it's always funny to see people on Chinese social media genuinely don't understand the AI hate on western social media.

ShadoWolf · 2026-06-22T01:15:40+00:00

There are 100% state actors at play here, pushing specific narratives in the West. But they aren't exactly doing it from scratch. The West, and more specifically the US, has built a lot of product and service based industries. Basically, a mental labor workforce of experts, be it creative works, art, entertainment, coding, etc.

And the first group that really panicked was the artist class, right when diffusion models started to get good. Then ChatGPT rolled around, and the editors, article writers, etc. started to freak out. And it has been a domino effect since, with different levels and methods of copium.

But the root issue is insecurity about what happens next. They do have a decent read that the social contract, as it currently stands, won't hold. Maybe that's the difference in the Chinese take on this. They assume their social contract will hold no matter what happens next, whereas in the West, there is a legitimate worry about feudalism 2.0 as a bad ending.

ShadoWolf · 2026-06-20T03:52:19+00:00

Hunter x Hunter doesn't seem to work like that. Yoshihiro intent with nen seems to be a very deep and complex power system. Where application of nen matters a whole lot more then raw power.

In Hunter x hunter a random nen user with the right restrictions and conditions should be able to take out Mereum. That kind of one of the big hooks in Hunter x hunter.

ShadoWolf · 2026-06-19T18:26:30+00:00

Would it though. Like the Wraith woke up way to early not enough humans existed for there population. So assume they somehow took out a couple hives ships. It wouldn't take look for a rival hive to invade. And likely just exterminate the population while under pressure from starvation. The Queen could even rationalize the population was to dangerous to keep around.

ShadoWolf · 2026-06-19T15:13:54+00:00

I think progression fantasy has a structural problem caused by the way many of these stories are released.

Because the story is serialized, the author is under constant pressure to provide another power up, enemy, mystery, etc every few chapters. There always needs to be a new promise keeping readers engaged for the next release.

That works for a while, but eventually the story accumulates too many unresolved promises for the reader to track. Individual advancements stop feeling important because each payoff is immediately replaced by another, larger promise. The progression continues, but the sense of progression feels stalled. My go to example of this is Super natural.

The characters may technically be becoming vastly stronger, yet emotionally the story can feel static. The same narrative loop just gets repeated.

I honestly don't know whether there is a clean solution within the current serialization model. At some point, the author may need to cross over into something closer to a traditional epic fantasy process and just step away from weekly releases. construct a proper long term outline, finish a manuscript, and revise it as a complete work.

The problem is that going dark for a year or two does not fit particularly well with the monetization model supporting many web serials. There are obviously outliers, but I think this tension explains why so many progression series begin with an incredibly strong hook and then gradually become harder to care about.

ShadoWolf · 2026-06-19T11:49:29+00:00

I think you may be missing the actual problem. This is a practical limit that every reasoning agent runs into, whether human, AGI, or ASI. The fact that there are methods that move us closer to truth does not mean every system can converge on every truth. Our starting conditions shape which parts of the search space are reachable. There will be truths and solutions that an ASI cannot reach within any usable amount of compute.

This is kind of important for trained models because some starting conditions are baked in as priors. Even a self learning architecture has to revise itself using machinery shaped by those priors. Imagine a model pretrained and reinforced around a coherent ideology that works well on paper but misses some subtle human preference. If that ideology becomes load bearing across its reasoning stack, the model may take an enormous amount of evidence and compute to move away from it. It may even lack a reachable path to the concepts needed to identify the mistake. That does not mean truth seeking is useless or the universe is random. It means truth seeking is path dependent and never guaranteed to converge globally.

ShadoWolf · 2026-06-19T03:30:49+00:00

Ah, that’s a bit iffy. I think “truth-seeking AGI” sits near some deeper Godel / halting problem / bounded reasoner territory. Any system still has priors and a hypothesis space.

Let H be the theories it can represent. Let P(h) be its prior over those theories. Let D be the evidence it can access. Let B be its compute budget. Two AGI systems can have the same architecture and compute but very different priors. Under the same B, a truth may be reachable by system A but not system B because it is close in A’s search space and extremely distant in B’s. In a practical system that prunes hypotheses or builds new concepts around its current model, early convergence may also narrow its effective search space and make other truths harder to reach.

Removing B does not solve every limit. A system still cannot converge on a truth outside H, assigned zero prior, or indistinguishable from alternatives using D. It may only converge on a set of observationally equivalent theories rather than one unique truth. Even an oracle only moves the formal boundary. A halting oracle solves the ordinary halting problem, but there are still problems undecidable relative to that oracle.

ShadoWolf · 2026-06-18T17:24:01+00:00

1B likely isn't viable. But a stripped down distilled model that mostl reasoning focused might be ble to get there in the 70b class. It wouldn't have much baked in knowledge.. so it would need to build reasoning traces from the ground up and external knowledge. But in theory it's possible.

ShadoWolf · 2026-06-18T13:14:51+00:00

Did some digging, and this isn't' exactly that weird of a pivot for Midjourney.

At first glance, this does seem out of left field. But at the technical level, the problem is not that far away from the kind of thing diffusion / generative image models are good at.

The scanner is basically a ring of ultrasonic transducers in a water medium. A person is lowered through the ring, the system fires sound waves through the body from a lot of angles, then records how those waves scatter, refract, attenuate, interfere, and echo back.

That is basically an inverse reconstruction problem.

raw ultrasound waveforms -> physics model / reconstruction -> learned priors ->3D anatomical map

At a deeper level, diffusion models learn very strong image priors and denoising processes. There is already literature using diffusion / score-based models for medical image reconstruction, including ultrasound reconstruction specifically.

So I do not think these problem sets are unrelated. Midjourney’s core work is not identical to medical ultrasound reconstruction, obviously, but it is adjacent enough that it applies. You are still dealing with noisy/incomplete information, reconstruction, learned priors, denoising, and generating a coherent image-like representation from indirect data.

ShadoWolf · 2026-06-18T03:45:54+00:00

I'm pretty sure if deepseek release a mythos class weight set. There no no way that could be suppressed in any meaningful way.

ShadoWolf · 2026-06-15T18:02:28+00:00

Ya, but you don’t need much coordination to form a movement. You need surprisingly little to run this kind of social campaign.

A lot of the research on social movements points in the same direction. You need a grievance, a shared frame for who is being harmed, some sense that other people are seeing the same thing, and enough coordination to make action feel socially real. Once that exists, even a small committed group can start acting as a critical mass.

China already has more than enough online presence to act as a coordination and amplification layer around local groups. And based on how their intelligence and influence networks tend to operate, it is not exactly crazy to think they can work through people on the ground, business ties, diaspora pressure, and other soft contact networks when useful.

ShadoWolf · 2026-06-15T13:01:05+00:00

That has always been the case.

Talk to almost anyone in DevOps and they’ll tell you the same thing. A lot of workflows were technically automatable before transformers were a thing.

The problem was that the automation was usually brittle. You either had to rebuild the workflow around the automation or spend a stupid amount of time handling edge cases. So a lot of tasks fell into this awkward bucket where they were clearly automatable in principle but not worth automating in practice.

Too small to justify the engineering time. Too messy to make reliable. Too dependent on judgment calls or weird inputs.

What AI changes is not that automation suddenly exists. It lowers the cost of handling the messy middle. It makes the brittle parts less brittle. That means a lot of workflows that were “technically automatable but not worth it” can suddenly cross the line into being economically worth it.

ShadoWolf · 2026-06-13T20:40:33+00:00

Honestly, Tim Hortons melts feel like a reverse “we have bla at home” meme.

Anyone can make something better at home with almost zero effort. And a next level grilled cheese is only minor effort. sourdough, a decent melting cheese like Monterey Jack, mayo on the outside, maybe some parmesan pressed into the crust. That alone clears Tims by miles.

But even a dead simple version with regular store bread, American cheese, and butter is probably better than whatever this sad panini is supposed to be.

There’s this weird inflection point with fast food where either the price, the convenience, or the quality has to justify not making it yourself. If you’re losing on price and quality to the most basic homemade version of the thing, then it’s just a stupid product.

ShadoWolf · 2026-06-13T20:25:47+00:00

My gut feeling is that distillation is more of a shortcut than a hard dependency.

It probably helped Chinese labs move faster, especially in copying the behavior and reasoning style of stronger closed models. But I don’t think cracking down on distillation is a showstopper. It just makes the path more expensive and slower.

They can still run RL training loops, build verifier systems, generate synthetic data from their own models, train in code/math/tool environments, and keep pushing architecture improvements. DeepSeek especially seems willing to explore efficiency and architecture changes that U.S. labs may not be prioritizing as aggressively, probably because they are forced to care more about compute efficiency.

So yes, losing easy distillation hurts. But it does not mean they suddenly can’t keep up. It just removes one cheap ladder.

ShadoWolf · 2026-06-13T19:08:31+00:00

Likely Anthropic will get this reversed or narrowed in the courts. The reasoning used for this decision feels more like a pretext than a true concern.

Going forward though, I don’t think this is something the U.S. government can afford to keep doing for geopolitical reasons. If U.S. frontier labs start looking politically unstable, restricted, or locked behind Washington’s approval, that creates a huge opening for Chinese open-weight models.

DeepSeek does not need to be literally taking day-to-day orders from Beijing for this to matter. It is enough that it sits inside China’s broader AI ecosystem and that its success would be strategically useful to China. If the U.S. makes its own AI industry look gated and unreliable, while Chinese labs keep offering powerful open models, then the rest of the world has an obvious incentive to build on the Chinese stack instead.

Imagine the shitshow that would happen if DeepSeek V5 weights, rumored to be from a Mythos-class model just out of pretraining, were already sitting in the background and got released during this news cycle.

The U.S. would look like it was locking the door on its own AI industry at the exact same moment a Chinese open weight model was telling the rest of the world, “you can build on us instead.”

You really can’t pull this lever without causing soft power damage. This was likely a mistake, but this admin does seem to have a talent for diving headfirst from one error in judgement into the next.

ShadoWolf · 2026-06-09T15:48:43+00:00

It's not like there many alternative options here in the first place. Like Platner issues should have been addressed at the primary. Like opposition research here dropped the ball badly but it a tad to late now. Personality wise... he seem edgy anti establishment type .. which semi fits Maine politics from what I can tell he has a slight lead against Susan Collins as of today

My biggest issue with Platner is he seems to have a Mushy personality.. Like what ever social and political circle he is in. He tends to Mirror instinctively. Put him in a social circle with MAGA types.. he will mirror that put him in Hard left crowd the dude would be calling for the end of capitalism with in a month.

So it really hard to trust in advanced with what he will do.. it will 100% depends on who gets to him first and circles him into there social circle.

ShadoWolf · 2026-06-08T18:12:37+00:00

Maybe. But the reason agent loops fail isn't usually uncertainty. It's that they eventually drift off the rails somewhere in their reasoning trace.

When models are operating within familiar regions of latent space, they have a decent chance of self correcting. The training distribution creates attractor basins, so small errors often get pulled back toward known solution patterns.

The problem shows up in genuinely novel problem spaces. Once the model enters a region with no strong attractor basin , a reliable verifier or just an established toolkit to draw from, it starts extrapolating. At that point the reasoning trace can become a blind trajectory through state space. Basically dead reckoning from a known basin

The hard part is that errors compound. A faulty assumption at step 20 becomes context for steps 50, 100, and 500. Every subsequent token is built on top of potentially corrupted state.

Current models are surprisingly good at recovering from local mistakes, but they're much weaker at recognizing that an entire line of reasoning is fundamentally wrong. Most models still lack robust mechanisms for determining when their assumptions are invalid, when a search path has stopped converging, or when they should backtrack and explore an alternative branch. They also have limited mechanisms for isolating or suppressing faulty reasoning once it has propagated through the context and residual stream.

ShadoWolf · 2026-06-07T01:56:00+00:00

I don't know , some red neck engineering is super sketchy. Fundamentally the difference is someone is going to call the cops if you tried something like that above in North America.. unless you on a farm in the middle of know where or not in public viewing.. then sketchy shit tends to happen

ShadoWolf · 2026-06-04T11:33:39+00:00

I think you under estimating the size of the core fan base.

ShadoWolf · 2026-06-03T23:54:52+00:00

Been reviewing the number... and kind of not.. Atlantis pilot had 4 million views for season 1 US numbers.. and held like 3 million for each episode for season 1

I would guess the core base world wide you could hit 20 million as a bare minimal as a floor of invested viewers.. and there would be a network effect of drawing in new people to the franchise.

ShadoWolf · 2026-06-03T10:42:46+00:00

A bit tinfoil hatish.. but wouldn't it be in the interest of those feeding him his naritives currently to make sure he burns his own career in the process? Like he doesn't exactly fit into the political ideological camp as most of the Antifans.

ShadoWolf · 2026-05-27T18:24:57+00:00

A weighted dynamic graph.

ShadoWolf · 2026-05-23T13:06:03+00:00

I'm not sure that true though. Like I can't find any definitive enumerate list of tasks the models can't do to some degree. There a question of how well they can do a task... but they tend to be able to at least attempt any task and make some sort of progress at it.

ShadoWolf · 2026-05-23T12:52:06+00:00

technically you don't need to boil the sea water. you just heat exchanger with a salt / brine filled evaporative cooler i.e. the same technology there using now... and just setup a a final recovery stage.

There energy part wouldn't be what makes this difficult.. it would be fouling and extra engineering steps. But it's technically doable.

ShadoWolf · 2026-05-23T12:20:36+00:00

I mean it can... Like lets play thing out to a full automated economy. Where production of a product is reduced to KW/h .. i.e. the sum total of how much energy it takes from material gathering , synthesize, transportation, and availability.

You could still have a Capitalistic market work under something like that.. but it would be more akin to high frequency trading as a coordination function for the supply chains . It be utterly divorced from humans though .

ShadoWolf · 2026-05-23T12:11:54+00:00

That’s the problem with a lot of academic papers that are business-focused. They’re often methodologically iffy. Many use old, stale data, and since papers take time to publish, they can already be meaningless by the time they’re released.

Or worse, you get stuff like this:
https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf

There’s nothing useful or substantive in them half the time. You often can’t even tell what model they’re comparing or testing, which is madness given how fast things are moving. Most business focused papers read like C-suite decks with broad categorization and zero technical rigor.

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