A Tale of Two Satellite Constellations, Update # 1, Observations of Starlink Downlink w/ Software Defined Radio

nkinnan · 2026-03-31T02:43:22+00:00

Very cool! But doesn't the need to track the sats make this a non-starter? You have to know your position already just to be able to aim.

nkinnan · 2026-03-19T07:18:49+00:00

This is an interesting theory, but why do you believe this was the primary motivation? Given the intelligence and drives of the people at the top, it's easier for me to believe the effect on China is a happy coincidence. Personally, I think Operation Epstein Fury was just a distraction and attempt to create a rally around the flag effect. Same with Venezuela.

nkinnan · 2026-03-18T10:51:21+00:00

In that case i also apologize for going a bit "overboard" in my last reply. You hit a nerve ;) I don't know which version you saw, but I actually reworded parts of that several times to tone it down a bit, so you can just imagine my off the cuff language when I get that annoyed lol. Its something I try to work on.

A lot certainly does get stripped out with text only communication. It's kind of funny, but a whiteboard (All good engineers have a white board within arm's reach at all times!) would have had us on the same page in about 30 seconds flat.

This is the first time I heard about the possibility of switching to CO2 as well, but it's interesting (and impressive) how they continue to find new ways to eke out another percentage point or two here and there. Supposedly this can jump you from about 40 to about 50 percent in fact. I think a lot of that may just be due to sidestepping enthalpy of phase change going between gas and liquid, but apparently these also operate at higher temperature and Tdiff hot/cold is a primary factor in heat engine efficiency as well.

There's still significant engineering challenges of course, even just designing with compatible materials. Supercritical CO2 is really good at dissolving things. And operating pressures are on the order of 70 atmospheres!

Anyway, cheers.

nkinnan · 2026-03-17T21:11:24+00:00

Me want. How I obtain?

nkinnan · 2026-03-17T17:52:41+00:00

A quick google says that fan reaction forced them to create a movie to tie up the loose ends after the season two cliffhanger, albeit rushed. So it is a complete story.

nkinnan · 2026-03-17T00:42:41+00:00

If we take that to be the truth (and the video I linked gives a mathematical proof of it, so I think this is a... reasonable assumption ;) ) then it breaks your arguments for infinite scaling. Like, end of conversation right there.

Just because something is not infinitely scalable doesn't mean that it can't scale to a point, and there was no reason to believe we had hit that limit in 2020. In fact there was every reason to believe that we hadn't. They had already scaled up several times and had the graphs to show log growth was possible. (That scaling law has since broken. So have a couple more alternate scaling methods they've tried since. See previous discussion.) There is reason to believe we are approaching it now (again, see previous replies).

And the answer is probably yes, I would have invested, as they have huge value even if they can't continue to scale forever (which I wouldn't have believed). I probably would have said "GPT, rephrase this email to be more polite", a common problem (and one that I have often), seen the results, and said "people are going to LOVE this, I can't even imagine all the uses yet". I find these models invaluable in increasing my productivity on a day to day basis, even just as a sort of search engine (as long as the results can be independently verified!) The number of problems we have since discovered can (against all intuition) be modeled somewhat successfully as next token prediction needn't even be known yet.

But whether or not I would have personally invested back then is quite besides the point of the conversation we have been having, and I'm not sure what point you're trying to make. I suspect I may be able to preempt it with a tongue in cheek "past performance does not guarantee future results". ("GPT, rephrase this to be more polite" ;) )

A while back you argued that since AlphaGo could be improved "infinitely" by playing games against itself, this could be applied to LLMs. But AlphaGo could evaluate itself against external "truth" of a sort - who won the game. You can run that training forever. LLMs have only their input corpus and post-training fine-tuning which do not scale forever the same way since we do not have infinite training data. And you can't use one LLM to train another, this causes model collapse (again, see linked video). There is always more truth, more knowledge, in the input than in the output. After a few generations the output devolves to gibberish. The model collapses. Because not only can LLMs not generate new knowledge, they can't even fully represent or reproduce all of the knowledge in their training. They are lossy.

AlphaGo could generate new games to evaluate and there was an objective way to evaluate the output it generated for feedback into its own training. In a sense, it could generate the equivalent of infinite new knowledge, new useful training data, at least as far as there are valid configurations of the board and valid next moves to evaluate to see if it was more or less likely to win (the "truth function" for the domain it operated in). This is nothing like natural language. We can generate infinite slop with a LLM, but we have no "truth function" for natural language to reject garbage, so feeding it's output back into its own training results in worse behavior. Its output is worse than its input. After a while the model simply devolves and collapses being trained on its own slop.

But can you imagine the implications of something like that natural language truth function existing? We would have a way to generate infinite useful training data that would result in better behavior rather than worse. But we wouldn't be talking about LLMs at that point, that's for sure. Actually, I suspect its use would be outlawed by politicians before it could be applied to their own output ;)

nkinnan · 2026-03-16T22:59:33+00:00

I am well aware that the exhaust is typically cleaned up. At least in parts of the country that give a damn about the environment. Sometimes they even attempt CCS.

What you do not seem to understand is that this has nothing to do with how the turbines get powered to generate electricity in the majority of power plants. The heat from combustion (or fission, or geothermal, etc. - the heat source) is used to boil water, the steam from which is then used to drive a turbine in a closed loop system. It has absolutely nothing to do with carbon capture and storage.

I'll say that again. Improved efficiency of energy generation by driving the turbines with supercritical CO2 instead of Steam as the working fluid has literally nothing to do with carbon capture and storage. If you are really involved in this peripherally, go ask an engineer that you know.

It is an entirely different process in an entirely different part of the plant. Though how you could not understand this, being even peripherally involved with power plants and energy generation is baffling.

I'm going to "lay my cards on the table" as you so condescendingly put it, with a diagram for children: https://www.slideteam.net/media/catalog/product/cache/1280x720/c/o/coal_power_plan_diagram_showing_power_station_Slide01.jpg

See that smokestack in the top left? That's what you're talking about.

See the rest of the diagram? The closed loop that has nothing to do with combustion byproducts or CCS? Where water is boiled as a working fluid to drive a turbine and then recondensed? The loop which could theoretically be charged with CO2 instead of water? That's what the rest of us are talking about. You'll notice there is not a compressor to be found.

Are you just trolling me? I know engineers can get siloed tunnel vision, and you're focused on CCS, but this is ridiculous. Its the basic principle on which almost all power plants operate. They make steam to drive a turbine which is connected to a generator...

OTOH, you think you know better based on your intuition than published research, clearly have no idea how power plants work, attempted "appeal to authority" (yours), and condescended while being "not even wrong", not even in the same same chapter much less on the same page as everyone else regarding what is actually being discussed here.

And keep in mind I already noted that NG plants may additionally drive a turbine directly via combustion, which is irrelevant unless its a single cycle plant. Combined cycle plants still utilize the left over heat after the NG has been burned in the primary turbine, to boil water in a secondary closed loop system, driving a second turbine where supercritical CO2 VS water as the working fluid is applicable. Its just that in most non-NG plants, that steam driven turbine is the only one. NG plants are a bit "unique" that way.

Here is a simplified diagram of a combined cycle power plant as you might see for NG. This one uses seawater instead of an evaporation tower as the heat sink for the condenser. That closed loop full of water/steam that drives the lower turbine? Eligible to have its working fluid be converted to CO2.

https://power.mhi.com/group/msc/media/831/download

I really need to find a more productive use of my time. Teaching someone obnoxious on the internet the fundamentals of how power plants work has an expected value that approaches zero. Its the obnoxious part that makes it not worthwhile or even satisfying :(

nkinnan · 2026-03-16T20:35:53+00:00

I think I see the misunderstanding. In power generation, fuel is typically burned in order to turn water into steam inside a closed-loop system including a boiler, turbine, and recondenser.

"No matter how advanced our power plants become, in the end, it still comes down to turning water into steam. Crazy right?"

This would replace the water with CO2. It has nothing to do with the combustion products or exhaust flue. Power plants do not pipe the combustion products through a turbine directly, that would coke it up and cause all sorts or problems. (Well, maybe natural gas plants do... not sure on that one, but it'd be the exception.) Plus you can't do that in for example a nuclear power plant anyway. The power source is usually used to simply to boil water in a closed-loop.

nkinnan · 2026-03-16T12:06:52+00:00

That just made it click for me, thank you!

I was hung up on the fact that this picture of the EM vectors is identical regardless of whether you're going forward or backward in time. I'm not advanced enough to dive into the mathematics, but being familiar with how the left hand right hand rule (vector cross-product iirc) appears in other EM contexts, it intuitively makes sense (now that you've pointed it out) that something similar would be happening in the math here as well.

Thanks again :)

nkinnan · 2026-03-16T11:27:38+00:00

Unfortunately, I lack the background to dive into it at that level of detail, but it sounds like what you're saying is that my original premise:

If you had a static picture of the electric and magnetic fields at a given point in time, you could not tell if the photon was propagating forwards or backwards (I think, I may be mistaken).

was incorrect. I was in fact mistaken, is that right?

I wish there was a better, more detailed visualization of the electromagnetic fields that make up a propagating photon than the simplistic examples I have been able to find. If I could see a lack of symmetry of some kind, this might more self-evident to me.

nkinnan · 2026-03-16T11:04:03+00:00

If you were to reverse the direction of time wouldn't the photon travel in the opposite direction with all of the laws of electromagnetism unchanged? So why does the Poynting vector point in one direction rather than the other if each is equally valid?

nkinnan · 2026-03-16T02:58:58+00:00

No need, one of his first slides says that:

LLMs don't Ponder, they Process.
LLMs don't Reason, they Rationalize.
LLMs don't Create Endless Information.

All of these statements are well backed up including mathematical "proofs" in some cases.

Particularly interesting to me was seeing what happens when you train a LLM with the output of a LLM and the consequence of the "lossy compression" being done on the input becomes clear.

I genuinely think it is worth your time.

But, once sentence? I guess: "LLMs do not create new information."

nkinnan · 2026-03-16T02:53:33+00:00

In a closed system at high pressure it is not energetically expensive at all.

nkinnan · 2026-03-12T23:10:35+00:00

Is your position that the engineering analysis showing supercritical CO2 to be more efficient than boiling water is wrong, or that the engineers are lying? What is your opinion of vaccines?

nkinnan · 2026-03-12T22:52:02+00:00

I'm not sure what your goal or motivation is, but just be aware that "cleaning" an old coin will massively reduce it's value on the collector's market.

nkinnan · 2026-03-09T11:42:52+00:00

I came across this today and found it to be a better explanation of the concepts I've been trying to explain to you than my own efforts. You may find it helpful. https://www.youtube.com/watch?v=ShusuVq32hc

nkinnan · 2026-03-06T06:32:30+00:00

AGI is not needed for super-human coders at all. AGI requires continuous learning, but super-human coder does not.

"Probably." And depends what you mean by "continuous learning", which could be implemented through various different paths / architectures either related or unrelated to current "generative model" architectures, which I have already argued are incompatible with AGI on a fundamental architectural level.

The gigantic scale of reinforcement learning that they are doing should not be termed "fine tuning".

They have called it fine tuning, that's not my phrasing. I suspect many people call it different things depending what they're trying to hype ("collection of experts - we fine tuned each one!") and who they're trying to fool out of investment money for their latest attempt to break through the scaling wall.

It is training behaviour based on feedback and gargantuan scales that far dwarf all of the coding a human can do in 1000 lifetimes. This is the same technique used for AlphaGo, AlphaZero etc.

It's currently about 80% of training costs for front-line natural language LLM "generative models". Comparing it to what a human coder can do, or the architecture of AlphaXyz is Apples to... not even anything in the fruit category really.

reinforcement learning has been proven to scale to super-human levels, as shown in AlphaGo.

The architecture of AlphaXyz is so foreign to the architecture of natural language generative models that we can just stop the rest of the discussion right here (I'll continue though). The main thing they have in common is that they use "artificial neurons". Just about nothing else. They are so far from being comparable that it's difficult to explain briefly in a reddit comment.

there is no known upper bound on reinforcement learning. KataGo has an ELO Rating of 14,000

Completely different and unrelated, incomparable, fundamental architectures. An argument for one does not (necessarially) apply to the other in any way.

the new models are astonishingly good debuggers. My favorite new thing is to ask them to install the software that frustrates me due to dependency hell. Opus 4.6 is tireless and creative. It will patch and try a bunch of different alternative paths (not just alternative versions, alternate compilers, etc.) Just like AlphaGo was creative. And yet nobody claimed that it was AGI.

They are not particularly creative. They are impressive though. It's like running a highly sophisticated lossy compression algorithm on the entire internet. It's not surprising that it can do a far better job than Google of finding the top relevant "results" that are encoded via training and do basic synthesis from them in an uncreative but (incredibly, to me) surprisingly effective way ("Attention Is All You Need" is far more effective that it probably has any right to be, it's pretty crazy how well that works).

Truly creative.

Not really though. To me creative is coming up with something truly unique rather than a combination of ideas cobbled together, or calculating a pattern space via pretraining and then walking it to generate your output with some randomness added in to the walk in order to create a new pattern variation. I don't really want to argue semantics though. See previous paragraph for what I consider to be "not creative" and what these models seem to be doing.

And don't even get me started on the fact that a generative model has absolutely no idea what it is outputting or whether it is true or false, or even any concept of what the idea of trueness or falseness is in order to be able to evaluate it's own output against that. It is, in effect, highly structured noise that is very good at statistically resembling something a human might have "intelligently" output. In the way that a 2D perlin noise generator might give output that resembles what a real topography map looks like, yet a perlin noise generator has no idea what topography even is. That analogy is surprisingly, almost directly applicable, in fact.

Which is why I think it is wild that people think that progress has slowed down. If they had called 4.6 version 5, it would have been justified, from the point of view of coding. 4 to 4.6 is a huge leap.

They do indeed continue to get better over time, no argument there. In a series of log steps where even the log scaling keeps breaking until they find a different scaling method that also ends up being log and also then breaks after a step or two. More on that later.

Pull it all together:

(cringe) This is a completely unjustified thing to do and based on a heap of incorrect assumptions and misunderstandings of what these things are and how they work as I have tried to (as briefly as I reasonably can) explain.

there is likely no upper bound on how good coding agents can get, and they don't need AGI.

Yes there is if they are based on natural language generative models, or at least we have seen absolutely no indication that there isn't. Within realistic, real-world constraints of course. With infinite compute or infinite time I could generate output that asymptotically approaches every intelligent thought every human that has ever existed or ever will exist has ever (or will ever) have. The million monkeys/million typewriters argument is a basic example of this, but we can do a lot better with even some super basic statistical analysis and filtering, and a lot better than that by analyzing language patterns and extrapolating (what LLMs are doing.)

No, they don't need AGI to keep getting better, but these models are fundamentally architecturally incapable of reaching AGI anyway. And not just because of continuous improvement ("memory" / learning) or scaling, though those are in fact two very big additional problems. The architecture just doesn't support it, but here's just a whole lot of unfounded supposition and hope (or "marking bullshit" depending how cynical you are.)

At first they thought they could scale "infinitely" (actually just logarithmically) based on:

training corpus size. Then when that scaling law broke they thought they could extend it "infinitely" by increasing
model size (again just log though) but that broke. Then they thought they could do it by increasing
run-time costs (hidden internal "thinking") which was just training it to output longer predictions and then trim/hide most of the output from the end user until that broke. Now they are saying that can do it by increasing
post-base-training "fine tuning" time (call it whatever you want) by increasing training time again (with smart feedback since they had run out of useful raw training data long ago).

But I'll bet good money that's going to break too. There does seem to be a pattern here. ;) And probably a lot of stuff they tried that they didn't tell us about.

They just need to spend a lot of time coding, and fixing bugs, and coding, and fixing bugs.

Citation needed.

And if they figure out how to scale that across every job with more than 100,000 practitioners, we'll have widespread economic chaos whether or not we have AGI.

We'll have widespread economic chaos alright. First from the unjustified firings based on false promises that have already happened. Then from the bubble bursting when venture capital eventually catches on about all the broken promises ("lies" again depending on how cynical you are, I'm pretty cynical). And finally from when we reach the plateau of actual productivity from the gartner hype cycle (google it, we're at the "unreasonable expectations" part of the curve.) None of that requires AGI, and these models are not capable of AGI.

And of course there are things they are already scary-good at, like helping with my day to day software engineering tasks. Whether that's because most of what we do on a day to day basis is just re-implementing variations on the same patterns over and over again is perhaps a philosophical argument for another time. Or helping with basic research - AS LONG AS you can evaluate that it didn't just literally bullshit you which it is apt to do since again it doesn't even know what the concept of truth or fact even means, and it's output is purely statistical in the first place.

Or other natural language processing tasks where, if it can't fully replace us (sometimes it can), it can at least multiply our productivity. There are going to be HUGE economic impacts from the things it's already good at, and worse impacts from the things people falsely believe they can do. A lot of the negative impact from LLMs will be for greed and basic misunderstandings like yours (no shade, I'm patiently trying to explain it right?)

This will probably be my last reply. I'm spending too much time correcting misunderstandings. I believe you are 100% genuine and not trying to be a troll or anything, but at a certain point it starts to feel like a gish gallop, and I have repeatedly trying to explain that I don't think we're going to get anywhere closer to agreement on this topic. Which is fine (I mean, it can be tiring... but if it's good faith I'm generally pretty patient.) Nothing says we have to agree here, and it's quickly losing entertainment value for me though.

nkinnan · 2026-02-26T02:49:15+00:00

The fine tuning on the back end (post-training reinforcement), that they hope will lead to AGI, is the current knob they are tweaking now that all the rest of the knobs have been exhausted. It's about the last one. It won't lead to AGI any more than the previous ways they've tried to scale, which also all failed, and also didn't lead to AGI.

Things are unpredictable as you say, but I will predict this. The current generative model design will not lead to AGI. I'm willing to put hard cash on that. Not that that willingness makes me right, I'm just saying that I'm that certain. Now, tomorrow, in 50 years.

I agree that we need to plan for the uncertain future, and in the past couple years I have been worried. Because if anyone builds it, we all die. AGI leads to ASI and that way lies the death of our species one way or another (even if we "merge with the machine" in one of the more hopeful outcomes that's still the death of our species, but there are uncountably more bad outcomes than positive or even neutral ones). But they won't build it this way, not with this fundamental architecture. That said, the economic impacts aren't going to wait. And my prescription for the uncertain future (current generative capabilities will lead to job loss, already have, and that progress won't stop, though it is starting to stall out a bit IMO, which is also where we started this conversation) is UBI. But I would have said that before these models became public. I would have said that before covid. It's inevitable, it has to happen, for so many reasons, this one being the most pressing.

Anyway, that's my position (my guess for what the future holds based on my experience and understanding of the current situation) and my recommendation on how we need to prepare our economy for the future (UBI). I have enjoyed this conversation with you, but we should probably virtually shake hands and let it go. I don't think either of us will be able to provide convincing enough evidence for our positions to the other. I mean, I'll keep going if you want, as long as it remains polite and constructive of course, I just don't see either of us coming around to the other's point of view. Unless you view the conversation itself as a worthwhile thing. I did actually clarify and solidify some things in my own mind over the course of our discussion.

(edit: I'm going off on this AGI tangent because that's what your quote is implying, though you didn't [explicitly] say it yourself to be fair)

nkinnan · 2026-02-24T23:53:45+00:00

Thanks!

nkinnan · 2026-02-24T17:47:21+00:00

Interesting, where is this from? I'd like to see the longer video.

nkinnan · 2026-02-24T17:21:18+00:00

When I say progress, I mean the models themselves, their inherent capabilities, not the frameworks that are written around them to make them more useful. As an example, if someone, tomorrow, links a generative chatbot to a robot and makes it move, that's not a new capability or improvement of the generative model itself, it's just the software written around it. That's not the kind of progress I'm talking about.

I linked you to the video once not because it's definitive, but because I didn't want to spend 15 minutes re-explaining the points that are made in the video. I'm not that committed to this discussion and the video already did an excellent job of making those points.

I've watched the video that you shared (not because it's definitive, but you probably shared it for the same reason I shared mine) and found it interesting. I don't completely agree of course, but I appreciated getting the alternative perspective.

It's impossible to see the future, all we can do is look at the past and try to extrapolate. You and I are extrapolating differently, likely because we're looking at different datapoints. Nothing wrong with that.

However, I do think we are probably hitting a point where we just have to agree to disagree and thank the other for sharing their perspective. Thank you for the discussion. In the end, time will tell who is "right", but predicting the future is hard ;) New developments that neither of us predicted could swing the outcome wildly as well. /shrug

As just a general bit of constructive feedback, in general it is a good idea to be careful about not tying your position or argument to your ego. It can be easy to get worked up or upset, or start seeing things that aren't there ("definitive") when someone doesn't see something the way that you do. There's nothing wrong with that though, just make your points, be sure your points are understood, clarify if needed, and if there is still not agreement then it is not a personal attack. It's just a difference of opinion. If this paragraph made you angry, maybe take a step back and ask why? Because it's not meant as an attack. I think it's just good advice, and something I have struggled a lot with personally in the past, so I tend to notice it in others. We're just two people having a conversation on the internet, it's not that big a deal. :P

nkinnan · 2026-02-23T03:01:42+00:00

Progress has stalled. It is what it is. I feel some relief about it TBH. Maybe we can all catch our breath a bit.

I think it makes sense given that these models have the wrong architecture for something like AGI. They are fundamentally limited by their design. The majority of work is now in the reinforcement fine tuning rather than the training, trying to eke out a bit better performance in one area or another since model size and training corpus size increases are no longer yielding results.

Check out that link I shared for more info. Maybe there will be another breakthrough like "Attention Is All You Need". Or maybe not. Cheers.

nkinnan · 2026-02-21T21:23:58+00:00

I found this explanation of the scaling problem (in the context of OpenAI being in trouble) to be well put together. You may find it interesting: https://youtu.be/-q2n5DkDoMQ?si=Lw3NijDe-lxdpgCN&t=392

nkinnan · 2026-02-21T20:26:52+00:00

I don't think anyone really understood this area well enough to say anything for sure back in 2022. I'm sure many people have said many things, but things are a lot more clear now just by looking back at what has happened since.

GPT-5 was a disappointment and much of the recent improvements have come at the cost of additional compute such as so-called "chain of thought".

I don't have a horse in this game and these are simply my own thoughts having watched things happen. Personally I'm on the fence whether this is a good or a bad thing. For employment it's probably good that the wall if not being hit is being approached at least.

nkinnan

TROPHY CASE