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Alex Corrino

@AlexCorrino

Funny that both @SemiAnalysis_ and @zephyr_z9 wrote tweets trying to debunk the idea that KDA (Kimi Delta Attention) lowers aggregate HBM demand, but neither one has mentioned the most obvious argument: It's exceedingly likely that the frontier labs have studied this design-space and either implemented some of KDA's concepts already or discarded them because of specific trade offs. The original paper laying out KDA was published in October 2025, and it was itself a follow up to a paper from 2024. We've seen open source models from Google and OAI that approach and handle the issue of context management in both similar and different ways. Yes, Kimi's quality is incremental data saying that KDA is a viable approach. The underlying idea isn't new, and there's zero reason to assume the frontier labs are only learning about this now.

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Kimi.ai

@Kimi_Moonshot

Kimi K3 has received far more love than we expected, and our GPUs are feeling it. Over the past 48 hours, demand has pushed close to the limits of our current capacity. To protect the experience of existing subscribers, we're temporarily pausing new subscriptions and prioritizing compute for current members. Existing subscribed users are not affected. We're adding capacity as fast as we can and will reopen new subscription spots in batches. Going forward, we'll also split membership into two more focused plans: Kimi Membership for Kimi Web, App, and Work; and Kimi Code Membership for coding workflows. This will help us match compute more precisely and keep the experience stable. Thank you for your patience and understanding!

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Zephyr

@zephyr_z9

Yes Take Kimi K3 for example, the total parameters are 2.8T (served at fp4, so the total size will be around 1.4T) Kimi K3 is also the sparsest frontier model on the market at just 1.7% They have at least 75%-85% GM on inference

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Kun Chen

@kunchenguid

ok just spent a morning with Kimi K3 as my firstmate, here's my real experience 1. it's very, very slow potentially due to the fixed max reasoning. you should expect the experience of something slightly slower than fable 2. its claimed cost efficiency is not manifesting in real economics i bought the $40 plan, and a few prompts later it's already eaten 1/3 of my 5-hr limit - it was in a single session and my context window was only 200k long at that time i don't care what the benchmark numbers say, and what the face value API pricing is, in reality Kimi K3 burns my Kimi subscription as quickly as Fable burns my Anthropic plan - i observe no efficiency benefit 3. its instruction following capability is weaker than other frontier models firstmate stretches frontier models' reasoning capability and is a really good test that can quickly reveal how good a model is at following instructions the pure "intelligence" of K3 does hold up - it understands my intent very well, and can diagnose problems, delegate tasks all fine but i very quickly noticed many instructions in firstmate's system prompt not strictly followed by Kimi K3. these were never a problem with gpt 5.5, 5.6, opus, fable and grok 4.5 so all in all, i'm now very skeptical of the claimed performance and going to keep my eyes wide open on its true capability

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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.

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Claude

@claudeai

Beginning July 20, Claude Fable 5 will be included in all Max and Team Premium plans, at 50% of limits. Pro and Team Standard users will continue to have access to Fable via usage credits, and will receive a one-time $100 credit. Demand for Fable has been challenging to predict, which is why we rolled it out to subscription plans in stages, extending access several times as we secured additional capacity.

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Francis

@FrancisCashFlow

everybody hating on serenity posting his -49% pnl is so lame to me its normal to have pullbacks as a trader and he still has a better ytd than all of you its all jealousy that he's grown to 1m followers and they wish they had the same numbers to sell their shitty substack