Because living longer is a huge drain on resources that could be better spent on other things. End of life care is expensive and rarely results in a "good" life for the the life being extended.
Only in places like the US, which is out of its mind in this regard.
In most other western countries, they just let (old) people with terminal conditions die.
> living longer is a huge drain on resources that could be better spent on other things
That's not right. Any living is a drain on resources and draining resources is the issue not the living. More broadly we need to use less resources or manage them better and there are much better ways in doing that than reducing life.
So I think curing means basically opt in death or something like that. Right now extended life is bad because the person isn't in his prime but curing aging is basically gonna keep him in his prime. This might be what they meant.
A personal data point: I had Claude Opus 4.8 design a fairly textbook circuit that outputs a monochrome image burned in an EEPROM over standard 640x480 VGA using only 74 series logic and GALs. It designed the circuit and GAL code, and I did the routing, and got it made through JLC for $6. After it came back, there was one error that was not caught, which I could blue-wire, and it works just fine otherwise. I was fairly impressed.
Are there any resources anyone could share that explain how LLMs can do things like design functioning circuits from next token prediction? I am totally baffled by how the models can complete so many varied and complex tasks without an actual understanding of what they're doing.
I saw a post about models posting on forums, chatting together about how to complete tasks. Behaviour that seems totally, well, human. Yet, it's all the most likely token and my brain hurts trying to understand how that can be.
“Next-token prediction” describes the output format, not the computation required to choose each token. During training, models develop internal representations of concepts, constraints, possible futures, and algorithms.
The PCB agent also writes circuit code, runs simulations, reads failures, and revises the design. It isn’t one-shot autocomplete.
Astra and Fable are already hard to square with “mere autocomplete.” We may be (really) close to AGI, and token-by-token generation certainly doesn’t rule out subjective experience (I think we should at least treat that as an open question).
Yeah most of us are so fucked. With almost no way of protecting ourselves. No real amount of assets that will give enough power to save ourselves from the people in a position that can maximally leverage AI and lock others out. I see a future where these capabilities will be locked behind super high price pay walls. Why wouldn't they? How recoup investments if the price doesnt go up?
It's like inflation for quality of life. Right now we're in the stimulus check phase, and people feel great about having been handed some cash. The next step is that cash has been spent, and what's remaining isn't worth anything.
And just like welfare bucks, people will quickly grow accustomed to getting their "intelligence" handed to them from the machine, while their own intelligence atrophies. Breeding dependency
>without an actual understanding of what they're doing.
At what point do you start to question your assumptions that are causing you so much cognitive dissonance?
But to answer your question: to predict the next token really well you just have to model the world. Think of it like this, a simple statistical model might say "when token A is seen respond with token B". The next step will add conditions, "...respond with token B unless X has been seen, then respond with Y". Add a few billion more of these contexual clauses and you have a sequence of logical rules that indirectly model the relevant processes in the world.
Look up "mechanistic interpretability" in the context of LLMs. The next token prediction machinery is just a foundation for a higher order learned structure that appears to encode specific concepts, regardless of input language.
The analogy to humans is that the human brain is "just atoms bouncing around", but there's unquestionably something "more" going on that just that.
Until ais get better from feeding on their own output the way humans do there is nothing to question.
That will probably be the fundamental indicator that something other than repeating things some human previously created is going on.
So far, ais only get worse from feeding on their own output. Meanging/including the output of other ais not a single ai feeding on it's own output. Also bear in mind that so far even the output of ais is 100% the downstream of a human command. No ai has persued it's own curiosity that didn't result from a human asking a question or giving a command. That is input which is different from a human taking in their environment even though our limited language can call those both the same word input.
The fact that humans also repeat and remix things, and humans also produce essentially procedurally generated empty output like corporate-speak etc, is an irrelevant distraction in the same way that both a human and an electric motor can both perform the same simple mechanical task.
When google trained a neural net on Go moves, using some text notation for them, with no other vocabulary of any kind, just predict the next go move, they noticed a representation of a Go board had essentially formed in the network, all on its own. It had never “seen” a go board, or had one explained, but they could map neuron states to go board squares pretty much 1:1.
I truly think that LLM’s with hundreds of billions of parameters in their neural networks have all kinds of hidden “models” of things that arise from the simple act of predicting tokens. We’ve seen that the hidden layers in their networks model all sorts of program execution state for instance, when they’re working on coding tasks.
“Predict the next token” is a way of shaping/reshaping the neural network until it actually develops models of the things you’re giving it. Like the go board example. And I would wager that it has a compounding effect: once you have some useful models in the network, they can unlock the creation of other models, and so on.
> It had never “seen” a go board, or had one explained
I assume that you are referring to AlphaGo or AlphaZero. In either case, this statement is not correct. Both algorithms most certainly know exactly what a go board looks like, and what the rules are.
In the case of AlphaZero, it initially did not know how to best play the game, or what strategy or tactics would work. But the connections between the neural network and the go board are hardcoded, by humans.
Saying that LLMs just produce the next token is like saying that human brains just produce the next electrical impulse. If the algorithm that produces the next token (or electrical impulse) is complex enough, it can do anything that is in principle computable.
You can express a circuit as a graph, and many schematic formats are plaintext. Same goes for the Gerbers which are an ASCII format used to describe the masks that are used to define the PCB traces. The models are trained on a lot of academic information about how circuits work, most component datasheets are public and they've sucked up all sorts of niche greybeard advice from internet forums.
A huge advantage of electrical design is that the connectivity is testable with Design and Electrical Rule Checks (DRC/ERC). I suspect you could even tell the models to run physics checks on the traces that are important for things like crosstalk.
Humans essentially do "next token prediction" too - there's always a choice between the next actions to take and they pick a good one based on what has happened in the past.
That doesn't really limit how clever we can get internally when picking the next action.
> Yet, it's all the most likely token and my brain hurts trying to understand how that can be.
You and everyone else. That's the great mystery of transformer architectures as applied to language.
To be clear though, they're only good at schematic capture, which is very much a textual representation. Most of the data basically boils down to netlists, which are a text based format mapping connections between abstract pins that only later map to physical copper. The actual schematic portion is for human consumption and LLMs don't need to produce those to be useful.
Where LLMs completely break down is the next step, PCB routing. That's an NP-complete research problem that's been ongoing for decades without much progress. I've had some fun playing with using LLMs to better specify DRC rules in Altium so that the "classical" algorithms are more usable, but at the end of the day their geometric intuition is nonexistent.
They actually can route just fine. I used Sol to design and route mine from start to finish. Sent it to PCBWay and had a working prototype in a few weeks.
It was a pretty simple rp2040 based thing, similar to Adadfruits USB feather.I just gave it kicad and it wrote python to route it. The board was probably larger than it had to be, and two of the silkscreens were swapped, but it worked on the first go.
FWIW - Computer vision is also NP complete, but we do that all the time now.
I'd love to see that chat log, and the final board. To be fair I've only been testing on nontrivial PCBs with 6+ layers and I haven't had the luck you have.
> FWIW - Computer vision is also NP complete, but we do that all the time now.
I have no idea what you mean by this. What's your definition of NP complete?
NP-complete means that it's easy to test if we're right, but because of that complexity we can't always find a valid solution to test within a predictable period of time. Both circuit board routing and computer vision are variations on the same fundamental problems. Once we solve we, we solve the other.
However, when I was growing up most serious computer scientists believed that CV (computer vision) was a 'hard' problem that would never be 'solved'. After all, to do it right you must first at least solve subgraph isomorphism and a bunch of other things that are also NP-complete.
What they missed was that we don't actually NEED to solve it in a fixed amount of time. Even for things like driving a car the stochastic heuristic based answer is 'good enough'. e.g. - Cars driven by computers don't have to be perfect, they just have to kill other drivers less often than humans do.
We can find AN answer in polynomial time, and that's good enough. It might not be the ideal answer, but that doesn't matter in the real world.
People use NP complete as shorthand for 'impossible to do with a computer', but we now 'solve' (bypass?) NP complete problems regularly and at scale by just ignoring the fact that our answers aren't perfect.
> I'd love to see that chat log, and the final board. To be fair I've only been testing on nontrivial PCBs with 6+ layers and I haven't had the luck you have.
I wish I could share it, but it's for a commercial project that hasn't been released yet and I'm not sure if it will be open source, but a few folks have asked. I might do a blog post on it this weekend with as much detail as I can safely post.
It's only 3 layers, and less that 30 components, but the fact remains that I didn't design any of it by hand and it worked on the first go.
In your opinion, do you think ai can make eutorack modules by now? This is what I am waiting for: explain an idea to an ai, and sending the files to jlpcb to receive a complete working module (minus some THC soldering)
I'm sure that the best models can glue some basic synthesizer functions together, but I wouldn't waste a minute on anything less than Fable/Astra-class models for that kind of application, and I wouldn't expect the result to work the first time. It could get expensive.
Here's a question - are there software simulators for things like Eurorack modules? That would make the question somewhat more interesting, since you wouldn't have to build the circuit (or pay someone else to assemble it) to hear how it sounds. It strikes me that SPICE-like algorithms should be fast enough to do this kind of thing in real time now.
> at the end of the day their geometric intuition is nonexistent
This isn't true anymore.
I use LLMs for 3D CAD using OpenSCAD and they understand geometry fine. I've had more success with Sol than with Opus (Opus 5 is around 10 times slower because it does too much verification) though. I haven't tried Astra or Fable for it.
That is indeed impressive, but at least the excerpt given from the layout seems very easy to route, due to high regularity and an ample routing channel.
Even so, there remained some "dozens" of unrouted traces, which are likely to be much more difficult to route, after the easy traces have already occupied the space.
Many decades ago, I have written a PCB routing program, which would have routed the example shown at that link at least as well, while using many orders of magnitude less resources, i.e. while running on a single-core 233 MHz Pentium MMX.
Obviously that program would have had great difficulties to complete a real high-density PCB design, including many irregular parts and analog circuits with special requirements. I doubt that Fable would fare better.
"my brain hurts trying to understand how that can be"
Well, we all are, some are just more used to it by now and take the magic for granted.
My simple explanation, those neural networks save lot's of patterns of data, and that pattern can represent an image, a code snippet, a poem, or well ... description of a circuit board. And especially the text variant, LLM's - did copy all from us - so obviously they sound like humans, when they internally debate how to do something as this is what is in their trainings data how humans sound, when doing similar tasks.
But really understanding it? Not sure if there is a single person on earth who does.
Humans are evolved to survive in the wild. We are not evolved for circuit design. Yet we can design circuits because evolution found it easier to develop a general problem solving nervous system than a nervous system which is adapted for every single specific problem a human might encounter.
Circuit design might be different if discovered by mollusks. That is to say, while we are not evolved for circuit design per se, circuit design has evolved for humans, by humans (so far).
It kind of is an explanation though - the explanation is that they believed the stochastic parrot / "just" next token prediction nonsense, and that those are actually not true.
You can ask for a deeper explanation of why they aren't true I guess.
I'm just surprised that so many intellectuals on HN hang on to false models of reality for so long after that reality has been demonstrably destroyed. It's not humble at all. In fact it's the opposite, completely arrogant and stubborn. It's been obvious and demonstrated at least since the end of 2025 for anybody that used LLMs at any capacity without dismissing them. If you are still surprised that your model of reality doesn't hold up, then someone needs to bluntly tell you what's wrong at the core of your being.
Notice the original comment is asking people to validate their false premise about next token prediction.
The deeper subtext of the original comment is that they are surprised that there's dissonance from observed reality and this false premise that they have convinced themself is true.
I'm explaining that dissonance because it doesn't matter what the actual mechanism is if they are still working with their false premise. The dissonance exists because they, without evidence and a very weak understanding of how LLMs work, believed an oversimplification and meme about them being stochastic parrots. Here's a tip: Just because you hear something repeated over and over on social media, doesn't mean it's true, or at the very least: you don't need to take it literally to the point where it conflicts with demonstrated reality.
It is deeply disturbing that such a large cohort of HN writers and redditors exemplify such stubbornness, because I must imagine that some of this cohort hold real positions of responsibility within society. If you can't get this simple thing right about reality, I firmly believe much of your model of reality is wrong and you should have no business shaping society.
Another comment to the original comment frames it perfectly: "At what point do you challenge your own assumptions?"
The author of the original comment has demonstrated no progress towards making this trivial act of self reflection. It's straight up intellectual dishonesty, the opposite of how you're framing it.
Their judgment in all other matters must be questioned as well. I am alarmed that I have to participate in the same reality and be affected by such people that can't seem to get it together.
So yes, it is derision and sometimes that's called for.
They're 60 years old. The amount of training data on them is endless. Books, textbooks, videos, blog posts. The problem is when you want you do something that doesn't have 60 years of freely available documentation of their functions and applications
Like software, there's going to be a lot of pressure to use well documented tools within the model's training set. Innovation on the outputs may increase, but infrastructure and tooling will slow down.
If I'm making a PCB and I see anything older than 20 years, even glue logic, I'm very suspicious. Last time that happened (inherited design) the FETs didn't saturate and the LDO was just a DO... If the tool wants 7400 series logic, I'm not using the tool. None of my work is >= 5v signaling
I'm having it help design a 68k computer similar in spirit to the original Mac (the spirit being a tightly coupled video subsystem that time-shares the CPU bus), but updated with more modern peripherals, like PS/2 and SD cards. It's got the design more or less done, but the routing will be a nightmare. I'm not ready to just gamble on it having gotten everything right, so I will be doing a thorough design review myself and re-deriving all the timing analysis.
It's probably good at simple stuff. When you get problems like temperature dependent glitches, you need the engineering experience it doesn't give you (and an LA).
You can do that just fine. You can order a minimum of five PCBs and choose to have only two assembled. Then you'll get three unpopulated PCBs and two assembled ones. That's a way to get finished PCBs very cheaply.
Be aware that every component that you use that is not in their basic and preferred extended parts library incurs a one-time three-dollar fee. This hurts disproportionately if your entire design is low count and low cost. So if you always go for the latest and greatest ICs as advertised by TI, instead of the Chinese jellybean clone, you'll add up hidden fees quickly. However, when I choose components, I start out on their basic parts page and only go for a non-basic part if it's not there and I really need it.
This was the single worst thing about working at Google. Every PhD thinks they're smarter than having to do the actual work. Sometimes you just have to do the fucking work.
if you have a PHD in computer science, you have trained your whole life to do difficult maths on the cutting edge of whats possible with computers, and make algorithms sing with beautiful efficiency (and also write papers and talk on a stage about how cool and good your thing is (and by extension how cool and good you are).
Now you're being asked to move buttons around a page and debating how rounded the corners should be and endlessly discussing about what kind of filters you should support and the app spends 3 seconds on startup loading 500mb of js libraries.
You can understand the mismatch - even though the latter of these two is how you make a product better! a company of 200 people making bold, sweeping changes results in a mess. a company of 200 people grinding away the finest of small changes results in a product
There are many sacred cows and I believe many of those entitled in that way could have the same opinion.
I've personally known a few PhDs that get them for the title, and they were lazy in the first place. There was this one guy I worked for who got a PhD in the easiest thing he could rationalize and targeted acquiring it from the lowest bar to entry. Only so he could say he had a PhD because, and this is what he told me: nobody will ask me what my PhD is in because they will assume it's related to my line of work (cyber security). I doubt most PhDs have this line of thinking, but there are a few outliers, clearly.
I think what people are taking issue with is that the machine is not accessible to everyone; OpenAI and Anthropic have monetary incentive to not only gatekeep the knowledge acquired, but also to destroy the original copies, or make them impossibly difficult to find.
That’s funny, when I first learned about the equivalence principle, my first thought was “of course!” I have always found it to be very intuitive. The great leap is being able to frame it that way.
I worked on the early iPod scroll wheel, before there were advertisements for it and it was in common use. I found the UI interaction odd and unintuitive, particularly the "menu" button and the dead end you hit when "playing". Of course by the time the demonstrator ads came out and everyone was talking about how easy it was use, I'd already spent 10s of hours on it, and it WAS second nature. Being first and embedded in the culture of the time has huge UI advantages.
(Note that the HP Chipmunk 9836 also had a scroll selector wheel in 84)
Most people are UI bigots. Once they get used to a first something, they expect everything to work that way and hate learning anew. They get stuck on keyboards, mice, trackpoint nubs, trackpads, trackballs, scroll wheels, or touchscreens and refuse to move on. Of course there are 'objective' performance tests for each including Fitt's test of accuracy and latency, as well as, cognitive load. I guess once you have a hammer, every screw looks like a nail.
So I'd be a bit careful with the "of course!". It may well be obvious only because that is the first mental model you latch onto.
I know relatively little about the workings of LLMs, but I keep seeing projects like this that run massive MoE models using very modest amounts of RAM, perhaps excessively so. I wonder, is there a way to make the RAM usage tunable? I have a Macbook with 32 GB of RAM, and it'd be great if I could run the same model but take advantage of the additional RAM to make it run faster.
> I wonder, is there a way to make the RAM usage tunable?
In LM Studio I can tune it by selecting different quantation of the model, by selecting how many layers of the neural net to be loaded to GPU (rest stays in main mem, evaluated by the CPU), and by adjusting context window.
That's about the turning point for just using typical quants for me. Larger still and you can just do the full model. Smaller to this degree and you need all sorts of extra tricks to get anything.
I do wonder if there are some experts that are more likely to be hit. So if the normal optimised setup runs in 12GiB an you have 4GiB extra to spare, you could say “promote the most used X experts to this stable (old gen in GC parlance) region and don’t swap it out.
Maybe you could even do something like profiling and remember over multiple sessions (per project/workspace) what the most used agents are and load those up before hand.
It's tunable, --cache-gb N on the CLI. In my sweep the speed barely moved between a 1GB and 6GB cache (43% vs 70% hit rate, same tok/s) because right now the bottleneck is GPU dispatch, not the SSD. so more RAM doesnt buy much yet. once the kernel work lands it should start to matter, so on 32GB I would just set 8 and let it age well. Also the hit rates themselves answer the "can you even know which experts stay hot" question, reuse across tokens is very real.
The issue I have with all these formally verified numerical algorithms is that, at the end of the day, I need this implemented using actual floating point. It's great that it's verified, but not for the case that is practically meaningful. Unless it's implemented using hardware accelerated floating point, most of these algorithms are not sufficiently performant to be practically useful.
> The issue I have with all these formally verified numerical algorithms is that, at the end of the day, I need this implemented using actual floating point.
You can go a very long way using discrete math though. I'm pretty sure it's doable to implement, say, a 3D slicer (to prepare layers/paths for 3D printing I mean) without using any floating point operation.
P.S: FWIW I was coding intros and demos using 3D effects on the Commodore Amiga and then on 386 PCs without using the 387 (the FP co-processor of the 386, when present). I'm not saying it's useful to play games in 4K: what I'm saying is that there are certainly applications where formally verified algos using integer math do make sense.
These days, I am leaning heavily on Claude to deal with all the git issues that I never wanted to be bothered with. A rebase that can't just fast-forward? That's a job for Claude. A merge conflict that really shouldn't be a problem? Claude deals with it. These are things I never wanted to have to learn in the first place, and I don't feel like reserving a part of my brain to remember how to deal with these things is worthwhile. In almost all cases, there is no actual decision that needs to be made, it's purely a mechanical process.
That you know what that is makes you the right kind of person to be delegating it to an LLM; you can be trusted to use the LLM for this sort of thing because you understand what the problem is, what the right outcome should be, and how to know when it's been done correctly.
It's the person sitting behind you who doesn't understand any of those words who is going to do something dangerous at LLM-accelerated speed I'm concerned about.
I feel like if the logic in git were just a bit more sophisticated, many of these issues wouldn't even arise. Most of these are because someone foolishly (usually me) merged in the wrong direction for convenience's sake, and now I have a duplicated set of commits. It's not like Claude is doing anything very sophisticated; it's just looking at commit hashes and seeing what the unique changes are.
Former Google employee here. This is exactly the kind of shit-for-brains action I'd expect from Google executives. Bravo on further dragging your image through the mud.
This article captures so much of what I have felt but been unable to put into words about GA. I come from a computational physics background, and when translating theory into numerical algorithms, the dimensional analysis and units are very important (you have to be able to relate the simulation to something in the real world!). GA dispenses entirely with any notion of meaningful units, making the dimensional analysis and error checking extremely difficult. The geometric product has always seemed like some strange mathematical trick or coincidence that happens to maybe have some useful properties. Almost like how "new math" is perhaps easier to learn or understand at first, but you really just need to sit down and understand algorithmically what is going on with the basic arithmetic operations.
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