported one

The terminal ecosystem needs to change, and these terminals support a lot of cool features. Ghostty's pure reusable terminal state machine being a source of truth, Kitty graphics, Wezterm and its mux domains & lua config, lots of cool ideas there.

Having having an industry player like hashimoto's superlogical driving terminal features is the extreme version of that

But I love tinkering!

> A lot of Wittgensteins work, Heideggers work, and later philosophy of mind underlies current neuroscience.

Could you explain the specific neuroscience research programmes informed by these philosophers? I'm not an expert in neuroscience so I would not know. The only thing I know about neuroscience is like, the free-energy principle.

> You can't really investigate the nature of being like Heidegger was doing in a "scientific" way.

OK but I've acknowledged many philosophers and their contributions to general thought, even if they're not strictly "scientific". I was only following the conversation this way because neuroscience was brought up - normally I interpret Heidegger as responding to Western ontology and less through the phenomenonlogical/existential lens. Like, there's the phenomenological vehicle of criticism versus fully buying into it as a program to test cogsci or like any kind of test. I don't think Heidegger was merely saying "you guys are too abstract and heady and you should live in the moment", for example, he had specific claims he was making. Heidegger was definitely making specific claims about the world, otherwise you could dismiss him as art, which is not right, either. The issue is is that he's opaque :/

Yeah Hume is an example of someone who had the bias of the myth of the given, even though you can uphold him as someone who skeptical of notions of truth; associations, traditions, accidental regularities, type of guy

Heck even Hegel and Heidegger were orthogonal (I believe? I'm not sure if Heidegger responded either favorably or not to Hegel - I think he wasn't really responding to him, more to the Greeks and Kant) to each other. That's weird.

And Sellars can say he rejects the Myth of the Given but inferentialist is very much not a phenomenological theory. Accepting that "nouns" are not some representation relation, not "some thing we have yet to achieve but has an end goal / we refine it", etc. still doesn't get you to Heidegger I feel, either.

Like, there are so many positions circling around Heidegger, but only Heidegger has a distinctly Heideggerian feel. Maybe I'm overcomplicating it though. I think it's because he's distinctly making a claim about the human experience, whereas all the philosophers I've listed, you can list aspects of them that get away from that (even Kant lol). They've been "naturalized" in some sense. You can't naturalize Hediegger, but then I'm not sure why his theories are better than say, idk, science. You can diagnose the common prejudices of thought through a lot of other philosophers

Is that it? Wouldn't this be compatible with Hume's claims? And yet hume is still quite Cartesian in thinking (as everybody of around that era was)

> I think the real difference is how leaky the abstraction is. If you just need to understand language instead of compiler internal to write correct code, the compiler is non-leaky abstrction. If you rely on compiler to do auto SIMD optimization, and have to do hacks to trigger the optimization, compiler is leaky abstraction.

Autovectorization is not a programming model!

I forgot who put me on that article - I think it might've been Horace He - but that is a pretty big idea in idea space for me.

Is putting your reverse proxy in a VM not secure enough? What can break out of a sandboxed VMM?

I suppose there was a KVM exploit on twitter a while back...

To be fair, LLM's extend well past GOFAI and prove Dreyfus right. We were never going to get AI with formal methods. Literal big data slop is an orthogonal program and far more empiricist than probably the people being criticized here (if the people criticized here were GOFAIers).

Although I wouldn't say just yet that Heiddeger was validated. I don't even know what he's saying 90% of the time, honestly.

OK. The core distinction is this.

The philosophical issue I have can be decomposed into two parts:

1) What is the base ontology of what the neural nets thinks exist? 2) How do you recover, and begin to understand said ontology?

Modern researchers obviously want the goal of simplicity & composition, but to arrive at that, they build abstractions like transcoders and SAE's to get into their pet ontology (with the assumption that neurons themselves are garbage to understand). This obviously raises the question - why privilege your basis? Having a grand result, actually be an artifact of your bad representation is a tale as old as time. Even things like the PRH need to compare higher-level concepts like this.

The specific empirical claim is that, for the rightly structured & trained & large models that don't need to encode hodgepodges of bullshit, the neurons themselves are sufficiently atomic to be studied, dissolving any need to go to some intermediate layer in which we need to question "is this an artifact of our representation?" The resulting interactions between said neurons may be incomprehensible, but the point is that this provides a layer of objectivity (note: not claimed that "the neural nets found the objectively right abstractions in idea space", but rather that the neurons themselves are atomic).

There's still an issue here in that okay, even if neurons are atomic, humans have to ascribe some interpretation to them, separate from the problem of the composition of the atoms, which humans also have to interpret. But if he's right, then you do actually have this privileged base layer to work with, even if you don't know what the base layer means. That's pretty important, actually. Assuming this holds up in reality, ofc.

(There's an argument as to why we should even start from this line of inquiry but whatever lol)

I'm confused - it seems to be saying that, instead of thinking about neurons as packing together a lot of "concepts" together into one, the issue is that your view of what a "concept" is, was already prejudiced, and the neuron itself had a small clean role? Like, you can either think about this as the "Cat & Car" neuron or you can think about it as the "V shaped neuron" - one seems simpler.

But isn't the point that the neural nets learn some insanely abstract ideas anyways? So whether the complexity is "ahh we think this neuron does 100 seemingly unrelated things but we don't understand it" or "this actually is some kind of latent shared feature, but we don't know what the concept represents because the composition of the entire circuit is an arcane algorithm", we're kind of stuck, no?

This seems like a classic model-description issue more than anything.

What a coincidence, I've also picked up inferentialism but never really got into it.

I'm interested in the reversal that Sellars proposes - it seems clear to me that "representation" is not prior to the space of reasons, despite how unintuitive it is.

> An LLM encodes the normative regularities of our linguistic/conceptual games into its weights, but it cannot be a real participant in our games because it is incapable of staking durable commitments or changing its dispositions over time.

I see no reason why this can't be the case in the future, even though I think inferentialist-based theories are generally strong. Can't you force LLM's (or whatever AI architecture will exist in a decade) to stake durable commitments itself, then? They're at least autonomous enough to run computers. I agree that that is a core part to true meaning & autonomy, but nobody just lets them yet. I don't think inferentialism is a consciousness-based argument, either, so that escape hatch isn't available. I also don't think this is a necessary condition to engage in the space of reasons (but maybe then I'm misunderstanding it, or retreating to a more Sellarsian understanding). Could you explain on this more?

Though again, I only have the book sitting on my desk, and I haven't cracked it open...