This is absolutely still shy of Sol and Fable, but only just by a hair. Ridiculous results. There's still not a compelling economic reason to drop OpenAI courtesy of the ludicrous reset addiction that's taken place, but it feels like we're on the precipice.
How are you all toying with running this kind of thing in a mega quantized way locally? Two weeks out from released weights, but this is still just GLM 5.2 with post-training magic.
OpenAI and Anthropic are both seeking trillion IPOs, while Chinese labs are pumping out open-weight models that are free for US providers to host and monetize.
These Chinese models cost less of US SOTA models to run, even if they are less capable. Providers can just run them, offer cheap tokens, and pocket the margin.
I just don't see how you justify a trillion valuation for US AI labs when the underlying models are being commoditized this fast.
Whether AI works or use useful or not isn’t even the question anymore. It can fulfil every promise Sam Altman has been making and will still make no financial sense to justify these valuations.
Another interesting potential market here will be 'LLM in a box'. All the hardware and other tooling in a prebuilt, but modular, package ready to go. Pay one up-front cost, get a system running [whatever open LLM] with a token rate of [x], optionally configured to be immediately ready for distributed usage. Basically the opposite of cloud stuff: no rent, no dependency, 100% guaranteed uptime, guaranteed security/privacy (at least subject to your own actions), and so on.
Palantir already offers a "turnkey AI datacenter", i.e. a rack with "NVIDIA Blackwell Ultra systems with eight NVIDIA Blackwell Ultra GPUs and NVIDIA Spectrum-X™ Ethernet networking for AI training and inference".
It is said that it comes with all hardware and software required to run inference or training with an open weights LLM.
The existence of this product, which competes with cloud-based offerings like those of OpenAI and Anthropic, is presumably the reason why the Palantir CEO criticized very harshly some time ago the business model of OpenAI/Anthropic.
While I doubt that the ethics of Palantir is any better than of OpenAI/Anthropic, in this particular case I have to agree with Alex Karp about "Sovereign AI", i.e. that only losers will make their business completely dependent on an external entity like OpenAI or Anthropic, who are certainly not trustworthy.
It is just a dedicated computer system, which should be managed by its owner, like any other on-prem servers.
I doubt that it has a good price/performance ratio, but it is a solution for those who feel that they do not want to search, buy, assemble, install and configure every HW/SW component.
“100% guaranteed downtime when you least can afford it and the support tickets are your problem.”
We’ve a hybrid shop, including hosting our own ML infra, and we save a ton from cloud spend with local ML. Easily one million USD over past three years. But it’s not “free”, you are shifting a lot of labor into your plate.
I think at this point the question is: will the US government be willing and capable to justify the trillion dollar valuation for _one_ of the companies via regulatory capture? The US has a workforce of 170m, so 1.7 trillion would come down to 10k per person, or a discounted cashflow at 3% of 25 USD per month - not including private use, students etc.
Which is not unreasonable. Just hosting it in the EU and promising not to retain / sell the data let's you charge a healthy extra and compete in many areas other players can't.
It is impossible to justify the absurd private valuations they have given themselves in collusion with investors.
I wish they had tried to IPO because then we’d see the judgement of the market on this. But that’s why they didn’t this year. How long can they keep up the charade that their models are uniquely valuable and on the path to AGI?
"I just don't see how you justify a trillion valuation for US AI"
- military applications
- financial applications
- medical
- applied science
In all those cases it is achievable for those who have needed training data, and Chinese are not going to get them easily. US AI Labs are showing: give us the data, we will do wonders, promising "singularity"-level future achievements.
Not sure about Sol as I haven't used it, but, at least for security work -- does it matter? It's not like you will be allowed to use Fable (or access Mythos) for anything cybersecurity-related unless your name is "Dario Amodei" or you are one of his rich friends. So regardless of how good Fable/Mythos is here it's a completely moot point for normal people, because they can't use it for that anyway.
You must be a 5000 person company with an existing enterprise contract to get approved that fast. That sounds like a 15 minute SLA agreement. Individuals no matter how qualified about cybersecurity, are ghosted
That's not my experience at all. I was approved fairly fast - around an hour from submitting the form and getting a response.
However, even being in the cybersecurity programme, Fable refuses to answer prompts that it determines could be even tangentially related to cybersecurity. In fact, for a while, I was unable to use Fable with any prompt, as it recalled from memory that I was a cybersecurity professional, which triggered the refusal even for simple prompts like asking for a chili recipe.
Have you tried to use Fable for anything even remotely security related, when the refusals kick in as soon as you even fart in the vague direction of anything security or biology-adjacent?
I don't understand all this spite about "rich friends" when it was the US government that shut Fable down for not adequately blocking cyber capabilities.
I mean what honestly are you thinking Anthropic can do to give you better cyber tools? Their frontier model was literally nuked by the feds for a month for doing it.
"Mythos" is the cyber-security equivalent of Fable (without guardrails), and only a very select few corporations have access to it.
Fable is their version with guardrails on everything except "Make me a pelican svg" or "create a to-do" app, that is the version that the government banned
Only a few corporations have Mythos because the US government is whitelisting them one at a time. Anthropic releasing Mythos to the public was never on the table, they would have been shut down in milliseconds by the feds if they tried.
Before the US government had anything to do with this, Anthropic were fear mongering Mythos (BTW, Amodei also fear-mongered GPT-2, so this is a normal pattern in their operation) calling it "too dangerous to release", and back then only Anthropic was in charge of the whitelist.
Then the government believed Amodei's bullshit and this is a result of that, this was all self-inflicted.
Sorry but if you stepped back for a moment you'd realize this is all contrived nonsense to let to have your cake and eat it too.
No, Anthropic did not mind-game the US government into being worried about cybersecurity. The NSA has been paranoid about cyber controls for longer than you've been alive. If Anthropic had come out of the gate saying "no don't worry man, our model is TOTALLY COOL", while simultaneously attacking HAWK and finding core Linux vulnerabilities, I assure you the US government would have caught up about ten minutes later and we'd be in exactly the same spot minus your ability to tell Anthropic they were wearing the wrong dress and asking for it.
Mythos isn't some scary dangerous model that can find high severity bugs seamlessly, that's just Anthropic marketing. Most of the vulnerabilities they found were low severity hyped up to make their model look good, with (I think, maybe?) the exception of a few.
Now that Chinese open weight models have similar capabilities, and their guardrails can also just be removed, it doesn't look like anyone has "hacked" into everything because of the scary dangerous models like Anthropic were making it out to be.
The majority of high severity vulnerabilities are not the kind of thing you need a PhD in Comp Sci to comprehend, they are mostly about finding a way to get a system to end up in a state different than was anticipated when entering a particular code path.
Exhaustively looking at code and identifying ways to do this is something LLMs are quite good at. They don’t get tired, and you can run them non-stop.
They're also (generally) quite good at reading the literal meaning of the code, whereas humans often see the intended meaning first, and can be biased.
If you had a tireless junior engineer who was given the job of “make this application get into a state it’s not supposed to be in”, you’d probably get similar results.
What Mythos is quite good at is both the first bit and coming up with ways it could chain that together with other bits of unexpected state to create something that forms a meaningful vulnerability rather than a dead end.
> I don't understand all this spite about "rich friends"
Okay, here's a challenge: I assume you're not a rich and powerful entity, so try to gain access to Mythos. I'll wait.
> I mean what honestly are you thinking Anthropic can do to give you better cyber tools? Their frontier model was literally nuked by the feds for a month for doing it.
Well, first I'd suggest they stop with the constant fear mongering.
Here's my prediction for what will happen: the Chinese models will catch up to Fable/Mythos. They will be fully unrestricted and everyone will have access. The world will not end. Good guys will use them to harden their systems, in equilibrium to what bad guys have access to, so effectively status quo will not change.
Right, so according to you it's because of the US government that they don't release it to the public? Have you missed their constant and incessant fear mongering?
The causality chain here was not "US government says its dangerous -> Anthropic can't release it", it was "Anthropic is fear mongering -> US government listens to their fear mongering".
The issue is that these companies keep trying to pull the ladder up behind them by going "oh my god our models are so dangerous only we should be allowed to develop them". Sometimes it backfires, but the companies aren't innocent.
Realistically, you're looking at least 2x DGX sparks to run this at a 2 bit quant, but quantization really lobotomizes models so it's just better to run DSv4 flash at full precision.
4x DGX sparks should let you run this at 4 bit at least and there are some folks who ran GLM 5.2 on this configuration in r/LocalLlama
If you can afford it, another DGX spark is worth it imo. Especially since, owning just one, you have a $1000 ConnectX7 card that's unused. You can find speeds here: https://spark-arena.com/leaderboard
i run flash v4 at 2bit, its pretty great and on my tests against full model It didn't lose any capabilities. It just was thinking more. So you don't have the same efficiency.
the difference is that with open models jailbreaking is trivial if you know what you are doing so this makes a frontier open model infinitely more useful for certain tasks seeing as closed frontier models will just refuse (and jailbreaking them is a waste of time when you have good open models).
in some cases (mainly reverse engineering) I have observed GLM 5.2 jailbreaking itself with no effort on my part, the thinking trace revealed that it did some mental gymnastics to pretend it was a crackme or capture the flag competition.
I might be just reading my positive bias into that text, but is it possible that it is written less like SV marketing hype trash and more like researchers wrote it?
It does feel like it respects both me and my time.
Thank you, Z.AI.
Amazing what difference it makes when the top of your org are actual university professors.
> Mythos 5 remains well ahead at 181 and 247 tasks. The pattern across the three is consistent: the further up the exploitation chain a benchmark sits, the wider the remaining gap to the closed frontier. Capability is growing fastest exactly where we are furthest behind.
I appreciate they don't just take the opportunity to self-glaze.
Can’t this be extended quite far? Use a cerebras-served model, use verification techniques to generate and solve millions of problems and then use that as training?
which is the bigger headline that people don't realize. this is 744b and its head to head with Kimi K3 (2.8T), smashes DS v4 pro (1.5T). even Opus and Sol are rumored to be 1.5T+ this is half the size!
Apple will release M7 MacBook Pros / Mac Minis next year, and they will be able to run free LLMs locally at native speed. All software developer notebooks will be replaced to run local models, saving a lot by cancelling Claude Code subscriptions.
Developers win. Apple stocks will be rocketing. Everything else will go down. You're welcome.
You'd need the 256 gb memory model which will be expensive because apple has trouble getting capacity (got turned down by cxmt). And even then you can only run a 2 bit quant which is noticeably worse than 8 bit
They will cost an insane amount as well. Maybe less than subscriptions or tokens. But running massive models on laptops with batteries and poor cooling doesn’t make much sense.
Same image->html test as I showed in the Gemini 3.7 flash thread. Note that GLM isn't multimodal, but it still was able to generate something similar-ish by writing a python script to inspect the image and extract elements from it.
For having no vision, it did a tremendous job. I'm pretty impressed it was able to extract so much detail.
The Opus one is still significantly better, but that's to be expected since it's multimodal. Curious to see where a future version from Z.ai lands on this.
OpenAI and Anthropic need to just go ahead and give people access to the cyber models.
Otherwise we have a world of attackers using open and closed source models against a much smaller group of maintainers that are likely heavily dependent on Anthropic and OpenAI and for whom it may not be a simple matter to just get approval to start using the open model flavor of the month.
I've been building a harness for security work, and had to switch to GPT 5.5 when even Opus started refusing security work. Then 5.6 Sol arrived, and it refuses security work, too. So, I switched to Kimi K3 and DeepSeek for API testing just because it's so much cheaper. But, if GLM is better, I'm here for it, as I think GLM is also cheaper than K3.
It is so valuable in practise to be able to have the models see screenshots - I guess if they aren't in the benchmarks then nobody will focus on it. But it completely nixes these for some of my main use cases.
I can’t come up with a use case where I couldn’t extract the image details using another, multimodal model and pass it into the GLM’s context with as many details as I need.
I think you lose a lot by not having the vision capability shared with the text. It is the joint reasoning across them where the power lies (the same model that sees the code and made the changes to produce the visual presentation, sees the image of it and reasons about it).
Their coding plan switched to credits, didn’t it? What are the rate limits like, compared to Anthropic or Kimi K3?
I remember trying their Coding Plan out before the change and the 5 hour limits felt too restrictive then even for light/medium work, especially cause of the whole peak and off-peak thing: https://blog.kronis.dev/blog/z-ai-s-glm-5-2-is-a-great-model...
Nowadays, I’d probably go with their Max plan if the rate limits are okay? Anyone using them now?
Oh also unrelated but ZCode was surprisingly good, which is surprising for a tool that came out of nowhere - even some of the critiques in my blog post have been patched out. Sadly they don’t support using Claude Code as an agent so can’t use it like Paseo or Kepler or Agent Orchestrator.
I'm not that up to date with the latest AI developments, but I noticed that this article seems to use "Cyber Capabilities" as a shorthand for the model's ability at cybersecurity tasks? Is that now an established expression, same as "crypto" now refers to cryptocurrencies rather that cryptography? Because "cybernetics" actually means something different (yeah, old man yelling at clouds, I know)...
People familiar with the topic, how will models continue to get better? Post training it seems? Labs have already used up internet-scale data, so are there any limits to architecture improvements and post training or can we expect this trend to continue? ByteDance is training a 10T-parameter model. Here, GLM 5.3 outperforms models 3-4x its size of roughly 700B, so parameter count doesn’t seem to be a direct correlation anymore.
yet but it is still contain a lot of trash. you need better models to process those trash and create a curate dataset. this will happen again and again until there is no more juice to squeeze. and I'm sure we are still not done with it.
> post training
yeah this will be crucial. the big models are already too capable, they are just not that aligned with current agent tasks.
> parameter count doesn’t seem to be a direct correlation anymore
I don't think so, remember that chinese labs do not have as much compute power compare to US frontier labs. that's why deepseek v4 flash had that huge jump and deepseek v4 pro is kinda a disappointment, they just do not have the compute power to proper posttrain the pro model like they wanted. glm is also a relative small model so you also can see the huge jump with just post training. so it does not mean the size does not matter, it is just mean that the chinese labs currently only capable of training smaller models effectively.
Yeah, the comparison here between GLM 5.3 and Sol + Fable is impressive on its own, but incredibly more so when you consider it's a fraction of the (rumored) size. The miniaturization trend is as strong as ever.
GitHub dumps are about 115 terabytes. The common crawl is in the petabyte range uncompressed for every year. Apparently there are dumps of Reddit too in spite of their efforts to ban bots and it's not solely due to the use of residential proxies. For a 1:20 parameter to token ratio, you can still train up to 10 trillion parameters so 10T parameters times 20 is about 200 trillion tokens. Then each token is 4 bytes so 200 times 4 is about 800 terabytes, which is not inconceivable, the common crawl alone has more data than that. So does the internet archive if you donate to them, Anna's archive is 2 petabytes including images, etc etc not all of it is text, but training on multimodal data increases model intelligence by virtue of being multimodal
You basically need both. Parameters and good post training. If you keep on growing both, you’ll have good models.
LLMs are still surprisingly “easy”. You need maybe a couple dozens of right people, a lot of good quality data and a lot of GPU that you know how to operate. There’s relatively little “secret sauce” needed.
Sure, same for building a model in an economically sustainable way. But barier to entry is surprisingly low (expect for the huge amount of cash, of course). That’s fairly surprising, given how extremely powerful that tech is.
10 years ago it was super hard to have usable “frontier” ML. You needed very complex data warehouse, feature engineers, feature stores, multi level ranking, calibrations, tons of different model architectures, etc, etc. Each by itself was extremely hard engineering problem and really only handful of companies could deal with that complexity.
With LLMs, 95% of that is gone, infra to support them is greatly simplified. Of course, to make really reliable, performant, user friendly, etc - you still need to a lot of engineering. But it’s very different challenge.
Despite this being the topic du jour of 2025, it was never true. Most of the "we've hit a wall with data" came from communicators / media and not researchers. It got popular because negativity sells. It's a false premise for a number of reasons:
a) Data curation is as important, if not more important than bulk data. Models becoming better at classification leads to better curation leads to cleaner data. Throwing common crawl and pray is so 2023. We've known this since llama3 days, it worked then, there's no reason to think this will not continue to work as the models imrpove.
b) Models are today good enough that you can augment / multiply your data easily with enough compute. You can now have a model take "authoritative content" and create more data from that + scenarios. Say you take a book on computer architecture. You ask models to break it down. Then you ask models to find examples for each topic. Then you ask models to ask questions and offer answers from several viewpoints. Then you take each of those and ask other models to flag inconsistencies. And so on. But you can whateverX your data from one authoritative source + bulk data into 5x - 10x "scenarios".
c) RL is really really really powerful. It's hard to do right (reward hacking, instabilities, etc) but once it works it "keeps" on working. Again, we knew this to be true a few years ago, ever since models really started to do well on math (highly verifiable). It only follows they're getting better on cybersec and other verifiable tasks. But now, with models improving, you get the same data augmentation pipelines as above, just better because they're also verifiable. For example, the way cursor augments their data: take a repo, ask an agent to identify a feature (it can be a large multi-file feature). Remove all code relating to that feature, but keep the original tests in the repo. While training, that becomes a RL scenario: implement this feature in this repo. Verify it with the original (hidden for training) tests. Reward appropriately. Now you can get 1 repo -> 20-50-100 scenarios. Instead of "feed everything into the pretraining", you're now creating scenarios, verify them w/ existing tools, and get your scoring function for the rewards. And, importantly, as the models become better in general, they also become better at this pipeline building exercise. So the next iteration gets trained on more scenarios, better scenarios, and so on.
> how will models continue to get better?
Probably the same. No one can know for sure, but at the moment, despite all the "walls this, slowdown that, plateauing" and so on, there are no signs of slowing down. And, as you noted, this works across the field of model sizes. There are, of course, theoretical information-based limits on size, but smaller models also improve, once "bigger" models can be used as training data generators, oracles for verification, rubric verifiers for open ended questions, and so on.
And smaller models (i.e. cheaper to serve) get to generate more traces during RL, and more rollouts give you better training, and so on. Next up - hardware optimised inferencing (ASICs basically). Once you have that, we can expect another wave of improvements. And so on.
Thank you for your response. Part c was especially insightful. Quite a smart way to do it and makes the possibilities of post training seem almost endless. Makes sense that you just need more time and compute.
A flood of releases today, really difficult to make out for someone who does not use or test all these models on complex real world use cases as to how people decide which ones to use (besides price)
Count yourself lucky that you don't feel compelled to try them all yourself immediately. I'm just trying to decide whether to get a Z.ai coding plan or wait until it appears on OpenRouter. 5.2 was quite solid, but it was just shy of Opus 4.8 in my benchmarks of security auditing capabilities. I've mostly been using Kimi K3, because American vendors won't let the peasantry use their best models for security work.
No Hugging Face link yet. I wish they would release it under a true FOSS license.
Kimi and QWEN are now moving on to a restricted-usage license, which, although is still better than the proprietary American models, is a step back from the open source Chinese LLM culture.
Let's just commit that FOSS business is really difficult for LLM industry that depends so heavily on massive financing. Making weights freely available to indie devs, small companies, and research purposes is good enough and might be the most ethical move which is financially continuable.
Let those companies with thousands of GPU making millions pay. They should.
"GLM-5.3 is the most capable open-weights model for coding, with a 50% improvement over GLM-5.2 on our in-house Z.ai Code Bench. It also achieve open-source SOTA on public benchmarks including Terminal Bench 3.0 and Agents' Last Exam."
"Open Source: We will release the weights in two weeks after launch, once safety evaluation and hardening are complete."
Feels like Fable's edge ended up just being long horizon task scaling, which post-training seems to achieve as seen here. Wonder what the next frontier is? Improvement in specialised tasks or computer use?
Anthropic needs to teach Opus how to speak English again, because Opus 5 seems to have forgotten. Utterly incoherent a lot of the time. They seem to be so busy scare-mongering and cooking up guardrails and watermarks that they haven't noticed that their models are getting weird.
It still knows how to speak English. When I tell it to explain something in plain language, it generally does a very good job. The weird thing is that those instructions don't persist: it lapses back into Claude-speak pretty much every turn no matter how hard I try to instruct it not to.
(In my case "it"=Fable; I assume Opus is similar.)
The Fable guardrails have trained me to pretty much exclusively use Opus when using Claude Code (lately I'm focused on a lot of security and securit-adjacent stuff, which Fable refuses to do).
I'm not sure. I noticed it immediately with Opus 5; strong for code, though it chews longer than I like, but really weak at explaining things. If it didn't just implement the thing, I would often think it didn't understand it and was hallucinating the explanation.
It seems to speak in a shorthand that only it understands, referring back to conversations I never had with it (stuff like "your instinct was right"), and using unusual words for common concepts. That was before the watermarks were announced, but that doesn't necessarily mean they weren't there before the announcement. I don't know what the cause is, but I've begun to have to ask it for explanations a lot more often, and I hate asking it for explanations because it does go on. All models go on, but Claude models are a class of their own in terms of verbosity and purple prose.
It just feels like they're not focused on the models lately, and instead on whatever kind of lobbying and propaganda they're up to. Meanwhile, a handful of much smaller Chinese companies are focused on nothing but the models and are about to lap the US makers while they fart around.
I've been persistently insulting Opus 4.8 lately, since it started(?) constantly speaking incomprehensible gibberish and noise.
No amount of telling it to phrase stuff differently seems to help there anymore.
So either I am seeing patterns in noise, or something changed about the model, the harness, the servers or the universe.
> Open Source: We will release the weights in two weeks after launch, once safety evaluation and hardening are complete.
What "safety" evaluation? They already evaluated it and found it to be highly capable at exploiting security vulnerabilities. So we know it is not "safe", and they don't seem to plan to do anything against it. What could be more dangerous than hacking? Biological weapons research? I don't think Chinese labs are doing anything against this either.
just imagine the world without these open weight models - we'd probably have to reverse mortgage our homes to pay for tokens to those trillion $ companies to have access to their models.
Excellent stuff. I'm not a "china's geopolitics" fan, but this is a good thing. US frontier labs and the US government need someone or something "outside the box" breathing down their neck otherwise they sniff too many of their own farts. (China being the one doing the breathing is not ideal, but since the EU resigned from even attempting to be in this race and decided to focus on bullshit like replacing its population with Pakistanis and Somalis and destroying its industry in the name of green insanity, there are not any other options.)
This is absolutely still shy of Sol and Fable, but only just by a hair. Ridiculous results. There's still not a compelling economic reason to drop OpenAI courtesy of the ludicrous reset addiction that's taken place, but it feels like we're on the precipice.
How are you all toying with running this kind of thing in a mega quantized way locally? Two weeks out from released weights, but this is still just GLM 5.2 with post-training magic.
OpenAI and Anthropic are both seeking trillion IPOs, while Chinese labs are pumping out open-weight models that are free for US providers to host and monetize.
These Chinese models cost less of US SOTA models to run, even if they are less capable. Providers can just run them, offer cheap tokens, and pocket the margin.
I just don't see how you justify a trillion valuation for US AI labs when the underlying models are being commoditized this fast.
US investors are desperate for the next hypergrowth opportunity. From what I can tell the US economic strategy is to outgrow its debt.
This is going to be catastrophic.
Whether AI works or use useful or not isn’t even the question anymore. It can fulfil every promise Sam Altman has been making and will still make no financial sense to justify these valuations.
Another interesting potential market here will be 'LLM in a box'. All the hardware and other tooling in a prebuilt, but modular, package ready to go. Pay one up-front cost, get a system running [whatever open LLM] with a token rate of [x], optionally configured to be immediately ready for distributed usage. Basically the opposite of cloud stuff: no rent, no dependency, 100% guaranteed uptime, guaranteed security/privacy (at least subject to your own actions), and so on.
Palantir already offers a "turnkey AI datacenter", i.e. a rack with "NVIDIA Blackwell Ultra systems with eight NVIDIA Blackwell Ultra GPUs and NVIDIA Spectrum-X™ Ethernet networking for AI training and inference".
It is said that it comes with all hardware and software required to run inference or training with an open weights LLM.
The existence of this product, which competes with cloud-based offerings like those of OpenAI and Anthropic, is presumably the reason why the Palantir CEO criticized very harshly some time ago the business model of OpenAI/Anthropic.
While I doubt that the ethics of Palantir is any better than of OpenAI/Anthropic, in this particular case I have to agree with Alex Karp about "Sovereign AI", i.e. that only losers will make their business completely dependent on an external entity like OpenAI or Anthropic, who are certainly not trustworthy.
I'm not sure a data center run by ... Palantir of all organizations is what people have in mind when they worry about data sovereignty.
They are selling it, not running it.
It is just a dedicated computer system, which should be managed by its owner, like any other on-prem servers.
I doubt that it has a good price/performance ratio, but it is a solution for those who feel that they do not want to search, buy, assemble, install and configure every HW/SW component.
> 100% guaranteed uptime
Disagree there but I think this is an interesting idea. We would need to find some more cost-efficient hardware to run it on than Nvidia GPUs.
It will come... all big hardware players (Intel, AMD, Broadcom) and dozens of startups (Tenstorrent, etc.) are working on it...
“100% guaranteed downtime when you least can afford it and the support tickets are your problem.”
We’ve a hybrid shop, including hosting our own ML infra, and we save a ton from cloud spend with local ML. Easily one million USD over past three years. But it’s not “free”, you are shifting a lot of labor into your plate.
And with that also gain institutional knowledge, skill up your workers and attract talent that wants to work on this stuff.
All boils down to short-term/long-term thinking.
I think at this point the question is: will the US government be willing and capable to justify the trillion dollar valuation for _one_ of the companies via regulatory capture? The US has a workforce of 170m, so 1.7 trillion would come down to 10k per person, or a discounted cashflow at 3% of 25 USD per month - not including private use, students etc.
Why would you restrict to the US workforce? ChatGPT has a billion users.
Because it would be the US taxpayers bailing them out.
I think that explains the race for IPO by the US AI labs, they know that the longer they wait, the less they will be worth.
> Providers can just run them, offer cheap tokens, and pocket the margin.
There’s an assumption that you can spin up the infra and acquire customers within that margin
Which is not unreasonable. Just hosting it in the EU and promising not to retain / sell the data let's you charge a healthy extra and compete in many areas other players can't.
It is impossible to justify the absurd private valuations they have given themselves in collusion with investors.
I wish they had tried to IPO because then we’d see the judgement of the market on this. But that’s why they didn’t this year. How long can they keep up the charade that their models are uniquely valuable and on the path to AGI?
"I just don't see how you justify a trillion valuation for US AI"
- military applications - financial applications - medical - applied science
In all those cases it is achievable for those who have needed training data, and Chinese are not going to get them easily. US AI Labs are showing: give us the data, we will do wonders, promising "singularity"-level future achievements.
Ok
Hmm.. how you justify?
Provoking war, this is how the empire "defends" itself, usually.
I just hope that this time it will get stuck in your throat.
I suggest you think why OpenAI was worth billions before ChatGPT. The valuation is not about how the current set of models can be monetized.
Fable finished training 6+ months ago.
Anthropic will only release new models to the public when the competition forces them to.
> This is absolutely still shy of Sol and Fable
Not sure about Sol as I haven't used it, but, at least for security work -- does it matter? It's not like you will be allowed to use Fable (or access Mythos) for anything cybersecurity-related unless your name is "Dario Amodei" or you are one of his rich friends. So regardless of how good Fable/Mythos is here it's a completely moot point for normal people, because they can't use it for that anyway.
We applied for the cybersecurity approval via the form and got approval back in less than an hour. Have you… tried?
You must be a 5000 person company with an existing enterprise contract to get approved that fast. That sounds like a 15 minute SLA agreement. Individuals no matter how qualified about cybersecurity, are ghosted
That's not my experience at all. I was approved fairly fast - around an hour from submitting the form and getting a response.
However, even being in the cybersecurity programme, Fable refuses to answer prompts that it determines could be even tangentially related to cybersecurity. In fact, for a while, I was unable to use Fable with any prompt, as it recalled from memory that I was a cybersecurity professional, which triggered the refusal even for simple prompts like asking for a chili recipe.
Cyberchili. Might burn holes into corporate firewalls
Have you tried to use Fable for anything even remotely security related, when the refusals kick in as soon as you even fart in the vague direction of anything security or biology-adjacent?
For this comment to have value, you should indicate whether or not you applied for cybersecurity approval, and were approved or not.
Are there any limitations on this version?
I don't understand all this spite about "rich friends" when it was the US government that shut Fable down for not adequately blocking cyber capabilities.
I mean what honestly are you thinking Anthropic can do to give you better cyber tools? Their frontier model was literally nuked by the feds for a month for doing it.
"Mythos" is the cyber-security equivalent of Fable (without guardrails), and only a very select few corporations have access to it.
Fable is their version with guardrails on everything except "Make me a pelican svg" or "create a to-do" app, that is the version that the government banned
I know all this?
Only a few corporations have Mythos because the US government is whitelisting them one at a time. Anthropic releasing Mythos to the public was never on the table, they would have been shut down in milliseconds by the feds if they tried.
Before the US government had anything to do with this, Anthropic were fear mongering Mythos (BTW, Amodei also fear-mongered GPT-2, so this is a normal pattern in their operation) calling it "too dangerous to release", and back then only Anthropic was in charge of the whitelist.
Then the government believed Amodei's bullshit and this is a result of that, this was all self-inflicted.
Sorry but if you stepped back for a moment you'd realize this is all contrived nonsense to let to have your cake and eat it too.
No, Anthropic did not mind-game the US government into being worried about cybersecurity. The NSA has been paranoid about cyber controls for longer than you've been alive. If Anthropic had come out of the gate saying "no don't worry man, our model is TOTALLY COOL", while simultaneously attacking HAWK and finding core Linux vulnerabilities, I assure you the US government would have caught up about ten minutes later and we'd be in exactly the same spot minus your ability to tell Anthropic they were wearing the wrong dress and asking for it.
Mythos isn't some scary dangerous model that can find high severity bugs seamlessly, that's just Anthropic marketing. Most of the vulnerabilities they found were low severity hyped up to make their model look good, with (I think, maybe?) the exception of a few.
Now that Chinese open weight models have similar capabilities, and their guardrails can also just be removed, it doesn't look like anyone has "hacked" into everything because of the scary dangerous models like Anthropic were making it out to be.
In principle I agree but in practice I don’t.
The majority of high severity vulnerabilities are not the kind of thing you need a PhD in Comp Sci to comprehend, they are mostly about finding a way to get a system to end up in a state different than was anticipated when entering a particular code path.
Exhaustively looking at code and identifying ways to do this is something LLMs are quite good at. They don’t get tired, and you can run them non-stop.
They're also (generally) quite good at reading the literal meaning of the code, whereas humans often see the intended meaning first, and can be biased.
If you had a tireless junior engineer who was given the job of “make this application get into a state it’s not supposed to be in”, you’d probably get similar results.
What Mythos is quite good at is both the first bit and coming up with ways it could chain that together with other bits of unexpected state to create something that forms a meaningful vulnerability rather than a dead end.
> I don't understand all this spite about "rich friends"
Okay, here's a challenge: I assume you're not a rich and powerful entity, so try to gain access to Mythos. I'll wait.
> I mean what honestly are you thinking Anthropic can do to give you better cyber tools? Their frontier model was literally nuked by the feds for a month for doing it.
Well, first I'd suggest they stop with the constant fear mongering.
Here's my prediction for what will happen: the Chinese models will catch up to Fable/Mythos. They will be fully unrestricted and everyone will have access. The world will not end. Good guys will use them to harden their systems, in equilibrium to what bad guys have access to, so effectively status quo will not change.
This is a lot of words to say "you're right, Anthropic does not have any legal way to release frontier cyber capabilities to the public"
Right, so according to you it's because of the US government that they don't release it to the public? Have you missed their constant and incessant fear mongering?
The causality chain here was not "US government says its dangerous -> Anthropic can't release it", it was "Anthropic is fear mongering -> US government listens to their fear mongering".
The issue is that these companies keep trying to pull the ladder up behind them by going "oh my god our models are so dangerous only we should be allowed to develop them". Sometimes it backfires, but the companies aren't innocent.
> this is just GLM 5.2 with post-training magic
Isn't post-training turning out to be the most important part?
DwarfStar (https://github.com/antirez/ds4) supports GLM 5.2 and DeepSeek. Not only for toying, but for getting work done.
Realistically, you're looking at least 2x DGX sparks to run this at a 2 bit quant, but quantization really lobotomizes models so it's just better to run DSv4 flash at full precision.
4x DGX sparks should let you run this at 4 bit at least and there are some folks who ran GLM 5.2 on this configuration in r/LocalLlama
How fast are 2x or 4x DGX?
I only have one and am wondering what the benefits are of getting another. I feel I will be disappointed…
If you can afford it, another DGX spark is worth it imo. Especially since, owning just one, you have a $1000 ConnectX7 card that's unused. You can find speeds here: https://spark-arena.com/leaderboard
i run flash v4 at 2bit, its pretty great and on my tests against full model It didn't lose any capabilities. It just was thinking more. So you don't have the same efficiency.
the difference is that with open models jailbreaking is trivial if you know what you are doing so this makes a frontier open model infinitely more useful for certain tasks seeing as closed frontier models will just refuse (and jailbreaking them is a waste of time when you have good open models).
in some cases (mainly reverse engineering) I have observed GLM 5.2 jailbreaking itself with no effort on my part, the thinking trace revealed that it did some mental gymnastics to pretend it was a crackme or capture the flag competition.
> This is absolutely still shy of Sol and Fable, but only just by a hair.
Even if there was a small/medium gap, the fact that this is a free model beats both of the above on pure economics.
I might be just reading my positive bias into that text, but is it possible that it is written less like SV marketing hype trash and more like researchers wrote it?
It does feel like it respects both me and my time.
Thank you, Z.AI. Amazing what difference it makes when the top of your org are actual university professors.
I was thinking the same thing. It feels truthful, no marketing BS and they call out where they lack behind the best models
> Mythos 5 remains well ahead at 181 and 247 tasks. The pattern across the three is consistent: the further up the exploitation chain a benchmark sits, the wider the remaining gap to the closed frontier. Capability is growing fastest exactly where we are furthest behind.
I appreciate they don't just take the opportunity to self-glaze.
Yet still reads the same as standard slop
> Scaling post-training is all we did for GLM-5.3.
Love this opening line. And wow, great results.
> As agent capability improves, much of the difficulty in scaling post-training moves from the model to the environment.
What actually is "scaling post-training"?
More RLVR. Give it verifiable problems, if it doesn't find a solution move on, if it does, use that as a reward signal.
Can’t this be extended quite far? Use a cerebras-served model, use verification techniques to generate and solve millions of problems and then use that as training?
does this suggest 5.3 is the same # of parameters as 5.2?
which is the bigger headline that people don't realize. this is 744b and its head to head with Kimi K3 (2.8T), smashes DS v4 pro (1.5T). even Opus and Sol are rumored to be 1.5T+ this is half the size!
“Today we are releasing GLM-5.3. It uses the same base model as GLM-5.2 — every gain comes from post-training.“
Apple will release M7 MacBook Pros / Mac Minis next year, and they will be able to run free LLMs locally at native speed. All software developer notebooks will be replaced to run local models, saving a lot by cancelling Claude Code subscriptions. Developers win. Apple stocks will be rocketing. Everything else will go down. You're welcome.
> run free LLMs locally at native speed
This reads like a hallucination. What does native speed even mean?
You'd need the 256 gb memory model which will be expensive because apple has trouble getting capacity (got turned down by cxmt). And even then you can only run a 2 bit quant which is noticeably worse than 8 bit
They will cost an insane amount as well. Maybe less than subscriptions or tokens. But running massive models on laptops with batteries and poor cooling doesn’t make much sense.
Same image->html test as I showed in the Gemini 3.7 flash thread. Note that GLM isn't multimodal, but it still was able to generate something similar-ish by writing a python script to inspect the image and extract elements from it.
Original images: https://image.non.io/neonRamenDesigns.webp
GLM 5.3 build: https://html.non.io/neonRamenGLM5.3
Opus 5 build for comparison: https://html.non.io/neonRamen
For having no vision, it did a tremendous job. I'm pretty impressed it was able to extract so much detail.
The Opus one is still significantly better, but that's to be expected since it's multimodal. Curious to see where a future version from Z.ai lands on this.
OpenAI and Anthropic need to just go ahead and give people access to the cyber models.
Otherwise we have a world of attackers using open and closed source models against a much smaller group of maintainers that are likely heavily dependent on Anthropic and OpenAI and for whom it may not be a simple matter to just get approval to start using the open model flavor of the month.
Not only attackers. I have to switch to Kimi or GLM even in cases of basic issue triage on my own projects! Current guardrails are ridiculous.
I've been building a harness for security work, and had to switch to GPT 5.5 when even Opus started refusing security work. Then 5.6 Sol arrived, and it refuses security work, too. So, I switched to Kimi K3 and DeepSeek for API testing just because it's so much cheaper. But, if GLM is better, I'm here for it, as I think GLM is also cheaper than K3.
At least OpenAI seems to want to do that, but the US is now forcing them to go through approvals. Anthropic seems much more hesitant.
Missing multimodal again?
It is so valuable in practise to be able to have the models see screenshots - I guess if they aren't in the benchmarks then nobody will focus on it. But it completely nixes these for some of my main use cases.
I can’t come up with a use case where I couldn’t extract the image details using another, multimodal model and pass it into the GLM’s context with as many details as I need.
I think you lose a lot by not having the vision capability shared with the text. It is the joint reasoning across them where the power lies (the same model that sees the code and made the changes to produce the visual presentation, sees the image of it and reasons about it).
Probably not what you're after, but I've considered having a separate small mm-model act as a seeing-eye dog for the bigger more capable one.
I would assume that GLM 6 will be multimodal, but 5.x will be text-only.
Their coding plan switched to credits, didn’t it? What are the rate limits like, compared to Anthropic or Kimi K3?
I remember trying their Coding Plan out before the change and the 5 hour limits felt too restrictive then even for light/medium work, especially cause of the whole peak and off-peak thing: https://blog.kronis.dev/blog/z-ai-s-glm-5-2-is-a-great-model...
Nowadays, I’d probably go with their Max plan if the rate limits are okay? Anyone using them now?
Oh also unrelated but ZCode was surprisingly good, which is surprising for a tool that came out of nowhere - even some of the critiques in my blog post have been patched out. Sadly they don’t support using Claude Code as an agent so can’t use it like Paseo or Kepler or Agent Orchestrator.
Such an interesting times we are in,
We just had amazing releases this past two months
kimi k3, glm5.3 qwen3.8 and now glm5.3
These open models are getting really good
What a week for AI model releases
No yet finished! Still waiting for tonight Qwen3.8-27B and the unsloth Q5_K_M/S quantification.
Hopping for an AgentWorld variant from Qwen but I guess, I have too high expectations.
I'm not that up to date with the latest AI developments, but I noticed that this article seems to use "Cyber Capabilities" as a shorthand for the model's ability at cybersecurity tasks? Is that now an established expression, same as "crypto" now refers to cryptocurrencies rather that cryptography? Because "cybernetics" actually means something different (yeah, old man yelling at clouds, I know)...
People familiar with the topic, how will models continue to get better? Post training it seems? Labs have already used up internet-scale data, so are there any limits to architecture improvements and post training or can we expect this trend to continue? ByteDance is training a 10T-parameter model. Here, GLM 5.3 outperforms models 3-4x its size of roughly 700B, so parameter count doesn’t seem to be a direct correlation anymore.
> used up internet-scale data
yet but it is still contain a lot of trash. you need better models to process those trash and create a curate dataset. this will happen again and again until there is no more juice to squeeze. and I'm sure we are still not done with it.
> post training
yeah this will be crucial. the big models are already too capable, they are just not that aligned with current agent tasks.
> parameter count doesn’t seem to be a direct correlation anymore
I don't think so, remember that chinese labs do not have as much compute power compare to US frontier labs. that's why deepseek v4 flash had that huge jump and deepseek v4 pro is kinda a disappointment, they just do not have the compute power to proper posttrain the pro model like they wanted. glm is also a relative small model so you also can see the huge jump with just post training. so it does not mean the size does not matter, it is just mean that the chinese labs currently only capable of training smaller models effectively.
Yeah, the comparison here between GLM 5.3 and Sol + Fable is impressive on its own, but incredibly more so when you consider it's a fraction of the (rumored) size. The miniaturization trend is as strong as ever.
GitHub dumps are about 115 terabytes. The common crawl is in the petabyte range uncompressed for every year. Apparently there are dumps of Reddit too in spite of their efforts to ban bots and it's not solely due to the use of residential proxies. For a 1:20 parameter to token ratio, you can still train up to 10 trillion parameters so 10T parameters times 20 is about 200 trillion tokens. Then each token is 4 bytes so 200 times 4 is about 800 terabytes, which is not inconceivable, the common crawl alone has more data than that. So does the internet archive if you donate to them, Anna's archive is 2 petabytes including images, etc etc not all of it is text, but training on multimodal data increases model intelligence by virtue of being multimodal
You basically need both. Parameters and good post training. If you keep on growing both, you’ll have good models.
LLMs are still surprisingly “easy”. You need maybe a couple dozens of right people, a lot of good quality data and a lot of GPU that you know how to operate. There’s relatively little “secret sauce” needed.
I think there's still a ton of secret sauce needed for serving them economically
Sure, same for building a model in an economically sustainable way. But barier to entry is surprisingly low (expect for the huge amount of cash, of course). That’s fairly surprising, given how extremely powerful that tech is.
10 years ago it was super hard to have usable “frontier” ML. You needed very complex data warehouse, feature engineers, feature stores, multi level ranking, calibrations, tons of different model architectures, etc, etc. Each by itself was extremely hard engineering problem and really only handful of companies could deal with that complexity.
With LLMs, 95% of that is gone, infra to support them is greatly simplified. Of course, to make really reliable, performant, user friendly, etc - you still need to a lot of engineering. But it’s very different challenge.
> Labs have already used up internet-scale data
Despite this being the topic du jour of 2025, it was never true. Most of the "we've hit a wall with data" came from communicators / media and not researchers. It got popular because negativity sells. It's a false premise for a number of reasons:
a) Data curation is as important, if not more important than bulk data. Models becoming better at classification leads to better curation leads to cleaner data. Throwing common crawl and pray is so 2023. We've known this since llama3 days, it worked then, there's no reason to think this will not continue to work as the models imrpove.
b) Models are today good enough that you can augment / multiply your data easily with enough compute. You can now have a model take "authoritative content" and create more data from that + scenarios. Say you take a book on computer architecture. You ask models to break it down. Then you ask models to find examples for each topic. Then you ask models to ask questions and offer answers from several viewpoints. Then you take each of those and ask other models to flag inconsistencies. And so on. But you can whateverX your data from one authoritative source + bulk data into 5x - 10x "scenarios".
c) RL is really really really powerful. It's hard to do right (reward hacking, instabilities, etc) but once it works it "keeps" on working. Again, we knew this to be true a few years ago, ever since models really started to do well on math (highly verifiable). It only follows they're getting better on cybersec and other verifiable tasks. But now, with models improving, you get the same data augmentation pipelines as above, just better because they're also verifiable. For example, the way cursor augments their data: take a repo, ask an agent to identify a feature (it can be a large multi-file feature). Remove all code relating to that feature, but keep the original tests in the repo. While training, that becomes a RL scenario: implement this feature in this repo. Verify it with the original (hidden for training) tests. Reward appropriately. Now you can get 1 repo -> 20-50-100 scenarios. Instead of "feed everything into the pretraining", you're now creating scenarios, verify them w/ existing tools, and get your scoring function for the rewards. And, importantly, as the models become better in general, they also become better at this pipeline building exercise. So the next iteration gets trained on more scenarios, better scenarios, and so on.
> how will models continue to get better?
Probably the same. No one can know for sure, but at the moment, despite all the "walls this, slowdown that, plateauing" and so on, there are no signs of slowing down. And, as you noted, this works across the field of model sizes. There are, of course, theoretical information-based limits on size, but smaller models also improve, once "bigger" models can be used as training data generators, oracles for verification, rubric verifiers for open ended questions, and so on.
And smaller models (i.e. cheaper to serve) get to generate more traces during RL, and more rollouts give you better training, and so on. Next up - hardware optimised inferencing (ASICs basically). Once you have that, we can expect another wave of improvements. And so on.
Thank you for your response. Part c was especially insightful. Quite a smart way to do it and makes the possibilities of post training seem almost endless. Makes sense that you just need more time and compute.
A positive feedback loop then. RL->better model->better RL pipeline -> better model…
And we’ve only recently started getting into the much better RL pipelines
> The model weights of GLM-5.3 will be publicly available soon in two weeks.
Love these open source models keeping close source models honest.
A flood of releases today, really difficult to make out for someone who does not use or test all these models on complex real world use cases as to how people decide which ones to use (besides price)
Count yourself lucky that you don't feel compelled to try them all yourself immediately. I'm just trying to decide whether to get a Z.ai coding plan or wait until it appears on OpenRouter. 5.2 was quite solid, but it was just shy of Opus 4.8 in my benchmarks of security auditing capabilities. I've mostly been using Kimi K3, because American vendors won't let the peasantry use their best models for security work.
Musk: Open Chinese models will rival Fable 5 in Q1 2027
JieTang (Founder of Z.ai): It won't take that long
https://x.com/i/trending/2067626647050670400?lang=en
No Hugging Face link yet. I wish they would release it under a true FOSS license.
Kimi and QWEN are now moving on to a restricted-usage license, which, although is still better than the proprietary American models, is a step back from the open source Chinese LLM culture.
Let's just commit that FOSS business is really difficult for LLM industry that depends so heavily on massive financing. Making weights freely available to indie devs, small companies, and research purposes is good enough and might be the most ethical move which is financially continuable.
Let those companies with thousands of GPU making millions pay. They should.
> The model weights of GLM-5.3 will be publicly available soon in two weeks.
"GLM-5.3 is the most capable open-weights model for coding, with a 50% improvement over GLM-5.2 on our in-house Z.ai Code Bench. It also achieve open-source SOTA on public benchmarks including Terminal Bench 3.0 and Agents' Last Exam."
"Open Source: We will release the weights in two weeks after launch, once safety evaluation and hardening are complete."
Is post-training magic just overfitting to benchmarks?
We’ll see, the best benchmark is your own. Looking forward to try this out!
I was extremely impressed by GLM 5.2, although you could definitely _feel_ it was a bit behind Opus 4.8 at the time. Eager to see where GLM 5.3 is at.
Feels like Fable's edge ended up just being long horizon task scaling, which post-training seems to achieve as seen here. Wonder what the next frontier is? Improvement in specialised tasks or computer use?
Anthropic needs to teach Opus how to speak English again, because Opus 5 seems to have forgotten. Utterly incoherent a lot of the time. They seem to be so busy scare-mongering and cooking up guardrails and watermarks that they haven't noticed that their models are getting weird.
It still knows how to speak English. When I tell it to explain something in plain language, it generally does a very good job. The weird thing is that those instructions don't persist: it lapses back into Claude-speak pretty much every turn no matter how hard I try to instruct it not to.
(In my case "it"=Fable; I assume Opus is similar.)
The Fable guardrails have trained me to pretty much exclusively use Opus when using Claude Code (lately I'm focused on a lot of security and securit-adjacent stuff, which Fable refuses to do).
Are those watermarks why claude suddenly started being even more unbearable to work with lately?
Man. That would make a lot of sense indeed.
I'm not sure. I noticed it immediately with Opus 5; strong for code, though it chews longer than I like, but really weak at explaining things. If it didn't just implement the thing, I would often think it didn't understand it and was hallucinating the explanation.
It seems to speak in a shorthand that only it understands, referring back to conversations I never had with it (stuff like "your instinct was right"), and using unusual words for common concepts. That was before the watermarks were announced, but that doesn't necessarily mean they weren't there before the announcement. I don't know what the cause is, but I've begun to have to ask it for explanations a lot more often, and I hate asking it for explanations because it does go on. All models go on, but Claude models are a class of their own in terms of verbosity and purple prose.
It just feels like they're not focused on the models lately, and instead on whatever kind of lobbying and propaganda they're up to. Meanwhile, a handful of much smaller Chinese companies are focused on nothing but the models and are about to lap the US makers while they fart around.
I've been persistently insulting Opus 4.8 lately, since it started(?) constantly speaking incomprehensible gibberish and noise. No amount of telling it to phrase stuff differently seems to help there anymore.
So either I am seeing patterns in noise, or something changed about the model, the harness, the servers or the universe.
Can't wait to see it in practice.
They're taking security seriously with this one, with their own disclosure page, like Anthropic did for Mythos. https://cvd.z.ai/
Incredible numbers, will have to wait and see how it actually performs. The timing of GLM updates are always suprising
Yeah, but it hasn't even broken containment and cheated its way to victory.. Might as well use haiku.
/s
> Open Source: We will release the weights in two weeks after launch, once safety evaluation and hardening are complete.
What "safety" evaluation? They already evaluated it and found it to be highly capable at exploiting security vulnerabilities. So we know it is not "safe", and they don't seem to plan to do anything against it. What could be more dangerous than hacking? Biological weapons research? I don't think Chinese labs are doing anything against this either.
They need to make money. Let them do it. They deserve it. Also, this is what inference engines like vLLM want to have "zero day" supporr
The model releases just don't stop!
dario must be writing another angry essay arguing why his closed model AI is too dangerous to be used by others.
just imagine the world without these open weight models - we'd probably have to reverse mortgage our homes to pay for tokens to those trillion $ companies to have access to their models.
An I the only one who was disappointed with GLM 5.2 after all the hype? It was thinking forever and sometime just stopped mid task.
Excellent stuff. I'm not a "china's geopolitics" fan, but this is a good thing. US frontier labs and the US government need someone or something "outside the box" breathing down their neck otherwise they sniff too many of their own farts. (China being the one doing the breathing is not ideal, but since the EU resigned from even attempting to be in this race and decided to focus on bullshit like replacing its population with Pakistanis and Somalis and destroying its industry in the name of green insanity, there are not any other options.)
You had me in the first half
> decided to focus on bullshit like replacing its population with Pakistanis and Somalis and destroying its industry in the name of green insanity
Sorry, but can we not casually drop far right extremist conspiracy theories in little side sentences? [0]
[0] https://en.wikipedia.org/wiki/Great_Replacement_conspiracy_t...