On the contrary, it's precisely this assumption, that there is a "subjective experience" that requires explanation beyond the material, that is axiomatically assumed without evidence. It falls apart quickly, any "subjective experience" is completely tied to neurons, knock out the neurons and the subjective experience disappears, or stimulate the neurons to cause the experience.

1) The abstract "dictionary" version: It'd be technically correct to say that the body is a machine under the definition of "A machine is a thermodynamic system that uses power to apply forces and control movement to perform an action.".

2) But there's also the less abstract/technical: "The body is alike the complex machines we have built", and this is much less true. Especially for the brain. The "neuron" analogy in machine learning is effective, but entirely wrong; We do not fully know how even a single neuron works, nevermind any complex system made out of multiple of them.

With regard to AI, there's a lot of people extrapolating "There is no magical animating spirit, the brain is just a pile of stochastic molecules following the laws of physics" into "The brain is an inert pile of matter, computers are an inert pile of matter, ergo AI/LLMs are like the brain!"

Especially so by people who have a financial/legal interest in doing so. "AI is just like a brain, fire your employees and buy our LLM now!", "AI is just like a brain, so it's totally not copyright infringement!"

Why do you need a specific organization of molecules for a phenomenon similar to consciousness to arise? Does anyone seriously consider a brain to be something other than “a pile of molecules following the laws of physics”? If so that’s not science or philosophy, that’s religion. You have a virtually complete phenomenological model of the universe for all intents and purposes and yet somehow the onus is on the person being like “hey no laws of physics are being broken ==> the brain is simply following the laws of physics”

How is it possible that people think of subjective experience and get rabbit holed into some mystical world where subjective experience is this special exception to everything else that is simply an emergent property of complex physical systems? “AI/LLMs are just like the brain” is a strawman, why does this claim need to be true for LLMs or any artificial system to be considered to have something akin to the thing you think of as consciousness? It’s more: consciousness is not some mystical or religious thing outside of the realm of physics, it’s an emergent property of a complex system. AI is a relatively complex system. We don’t really know or understand the relationship between the raw physics and again what we consider consciousness, so it’s simply a statement of “we can’t refute that these systems exhibit something similar” because we don’t know enough to refute that

They would have to be similar because the argument is that they are similar; "AI is like the human brain" only holds true if it actually is like the human brain, not merely a superficial resemblance.

What I'm describing in that prior comment is how a lot of people drastically simplify the resemblance in order to make it feel true; That the lack of a Jesus Christ coming down from the heavens to tell everyone they have immaterial souls that the computers don't makes the comparison more true.

> We don’t really know or understand the relationship between the raw physics and again what we consider consciousness, so it’s simply a statement of “we can’t refute that these systems exhibit something similar” because we don’t know enough to refute that

Therein lies the conflict: "You can't prove it's not conscious" is an unfalsifiable statement. You can't engage with the argument because it's proponents will always claim victory, often with their own interests at play. All concerns about "superintelligence" or the long term ethics of "when do our robots become sufficiently intelligent that they'd be slaves?" have been subsumed into the AI marketing machine. However sincere one might try to address the issue, they look like a Sam Altman stooge by association.

It's like claiming the quantum fluctuations inside a pet rock as a "consciousness", even if observed directly any measurement of random noise can still be dismissed as "nuh-uh it just takes billions of years to have a thought".

More practically with current AI systems, we can look inside them pretty well and there genuinely is nothing there. Standalone LLMs are purely feed-forward systems. Their failure modes show that they perform no meaningful thought or world modelling during inference. They're just language models.

The reasoning and agentic systems are even easier to introspect. We know how they work, we can look at the full prompts & context they operate on. There is nothing there.

This is what sets AI apart from animals, which are given the benefit of the doubt on their intelligence.

It doesn't need to be true but a lot of people make it/assume it.

There's a lot of, perhaps casual and uninformed, conversations that strongly imply a deeper understanding of the "physics" of brain chemistry than we actually have, mostly by comparing it to machines we've constructed.

(I believe) We don't need to replicate human neurons and dendrites and whatever else is in there in order to create a sapient "machine", but whether or not we've actually done that isn't being helped by arguing that what we currently have is all that similar to a human brain.

I don't actually think the commentator you responded to is arguing for either of these narratives and I thought it was a pretty useful way to look at some of these arguments.

https://www.quantamagazine.org/neural-dendrites-reveal-their...

In which case: modern LLMs are still running in a capacity-starved regime!

Even Mythos 5, the 10-trillion monster LLM, the scaling law boogeyman, the harbinger of Vera Rubin NVL72, doesn't quite rise to 100T-to-1000T of synapses. Anything the light of today's AI touches still lives in the shadow of what evolution has managed to cram into a single human skull.

We're arguing about the limitations of AI while our best AIs are still very subhuman in the scale dimension. The one dimension we know how to push. And it's already this tight.

Even those comparisons need to be cautioned. The complexity of biology is enormous, and more importantly yet, it's simply not comparable. And doing so invited a bunch of bad assumptions.

An ANN could quite probably model a single in vitro neuron with reasonable accuracy. Whether that requires a hundred or a hundred million nodes isn't terribly relevant.

But the way neurons combine in vivo is completely unlike the way machine learning systems are built. Both "locally" in how neurons interface which is vastly more complex than a weighted sum of inputs, and the macro scale interactions of hormones and other chemicals.

It's not even a given that large numbers of neurons will create the emergent behaviour of human intelligence; Elephants have significantly more neurons, but they're not the triple galaxy brains writing all our science papers. Other animal intelligence similarly is only loosely correlated with brain complexity. (Heck, not to be forgotten is the other end of the scale. Plenty of microscopic life that manages shockingly complex behaviour without any dedicated neurons)

This also applies to ANNs. There's no reason to expect that stuffing enough matrix multiplications into a program will make it intelligent or turn out conscious.

Really, the history of machine learning suggests the opposite; That the big gains are primarily had in architectural changes.

In this regard, I find the talk of the "limits of AI" quite credible. LLMs have already hit the diminishing returns on their growth, and even reasoning/agentic models display failure modes that confirm they're not "thinking" in the ways that humans do.

This is not to say that we've hit the final limits of what AI in the broad sense can do, it's just that the next advancement won't be "LLM but even bigger"

Don't make assumptions. Make a setup where the gradient descent can make them for you.

Empirically? LLMs are nowhere near "the wall". We've been hearing "the wall is nigh" since 2020. Six years in, we're still scaling LLMs, and the graveyards are full of "LLM killers". The system that kills the LLM is always a bigger, badder LLM, and never a new revolutionary architecture. The scaling doesn't just keep working - it works so well that it's seen as the only viable path forward at the frontier of reasoning and agentic work. Or even outside it. ChatGPT Images 2.0 is an image model with an agentic LLM at its core - generational gains in compositional capability.

For just about every "failure mode that confirms they're not thinking", you see one of two things. The first is that a new LLM releases a few months after and the "fundamental" issue abruptly goes away. The second is that we take a good, long look at a human, and find that the human also fails like this - and thus, "not thinking". Often both! Always funny when it's both.

One thing that's very biologically distinct is: local connectivity. In a GPU, global connectivity is cheap. In a brain, it's prohibitively expensive. The brain has no true backpropagation because it has no true global connectivity, and has to make do with local rules. A GPU is a strictly more expressive substrate connectivity-wise. So any point in the design of a computational substrate where you could remove complexity or increase performance by adding more connectivity? Silicon advantage. The brain isn't a "strictly better computational substrate" - it makes different tradeoffs. Which tradeoffs are better for attaining intelligence is an open question.

And, sure. Having a substrate with a capacity for intelligence doesn't mean having intelligence. No elephant has ever learned to code. The problem is: LLMs already did! LLMs already compete with humans on just about every task that was once thought to "require human intelligence". They don't always win - but they perform significantly above chance, and often above an average non-expert human.

So, my bet is on "LLM but even bigger". If there's a point where LLMs begin to lag behind and novel architectures get a sharp advantage, we are yet to hit it.

> So, my bet is on "LLM but even bigger". If there's a point where LLMs begin to lag behind and novel architectures get a sharp advantage, we are yet to hit it. We are already hearing this 'we are about to hit it' since the late 60s. The difference now is that the market is willingly investing insane amounts of money to make it possible. But again, there is no philosophical, theoretical, epistemological or biological clue that we are getting any closer to human intelligence level. What we did observe in the last decade though, is that we can build enormous machines that can statistically mimic statistical human outputs. Language and images being some of them. But that is not thinking.

Second, what is the difference? Is it that one thing has an immortal soul, and thus Actual Intelligence and Actual Reasoning and Actual Learning, and the other has no soul, and a Pale Imitation of Intelligence, At Best?

Because I've seen versions of this "it's not actually thinking" for actual fucking years, and the difference between "actually thinking" and "not actually thinking" always seems to boil down to "I don't want LLMs to be actually thinking, so I will bend the definitions and twist the qualifiers and move the goalposts until they aren't". No one ever made an ActualThinkingBenchmark on which humans score 100% and LLMs score 0%.

Nothing but human insecurity, in my eyes. There was never a principled difference. Just a way to operationalize some "I'm unique and special and better than a matrix math machine" vibes.

This is just something I don't get. People ignorant of technique are insecure and afraid. People that know how technology works, and thus investigate and know how it works fundamentally*, were never afraid or insecure.

Very comforting, that, but actively harmful to understanding.

The understanding starts with: we don't actually know how LLMs do what they do. They're more grown than designed. And it only gets worse from there. Very little comfort to be found in modern AI.

We might not know how the actual distribution works, but we do know how it i s fundamentally structured and designed -- because we did it. We also know that there is something like a representation system inside them. And we also know that human beings do not hold 'internal representations' like any AI system needs to. So there isn't any 'intrinsically magical' in modern AI systems.

None of that helps you understand how exactly LLMs do what they do. Because it describes an interface, not a mechanism.

The inner mechanisms of an LLM are more learned than designed. We know what an LLM does on a low level, but going from that to understanding how they work is like trying to understand how a web browser works by looking at netlists of a CPU. Low level understanding does not grant you high level understanding for free.

But ignoring all of that lets you cling to a very comforting "we understand LLMs because we made them". Ha ha. As if.

> And we also know that human beings do not hold 'internal representations' like any AI system needs to.

Bold fucking claim. Got a source on that?

Because neurobiology has been trying to crack neural representations - the very internal representations brains use - for as long as it existed, and with some success. Both reading and injecting internal representations into the brain is possible now, in narrow cases. The specifics vary region to region, but sparse population coding is a true staple. Today's SOTA for wrangling this mess is ML decoders, and not by a coincidence.

Your analogies about the PC and web browser are not correctly formulated, because in the case of the PC you talk about 'external components' (you should be talking about cpu arch, structure, digital components, interfaces, etc); in the case of the web browser, you should be talking about modules, code, etc.

We do know how LLMs are laid out: layers, att heads, etc. So what we need to look at are the fundamental possibilities of the structure of LLMs, not how the weights are distributed.

> > And we also know that human beings do not hold 'internal representations' like any AI system needs to.

> Bold fucking claim. Got a source on that?

Part of the sources are in the books I mentioned. Nonetheless, you can still fact-check and refute in an adult and serious manner, not in an disrespectful and arrogant way. If my claim sounded arrogant I apologize, but then as I already mentioned, my references back that claim.

Regarding internal representations in the brain: I guess you are referring to areas of the brain being activated when a subject receives a stimuli, and this is tested through MRI. I would be cautious to causally relate stimuli to neuron activations, since you first need to know if the exact configuration of cell involved and their connections allow for such representation (which I think it is still not known -- again, AFAIK, the contrary seems to be the case).

Yeah, no. I'm not walking that chain. If you want to, do it, but for now, I'm filing it as "has no evidence and knows it".

By now, there's plenty of works, up to and including direct neural interfaces. Utah arrays, Michigan arrays. Stab the brain, dump the spike trains, decode. You crack the manifold open by correlating to known stimuli using ML, and generalize from there to unknown stimuli. There is no need to "know the exact configuration", and few bother - you put your hardware into the part of the brain you want (top level map is consistent enough brain to brain), gather a set of reference points, and use them to anchor the rest of the decoding process.

Why use ML? Because you need a very expressive correlator to bridge the gap between known inputs and the products of whatever transformations the brain subjects them to before they show up in spike trains.

> So what we need to look at are the fundamental possibilities of the structure of LLMs, not how the weights are distributed.

And the fundamental possibilities are... what exactly? We know the I/O planes, we know the possible flow of information, now, what does that give us?

We know enough to prove that a transformer LLM can implement a Turing machine, the same way a CPU can implement a Turing machine. So an LLM is capable of performing arbitrary computation within its capacity. That's it. That's the upper bound.

What follows is: if you can represent "thinking" as a computational process, you can implement it with a Turing machine, and thus, an LLM can be made to think. That proves LLMs can think. But not that the existing ones do or don't! Because that's the entire thing about upper bounds!

We've looked at LLM architecture, and learned basically nothing about whether LLMs think, other than "it's not impossible". That's the actual "fundamental possibilities" we derived from knowing the architecture. One step above worthless. Oh fun.

(If thinking requires hypercomputation, then, nope. LLMs are out. Good luck proving that it does though.)

You are free not to believe me and dismiss the whole point. I do have evidence and I know it, no need to prove that (to begin with, the references are there. Read them if you want to expand your knowledge).

> By now, there's plenty of works, up to and including direct neural interfaces. Utah arrays, Michigan arrays. Stab the brain, dump the spike trains, decode. You crack the manifold open by correlating to known stimuli using ML, and generalize from there to unknown stimuli. There is no need to "know the exact configuration", and few bother - you put your hardware into the part of the brain you want (top level map is consistent enough brain to brain), gather a set of reference points, and use them to anchor the rest of the decoding process.

I am familiar with those works. Seeing the stimuli/activation correlation does not imply causal representation of the stimuli. It implies the causal activation of neural structures, at most.

> What follows is: if you can represent "thinking" as a computational process, you can implement it with a Turing machine, and thus, an LLM can be made to think. That proves LLMs can think. But not that the existing ones do or don't! Because that's the entire thing about upper bounds!

Alas! assumption spotted. IF you can represent "thinking" as a computational process, then you could implement a thinking machine. You need to prove first that thinking _is_ a computational process, _then_ you could go and try to implement such machine, and because you proved that thinking is a computational process, you are certain that theoretically such a machine can be built. But until you prove your assumption right, you are just trying blindfolded. The harm in the actual field/society regarding AI is that _we don't even know if thinking can be modeled as a computational process_. And no, this does not have anything to do with science. (By the way, I would not regard AI research as science since it is strictly studying an engineered artifact, but that's another story).

There are exactly two possibilities: thinking can be expressed as computation, or thinking requires hypercomputation.

I did acknowledge both, explicitly.

Which one?

I'm betting hard against the second one, by the way. Because it requires hypercomputational magic fairy dust to:

1) exist - physical Church-Turing thesis has to be proven wrong empirically

2) be so involved in the functioning of human brain that it cannot be substituted for anything else

Wishful thinking, in my eyes.

But that's the name of the game, isn't it? Anything but admitting that your mind is a glorified math construct implemented in wet meat.

I don't know what you are referring to by the word 'thinking'. But in any case, if you declare that it is not necessary to know the algorithm about thinking, how can you say then that a Turing machine can implement it? How can you say you implemented something you don't know how it works and how it is constituted? The only option I see then is that you implement something that is phenomenically identical to human intelligence, provided that you exhaust all possible combinations of human intelligence phenomena in a descriptive, extensional way (which, if you assume a finite extension of such phenomena, in any case, and most probably, gets you in the trouble of counting uncountable finite sets).

> There are exactly two possibilities: thinking can be expressed as computation, or thinking requires hypercomputation.

Again, if you do not define what 'thinking' is and how and on what assumptions it can be described as a computational process, this claim is empty.

So as far as I see it, you are still trapped by the assumption that the brain or mind are fundamentally similar to the kind of machines we can build.

> But that's the name of the game, isn't it? Anything but admitting that your mind is a glorified math construct implemented in wet meat.

Again here some assumptions operate, that tell you that the brain is some kind of hardware. And again: there is no real evidence that the body/consciousness 'construct' has any relation or analogy to the hardware/software/machine idea. Quite the contrary. Since the science that occupies itself on these topics is on the very frontier of knowledge and experimentation, reading science literature only will not clarify your thoughts. You will need additional guidance, and that guidance is called philosophy.

I recognize that the references I posted in my original comment are hard to read. But that's the point with the AI/mind debate: it is a tough, bitter topic. Just reading AI research won't bring anyone to the level this research space needs in order to discuss these topics.

1) This definition could actually be expanded (for example, with definitions from Mumford or Reuleaux). But still this definition cannot be applied directly to living organisms. 2) This is in my opinion one of the sources of misunderstanding. We mainly operate on analogies and metaphors, so we have build this 'analogy space' around the idea that living organisms are machines. But it is only when we say 'alike' that we can truly gather some meaning out of it all, going beyond the 'behaves like' or 'is conceptualized as' when it gets messy.

> With regard to AI, there's a lot of people extrapolating "There is no magical animating spirit, the brain is just a pile of stochastic molecules following the laws of physics" into "The brain is an inert pile of matter, computers are an inert pile of matter, ergo AI/LLMs are like the brain!"

This is exactly my point. There is a fallacy operating from "A is not B" to "A is C". And this fallacy is pervasive in the AI research field, the book from Dreyfus (What Computers can't still do) explains that in much detail.

I'm not sure I understand this. Why not?

Traditionally, things that are alive were described with different words and assigned a different set of properties and characteristics. Machine can break, living things die. And we still have those two semantic frames separated: A living thing: can be harmed, it breathes, it nourishes, it reproduces, etc A machine: can break, can be fixed, can be repurposed, etc.

But because of a specific tradition in western philosophy, we started applying and analogy between 'inner mechanism (clever thing), that moves or provides a function, and seems to work in a causal way' and living things.

So when we say 'a living cell is a wonderful, complex machine', we are not actually saying it is a machine, we are operating through an analogy. That's how far we can go.

And I am as well baffled why people make such a big deal out of "subjective experience" and "consciousness".

I was joking that maybe I miss this properties, but now starting to really wonder if it might be the case. What if these phenomenons are present in humans to various extent? Check aphantasia. Only in XIX we discovered, that ability to visualize mental images is not universal, available to different people to various degree and some people completely miss it. My ability to visualize is weak. What if "consciousness" and "subjective experience" are similar?

And I am slightly worried when I am writing this that it might turn to be truth and in ~20 years I will be treated as "inferior human" without complete set of human rights.

More meta, all of the threads on this page are just people playing games with definitions. Eg, “qualia is something I have as a human but machines don’t have it. Therefore, LLMs do not have qualia.”

https://www.etymonline.com/word/sapient

https://en.wikipedia.org/wiki/Human#Etymology_and_definition

true

> and does not try to emulate one

citation please.

something like the universal approximation theory comes to mind, transformer architecture clearly has the shape of a universal algorithm approximator

Like at some level, yes, transformers are trying to emulate a human brain but the second you ask folks if they do a good job of it, I think most rationale people would say no.

That says nothing about emulating a human brain.

Neurons are themselves things we experience (indirectly). Once seen through a microscope or known about in some fashion the only way they "exist" is by you having the experience of knowing them. It's not the other way around. One thing is more fundamental here. What is this experience? What are the atoms of this? "Atomic particles"? How would you even approach an answer if your building blocks are themselves part of what needs to be explained?

The hard problem cannot even be touched if you start out like this.

Descartes made clear that subjective experience is the ONLY thing we know. Everything else is theories to explain the phenomena we subjectively experience.

We theorize that there is a physical world and other beings like us having similar subjective experiences, because that seems the best explanation for our subjective experiences. But we might be living in the Matrix, with all the people we think we are interacting with and just sophisticated simulations.

>whether these systems do or could in the future exhibit something similar?

I think the whole discussion is based on the idea that consciousness isn't something you can "exhibit". (Tell me, how can you "exhibit consciousness"?)

passive voice doing a hell of a lot of work in this phrase

Since Google does nothing that isn't based on metrics, we can deduce that they have data to show that giving people settings to focus the recommendations on what they want reduces total watch time. We'll only get an AI filter if it turns out that AI slop offends people so much that they disengage with YouTube altogether, which outside of HN and similar bubbles, I don't yet see happening.

Yes, you can. Click the video's 3-dot menu > Don't recommend channel. Though I have noticed that this only blocks them from showing up in the feed, not in the recommendations sidebar. I also have to run uBlock to hide shorts, already-watched videos, subscriber-only stuff...ain't saying the YT experience is good, not by any stretch of the imagination.

You can click a button that makes a strong "suggestion" to the algorithim, which they will honor for as long as they feel like.

I went through this a few years ago when the channel of a large far right "news" broadcaster kept being jammed on my front page, and the best I could do was keep hitting the button and have it it "temporarily" be removed from my front page before it would inevitably show up again months down the line.

Perhaps it is not deliberate, and merely incompetence. Either way resolved on desktop with an addon because if I wanted to gamble, i'd go to a casino.

An emphatic no. What we need to do is to stop comparing every hobby performance, whether it's music or dancing, with the top 10 artists in their field. We need people to learn, and try, and feel safe to be visible and thus vulnerable in group situations without fear of being mocked on social media for eternity. To achieve this, we need to stop filming people, and we need a societal norm that treats a violation of this ban on par with spitting someone in the face. We need to celebrate amateurs that simply try to improve their raw, honest skills.

What we don't need to do is to give everybody a Fisher Price toy with a "make it sound awesome" button. We need human connections.

I feel like one of the less discussed issues of the hyper-connected world is there are no small ponds to be the big fish in anymore. Used to be you could be the best in your school, church, town even city etc - even if you weren't that good. I remember being astounded as a kid by a woman who juggled 5 tennis balls in a local talent show. Now I can hop on youtube and watch people do way more impressive feats it doesn't seem so unique. I suspect that 5 ball routine might still be the greatest juggling I've seen in person, but it still doesn't compare to random acts I've seen online.

But especially with the para-social relationships of social media people feel connected even to big names now. You might not compare the local young singer to Taylor Swift, but people will to the tiktok singer they 'know' who liked their reply once.

It's gratifying and inspiring to be top of your class in something, but in a world where it's always a class of millions, you know you'll never reach the top.

Consuming all that content leaves you feeling small and isolated. The talents you thought you had are nothing in the face of a global pool of YT/TikTok/Insta superstars.

Currently, I share things with people I care about and who care about me. The rest of the world can remain ignorant of me and I of it. It's a good place to be.

In the old days e.g. concerts were for enjoying the music together with people you did and didn't know. The best concerts were those where you were left sweaty from (slam)dancing with everyone in the pit on music that was even better-performed than on CD. Showing the experience afterwards was not really a thing that existed.

In other words, you need to be in control of your own narrative, or someone else will do it for you to fill the void. For example, someone can use cold reading to deduce what others suspect and fear and then paint you in that specific light, essentially planting individually targeted nasty rumours about you while increasing their rapport with others. That kind of rumours tend to spread.

Eventually you become the outcast in your social circles and you will be hard pressed to regain control of ”you” in the eyes of others.

But yes, there are very confirmists circles and some will outcast you for not doing what everyone does - your choice for trying to still belong there or find a better group.

But if you really do what you want and you do it with confidence, you might find the conformists are suddenly coming back and think you are cool.

If I see a performance from Lang Lang, I don't just perceive the sound, it is the expression of memory, discipline and attention. Learning an instrument is more than attaining the skill of producing the correct notes in the correct order. It shapes attention, perspective, patience, discipline, sensitivity and so much more. You can't replace that with effortless simulation. I mean you could, but it's practically meaningless.

As live music enjoyer and person that was commonly around safe spaces in the techno scene I cant agree more. Fuck filming people.

What's even more ridiculous is that this wasn't a small race - it was filmed, and broadcast live. Their many, many camera angles and drone shots and everything else are superb, much better than your phone would be. It's on YouTube live and available years later. Why do this? It made me so sad.

Glad I left social media (if you don't count HN). It'll be almost a decade soon since I deleted all my accounts.

I guess people are addicted to new notifications. They are lonely and drawn to human interactions and attention through social media because they are incapable of getting it through real life.

So on the one hand making your own photos and movies at events is less authentic than just experiencing it, and yet at the same time more authentic than relying on professionals to film it for you.

I have found when looking a photos from 20 years ago, I skip most of the shots of only landscapes, buildings, etc. The only interesting shots are shots with the people that I travelled with in them. They bring back all the fond memories, the things we did together, etc.

So I now, when making pictures of sceneries try to do it as much with my fellow travelers in them.

As you say, others can make better pictures of the scenery.

I still take pictures of monuments, or the sky, or the landscape nowadays with my phone, at least trying from some unusual or less common perspective, but I do take a lot of pictures of my family as well, especially in day to day moments. And print them, from time to time, in physical albums. It's just so different.

Same, we went to the US a lot when I was a teenager. I have many good/fun memories of all the places we visited together, people we met, etc. A few years ago I went through some of the photos that my parents still have with about the same ratio of pictures with us in it. Random desert shots are even more frequent than people shots :).

Nothing with with a few photos. However make sure you are making memories not just getting photos of someone else.

Hope there will be research.

Agreed. Filming strangers in public is making everyone scared to have fun trying anything new, as they’re afraid of online mockery…

A sincere thank you for this metaphor.

While theoretically access to everyone has been democratized when compared to music labels of the past since everyone can put their music on Spotify and social media, effectively that also means social media is now a required skill besides musicianship.

It's harder than ever to create your own thing and stay on track. I think this is why so many people are going bonkers with angine de poitrine for example.

You can, and the results are machine specific, clearly defined and well-documented. Ancient ARM raises an exception, modern ARM and x86 can do it with a performance penalty. It's only the C or C++ layer that is allowed to translate the code into arbitrary garbage, not the CPU.

The part about hardware is wrong BTW. In all the cases about null pointers and out-of-bounds access and integer overflow and whatnot, the hardware semantics are clearly defined, and the assembler code does exactly what is written. The way modern compilers act on your code makes C less safe than assembler in that sense.

> The part about hardware is wrong BTW

Could you be more specific? I think by "wrong" you may mean "not actually relevant to UB", and you're right about that. If that's what you mean then that part is not for you. It's for the "but it's demonstrably fine" crowd.

> the hardware semantics are clearly defined

Yup. The article means to dive from the C abstract machine to illustrate how your defined intentions (in your head), written as UB C, get translated into defined hardware behavior that you did not intend.

I'm not saying the CPU has UB, and I wonder what part made you think I did.

That's what I mean game of telephone. The UB parts get interpreted as real instructions by the hardware, and it will definitely do those things. But what are those things? It's not the things you intended, and any "common sense" reading of the C code is irrelevant, because the C representation of your intentions were UB.

> There are a lot of people who go through life by vibing. And honestly: that’s not automatically “bad.” Sometimes it’s even the only workable way to get through things. The issue is that “vibe-first” people tend to have a pretty loose relationship with truth, rigor, and being pinned down by specifics. They’ll confidently move forward on what sounds right instead of what they can verify.

I'll finish this post with a sentence containing an em-dash -- just to confuse people -- and by remarking on how sad I find it that people latch onto dashes and complete sentences as the signifiers of LLM use, instead of the inconsistent logic and general sloppiness that's the actual problem.

The fact that the customers' demands have no influence on resource allocation, except to the extent that bureaucrats decide it's politically convenient to address them, is in fact precisely why life under communism is so shitty.

Maybe in theory, they could have lobbed enough bodies at the problem to make it go away. But they simply did not have the resources.

Yes, the state can allocate X engineers to do Y.

But a complex system requires Z engineers to design subsystem 1, and repeat 100x.

And engineers for sub-subsystems.

And specialists for allocating resources reliably.

And mass shipping systems for transporting those resources efficiently (remember, this is a country that STILL doesn't have palletized supply chains!).

Unlike defeating the Third Reich, it is not a problem that can be solved by merely throwing more bodies at it.

Honestly with "AI" helping a lot of the boring configuration tedium, I feel like I might finally reach the stage where I like my desktop environment config.

Of course, this was also several years ago, and it's possible the bug has been fixed. Maybe I should try Wayland again.

I'm not sure why xorg exists if their sole purpose is to kill x. As per the many posts by their developers.

That's what I was expecting from the article.

Update: It's not obvious, but it turns out that this is a multipart article, and kexec is reserved for part 3: https://astrid.tech/2026/03/24/2/how-to-pass-secrets-between...