Uberdeus

2.5K posts

Uberdeus

Uberdeus

@uberdeuss

Katılım Mayıs 2017
234 Takip Edilen62 Takipçiler
Sabitlenmiş Tweet
Uberdeus
Uberdeus@uberdeuss·
How QAnon spreads
Uberdeus tweet mediaUberdeus tweet mediaUberdeus tweet media
English
0
0
1
0
Uberdeus
Uberdeus@uberdeuss·
@MoloWarMonitor I don’t get it. He could use b2 to drop nukes as well, so what they will try to drop instead?
English
1
0
0
245
MoloMonitor 🇮🇹
MoloMonitor 🇮🇹@MoloWarMonitor·
A few minutes ago, while speaking at the White House, President Trump confirmed that the U.S. will "hit the area pretty soon and very heavily," referring to Pickaxe Mountain, where Israeli intelligence says Iran transferred the enriched uranium. However, as I've already said, the only way to destroy or damage this facility is to drop a tactical nuclear bomb, or alternatively, to recover the material through a highly risky ground operation. Most likely, however, they will first attempt to destroy the fortified entrances using B-2 stealth bombers.
MoloMonitor 🇮🇹@MoloWarMonitor

Yesterday, while speaking at the White House, President Trump said he wanted to strike "Pickaxe Mountain," the deepest uranium enrichment site in Iran. The facility is so deep (about 100 meters inside the mountain) that during last year's Operation "Midnight Hammer" against Iranian nuclear sites, U.S. B-2 bombers did not even attempt to strike it. In fact, destroying or damaging this base would require a tactical nuclear weapon or a ground operation.

English
16
19
96
7.1K
Uberdeus retweetledi
Steve Hou
Steve Hou@stevehou·
There def seems to be some degree of “distillation” or training on synthetic data generated from a more powerful model. I’d be shocked if there wasn’t. But this analysis is still intriguing. Acc to it, apparently Zhipu/GLM has been distilling Gemini 3.1 Pro? What I also find remarkable is how little DeepSeek seems to have distilled from anyone else. And apparently nobody has bothered to distill from OpenAI. 😂
Steve Hou tweet media
Lisan al Gaib@scaling01

pinky promise there's no distillation

English
8
9
60
22.8K
Uberdeus retweetledi
Uberdeus retweetledi
李其 Lizzi
李其 Lizzi@wstv_lizzi·
Been having some really enlightening conversations with this new generation of Chinese tech entrepreneurs, and I keep coming back to the sense that almost everyone is a 做题家, a kind of exam-trained problem solver. (By the way the term is a little derogatory in Chinese. Its more specific cousin 小镇做题家 / or small-town exam striver, refers to kids from smaller cities who study their way into elite universities, only to discover that doing well on exams does not give them the social capital or broader fluency of the urban kids...) But I don’t mean it dismissively here at all! There’s almost a kind of purity built into that mindset: you have a target (used to be the Gaokao, IMO gold medal, or now some global AI ranking or benchmark…) and you have this huge, incredibly capable community of other 做题家 competing with you to get there. What’s interesting to me is how little interest many of them seem to have in the grand philosophical questions or, for better or worse, politics... Even revenue and business model strategies feel secondary unless they are part of the formula for solving the problem / 做题. Many are genuine tech purists, a little surprising to me especially given the complicated geopolitics around them! They’re also very honest about themselves being 做题家. (Many call themselves that! Or some variation of the term, like 理工男.) And many point out that, among all the challenges they face, from compute restrictions to the current legal uncertainty, the hardest is the transition from 做题家 to 出题家, i.e., from solving a clearly defined problem to deciding which problem is worth solving in the first place. We know China is exceptionally good at asymptoting from No. 2 to No. 1, as No. 1 effectively gives you the problem and the target, and that’s where 做题家 shine. But what happens when the 题 / exam question is deciding what the next target should be when there is no benchmark… FYI trying out Substack notes now... Will probably write more (random!) thoughts over there: substack.com/profile/315221…
English
11
13
69
13K
Uberdeus retweetledi
Stratechery
Stratechery@stratechery·
Who’s Afraid of Chinese Models? Everyone is worried about Chinese models, but the frontier labs will be fine; we need to enable open U.S. alternatives. stratechery.com/2026/whos-afra…
English
46
131
818
469.8K
Uberdeus retweetledi
Kyle Chan
Kyle Chan@kyleichan·
China’s Ministry of Commerce is talking with AI companies such as “Alibaba, ByteDance and Zhipu on limiting the transfer of key data for the training of their models overseas, as well as allowing their model weights to be downloaded by foreign users” FT: ft.com/content/6049a0…
Zijing Wu@zijing_wu

Scoop: China weighs tighter export controls on 1. taking data overseas for training; open model weights 2. advanced chip fab overseas 3. acquisition of AI start-ups * Regulators are consulting tech firms with no final decision made as.ft.com/r/fc745fce-628…

English
9
23
115
18.8K
Uberdeus retweetledi
Tommy
Tommy@Shaughnessy119·
This is an absolutely fantastic take on the U.S. and China AI discussion. No notes, read it.
nic carter@nic_carter

havent seen one person from OAI or Ant address Jon's argument here. the point is simple: the USG does not owe either of the large labs a business model. if the economics of selling tokens don't work due to distillation/cheap clones/Chinese AI magick, the American enterprise and consumer will be A-OK. they will benefit from hyperdeflation in the cost of digital cognition just like everyone else. the hyperscalers will be fine. it's just OAI and Ant that won't be – in their current forms at least. if they are willing to adapt, they can develop new business models. so what if the token merchants don't do well? the neoclouds will be fine. the internet companies will be fine. the consumer gets cheaper queries. the enterprise will still incorporate AI. the only world in which this isn't fine, is if you hold a quasi-religious belief that we're on the cusp of a kind of AI rapture in which one of the labs Logs On And Wins Forever, namely hits RSI and we enter some kind of sublime post economic society run by GEOTUS Dario. so to accept that Ant's business model might be suboptimal or impaired by China's commoditization is to accept the unacceptable; namely that someone other than the anointed might kick off the runaway feedback loop and that they, instead might log on and win forever. this appears to explain the discrepancy in reaction to Deepseek Moment v254 Kimi edition. everyone has bag bias, of course. but leaving that aside, most people think it's pretty much ok if Ant and OAI suffer margin compression due to Chinese distillation / industrial sabotage via open weight models. the American economy is not reliant on those two firms. they could blink out of existence and we would pretty much be ok. the AI capex supercycle will still produce tokens, closed weight or not. American firms will consume those tokens. OAI and Ant would probably still scratch a living, due to the latent preference of some token consumers to buy domestic and face off against a known entity. this is only unacceptable if you think AI is strongly path dependent; that is, if it really matters who the market leader is when AI reaches a breakout level of capability. this is true both in the good case (superintelligence, singularity, etc) and the bad case (this is the essence of safetyism). but if this sounds more like wishcasting than forecasting, you probably don't mind the labs being pressured economically. now you can clearly tell which side I'm on. I think AI is a fantastic technology which is hyperdeflating the cost of cognition and will fundamentally reshape society but there are real reasons why it wont diffuse as fast as the AGI people think it well. I would prefer an American firm achieve RSI relative to a Chinese one but I think either outcome would be suboptimal; better that we don't end up with a closed oligopoly composed of Ant/OAI. China by crushing the margins of the labs is doing everyone a favor by eliminating their pricing power and empowering the buyers of AI, namely, everyone. objections: -but you can't celebrate America losing to China! - in my opinion this is a minor victory for China but not necessarily an enduring one. USA still has the chip, datacenter, and neocloud advantage, not to mention, it still has the best frontier models. Chinese labs releasing open weight models have no business model of their own. so even if they hurt the US labs, they have nothing to show for it. it's profoundly unlike their successful dumping campaigns with solar panels, batteries, drones, etc where they eventually built big domestic industries. (if China kills American AI with open weight models, we can even the score the moment they try and release a proprietary model). even if open weights win, the USA can still leverage AI extremely well and potentally retain the aggregate compute advantage. yes, the US would be more assured of victory if OAI or Ant won forever, but I don't know if I want to live in that world. - no one will ever train a model again - this is where I think the concern is unwarranted. let's say distillation really is a golden bullet and kills big training runs. that doesn't advantage either China or the US. that's a stalemate. not to mention, the trend seems to be less focusing less on massive pretraining budgets and more on finetuning for specific genres of tasks, thinking machines style. and lastly I find it hard to believe that training runs will stop altogether. the labs can probably develop anti-distillation techniques. you could adopt a whitelist style permission for everyone using your model. different consortia could be put together to share in the cost of training a model, if it is seen as too expensive for an individual firm. - the AI buildout is path dependent and OAI/Ant are now load bearing GDP infrastructure - it would be a significant setback for investors if they had to cancel their IPOs and suffered big markdowns, and some neoclouds with lab based RPOs would suffer for a while, but everyone would be fine, really. does Microsoft need OAI or Ant? does Meta? does Google? ordinary Americans have ~no exposure to either OAI or Ant. would the world want any less compute if it turns out to be another order of magnitude cheaper? certainly not. as we all know at this point, consumption would go up. I don't think the economy is so dependent on the labs that it couldn't handle their margins compressing.

English
14
20
240
74.2K
MoloMonitor 🇮🇹
MoloMonitor 🇮🇹@MoloWarMonitor·
According to my sources, Israeli PM Netanyahu will hold a closed security meeting tonight, as tensions escalate in the Middle East. Meanwhile, dozens of USAF fighter jets and refueling tankers continue to arrive from American bases in Europe, landing at various airports and air bases in Israel. I believe preparations for a major escalation will be completed by the end of the week, and the new operation could begin Friday night, when markets close. For now, however, to avoid worsening the market situation at the opening, a Reuters report is circulating about a possible 10-day pause in attacks to create an opportunity to revive the MoU between the U.S. and Iran.
MoloMonitor 🇮🇹 tweet media
English
11
58
196
23K
Uberdeus retweetledi
Foreign Affairs
Foreign Affairs@ForeignAffairs·
“The most urgent challenge confronting China’s leadership may not be how to surpass the West but how to rebalance its economy before the world forces it to,” write Enrico Fardella and @DrRadchenko. foreignaffairs.com/china/china-sa…
English
7
19
61
28.6K
Uberdeus retweetledi
Deedy
Deedy@deedydas·
The story of AI in the next few years is going to be compute: an essay on the future of AI. K3 in 2 days is already #10 on OpenRouter with ~140B tok/day, and it’s infra is crumbling. Throughput is down from 30tok/s to 13tok/s, E2E latency is up to 72s and time to first token is >20s! It would cost a minimum of $500k to buy the 8 B300s it would take to serve even quantized Kimi K3 and ~$4M for the more recommended GB300 NVL72 rack. I don’t think Moonshot has the compute available to scale to their demand! In fact, even the US based inference providers will likely not be able to scale capacity as much as they’d like even if they were to host it: a 2.8T model is no joke. GPU providers (neoclouds etc) are doing 3yr and I recently hear 5yr commits with an ungodly 30% down, and customers are chomping it up. Prices continue to go to the moon. The two big labs, hyperscaler clouds, Grok and Meta have compute deals locked in prior, and the rest are fighting for scraps. Tier 1 neoclouds (coreweave/nebius etc) are rumored to not even small “smaller” customers. Meta is the biggest wildcard here. With ~7GW of compute by eoy 2026 and no clear big model ties, they either get to frontier on their own or can host the most Kimi K3 capacity (unless they sell it to the labs). Even though the price of models has fallen over time, it’s worth noting that the price of frontier has not. 3yrs ago, GPT-4 released at $60/M, o1 at $60/M, Opus 4 at $75/M, GPT5 at $10/M, Fable at $50/M and now Sol at $30/M and K3 at $15/M. Even if you consider K3 frontier, that’s only a 4-5x flux in 3yrs. In that time, frontier demand has increased at least 3+ ooms and frontier intelligence performance has gone 32x at least by task time by METR. Essentially, so long as a) the demand for frontier intelligence continues to grow to near infinity, b) the frontier continues to grow in performance, even as c) if the price of frontier declines a little, the value accrued to frontier grows significantly! And there’s a tremendous bull case for those who have locked up compute if you’re bitter lesson pilled and believe larger models will always be smarter models.
Deedy tweet media
English
123
176
1.7K
191.4K
Uberdeus retweetledi
李其 Lizzi
李其 Lizzi@wstv_lizzi·
We wrote a while ago in our private newsletter about the different positioning of Beijing, Shanghai, Hangzhou, and Shenzhen as China’s four 4 AI innovation hubs just as @kyleichan said and the bureaucratic competition underpinning each ecosystem and among them. (should probably update that to include the emerging Hefei Wuhan nexus which is increasingly central through eg CXMT + YMTC, optoelectronics, the broader memory and hardware cluster etc) It is also a good time to remind readers that AI competition is not only a U.S.-China issue. It is also a (fierce!) subnational competition within China itself. Cities and provinces are competing for talent, capital, good companies, and Beijing leadership attention! while Beijing leadership is running the horse race but also trying to coordinate these rival ecosystems toward one national goal. Lots of tension between local bureacratic competition and national mobilization (another defining feature of China’s AI push less discussed…) Will probably write something public about it at some point…
Zichen Wang@ZichenWanghere

The city of Beijing emphasizes that China's two latest high-performing LLMs, the Kimi K3 and GLM-5.2, originate from Beijing. It cannot allow Shanghai, the city hosting the ongoing World Artificial Intelligence Conference (WAIC), steal the limelight of AI without a fight.

English
3
21
101
20.7K
Uberdeus retweetledi
Michael Pettis
Michael Pettis@michaelxpettis·
Shenwan Hongyuan's Zhao Wei: "Weak income expectations, deleveraging and poor social protections are not isolated phenomena. They all stem from a fragile employment market. The structural vulnerabilities in China’s labor market directly suppress wage growth and exacerbate the need for defensive savings." We are seeing an increasing number of analysts arguing publicly that wage suppression has been the structural reason for China's weak consumption growth which, in turn, leads to the economic fragility that further pushes up household saving. caixinglobal.com/2026-07-17/com…
English
6
19
111
22.1K
Uberdeus retweetledi
Institute for the Study of War
NEW: Japan is developing a centralized intelligence agency for the first time since the Second World War, with advice from its allies and partners. The agency will help counter PRC and Russian espionage in Japan and enhance Japan’s contributions to a US-aligned coalition in the Indo-Pacific. (1/2) Other Key Takeaways: PRC academics and state media promulgated a narrative that the Philippines’ northern Batanes Islands belong to the PRC. The PRC may be setting information conditions to expand coercive patrols around the Batanes to strengthen PRC access and control in the Luzon Strait. PRC authorities are likely detaining and questioning Taiwanese nationals who travel to the PRC to recruit collaborators and intimidate pro-independence voices.
Institute for the Study of War tweet mediaInstitute for the Study of War tweet media
English
18
223
611
83K
Uberdeus retweetledi
U.S. Naval War College
U.S. Naval War College@NavalWarCollege·
China Maritime Studies Institute's, China Maritime Report #56: The Silent Service: Assessing PLAN Influence in the Central Military Commission Full report: tinyurl.com/4pp83knm
U.S. Naval War College tweet media
English
0
67
167
40.8K
Uberdeus retweetledi
SemiAnalysis
SemiAnalysis@SemiAnalysis_·
A year ago, the big three was OpenAI, Anthropic, and Google. Things have changed. Moonshot's Kimi K3 sits above Gemini on every composite benchmark, and it's open source in 10 days. New episode: what K3 reveals about frontier margins, model sizes, and who's actually still in the game. 00:11 Is Kimi K3 the Third Best Model? 04:04 Why Delay the Weights? 05:30 2.8T Parameters and Serving Constraints 06:48 Frontier Margins and the 3x Price Hike 11:10 New Architecture, What Comes Next 14:09 Will Open Source Catch Closed? 19:51 Built for Chinese Accelerators 22:57 The Harness Is the Product 28:49 We're Still Early
English
52
79
933
119.8K
Uberdeus retweetledi
Dean W. Ball
Dean W. Ball@deanwball·
Some observations on Kimi: 1. It's a very good model! I don't think its performance can be explained away by distillation or anything like that. In agentic coding sessions, it seems pretty much on par with the best public models of Q1 2026. In my fairly limited use, it also seemed very token hungry. It's not obvious to me that this model is actually that cheap to run. 2. I am personally surprised the Chinese state continues to allow the open sourcing of models this good, given potential risks. To be clear, I *myself* might be fine with models presenting this level of marginal risk being open weight, but I am surprised that China is fine with it. I suspect the reason they are is 75% explained by strategic blindness/lack of AGI-pilledness (the CCP is very Yann Lecun-y in its views of AI). The other 25% or so is their lack of compute for customer inference (making China's open-weight strategy an unintended byproduct of US export controls) and the normal Chinese strategy of aggressive exports. For the companies, as opposed to the government, the decision to open source is partially ideological and partially because they are behind, and they know that very few people would pay for sub-frontier models from China. 3. Open-weight models are inherently decelerationist, and I'm continually surprised to see the so-called "accelerationists" so excited about open-weight models. I suspect the reason they are is that they know open-weight models are effectively ungovernable, and they simply like the overall cloak of ungovernability open-weight models create over the whole of AI. It's not a bad strategy; it reminds me of James Scott's recounting of the hill people in "the art of not being governed." Still, in the end, open-weight models deter further AI capex. 4. One probable outcome of an open-weight-model-dominant world is full AI communism, which is precisely what China proposes: rather than a market product, AI is a "public good" which will ultimately be provided by the state as a kind of "digital public infrastructure." This future strikes me as a dystopian hellscape, but I've never met an open-weight models advocate who doesn't ultimately concede this is where things end. You'd be surprised how many 'accelerationists' lobbied me, while I was in government, to support an eleven or twelve-figure federally funded data center so that startups could train models at a subsidy and then give them away for free. There was no other way for AI to progress, they said. Perhaps this is the logical end state of things. Nonetheless, I find myself surprised to see supposed accelerationists excited about such an outcome. I think many of them just don't know what they're doing. Many accelerationists do not view the creation and serving of frontier models as a legitimate business. 5. I would guess that the Trump Administration will at some point realize that their best strategy here would be to create large amounts of regulatory risk around the use of open-weight Chinese models. You don't need to "ban open source" (one of the dumber motifs of AI policy discussion). You just need to direct every agency to issue soft law that creates FUD. "A Federal Reserve Advisory Bulletin found that there may be backdoors in Chinese AI models." It needn't be that well justified. You just create enough regulatory risk that every regulated enterprise backs off. You probably don't want to create so much regulatory risk that you scare off the hyperscalers from serving Chinese models; this will just drive startups to sketchier providers. There's a happy middle ground here. I'd assume they will do some version of this. 6. It's probably true that open-weight models of this capability make the world a bit more dangerous, but not so much more that you'll really notice. At some point the models will be capable enough that you will notice. "A nonliving, invisible, dangerous, and infinitely self-replicating agent escaped from a Chinese lab," you say? Color me shocked.
English
2.1K
923
7.6K
11M