A coding agent driven by a large LLM can delegate smaller tasks to a faster model. For example searching through the codebase for references, examples, or established patterns. They are treated as tools and don't pollute the main agent's context.
Why are you using the product release date for Kimi K3 and the training date for Fable? Either use the release date for both (6 weeks apart) or if you have it the training date for both.
By Anthropic. The government blocked it after it was already released.
Every LLM product goes through testing and alignment after training, maybe even some quick improvements here and there. Kimi probably did something similar.
Put another way, if Google says they have the best model in the world but won’t release it in December I will start caring in December, not before.
if Dario and the CEO of Moonshot switched places, Mythos would have been generally available to the public 4 months ago. As a statement of fact, it wasnt, because of Project Glasswing and Dario thinking they created a superweapon that the rubes shouldnt have access to.
> First ever comment said "Further releases of Chinese models that demonstrate the gap is not growing substantially is a huge problem. The spending will be called into question."
ah yes, because i said something factually accurate and vaguely positive about Anthropic I must be a shareholder which would mean I either run a venture capital firm or am a current employee of Anthropic...
And given that Chinese models are closing the gap there are basically two thing that could be happening. One is that they are moving faster than US companies developing closed models, and two that we're starting to hit a plateau for model capabilities where all the easy gains have been plucked, and now it's not really possible to move forward at the same rate on the frontier. Of course, both things could be happening at the same time.
Chinese labs have come up with a bunch of genuine innovations: GRPO, auxiliary loss free MoE load balancing, MLA, muon optimizer, and a bunch of other ones. The Deepseek papers are really well written, this isn’t just sneaking a peek at a peer.
The problems are inherently harder now too, partially because they take longer, so your training pipeline is waiting for long completions.
Also there probably is some “distillation” (technically pseudo-labeling, which is common in ML). But I wouldn’t put too much weight on it because that was true 18 months ago as well.
That's my thinking as well. The whole distillation thing is a distraction from the actual innovation happening in this space. What will be interesting to see going forward is what types of new techniques people manage to come up with to over come the current architecture limits.
The process takes time because even when you're distilling answers, you still need to actually do reinforcement training on the model. And given that Fable and GPT 5.6 just came out there simply hasn't been much time to do that. On top of that, Kimi also does better than Fable or GPT on a lot of tasks, distillation alone can't explain that, meaning there is a difference in architecture. You can watch a talk from Kimi founder to see how Kimi was actually trained and why it performs well. https://www.youtube.com/watch?v=5CkCW1P-g88
Not to mention that US companies models constantly distill each other as Musk was forced to admit under oath. This whole narrative has just been a massive cope.
> US companies models constantly distill each other as Musk was forced to admit under oath
> This whole narrative has just been a massive cope.
So wait, US AI companies all use distillation because... it's not effective and it's all just cope? Or is distillation really powerful and they all do it, which Musk was forced to admit under oath? But when China does distillation it isn't powerful and they don't need to do it, but they do it anyway because it's fun?
Either it's powerful and everyone, including the Chinese labs, use it as a way to rapidly catch-up against the SOTA models, or it's a red herring and the huge amounts of energy spent to protect and enable distillation is all just wasted money. Which is it?
I'm saying it's a cope to claim that the only reason Chinese models are catching up is due to distillation, while pointing out that distillation itself is in no way unique to Chinese companies. I'm sorry this was too complex of an idea for you to follow.
The fun part about this is that we can see who is right in about a year. If the leading labs continue making progress at hardening their models against distillation, and then they start pulling away again, we see who is right. If China is able to pass the US and release an independently better model than anything the US has, then your theory is correct.
Both sides have extremely smart people. One side has more $$$ and exclusive access to the best chips. For progress to converge without a corresponding breakthrough suggests there's something else at work.
Indeed we will, my prediction is that we'll see a model from China that definitively surpasses any US model by the end of the year. China has an absolute population advantage here along with having a much better education system. And China now dominates in published AI papers.
The US enjoyed an early advantage due to excessive money being poured into AI which led to the current bubble, and access to the hardware that was needed to train these models initially.
At this point, neither of these factors actually matter that much. The naive approach of simply making models bigger has hit a wall, and now you need ingenuity in figuring out better architecture for them. Precisely because Chinese companies have had to deal with more limited resources, they put a lot more effort into researching different kinds of optimizing techniques. And of course, China is also catching up in chip making, and Huawei clusters are already competitive with Nvidia for training. So, that gap is closing as well.
The big difference is that an absolutely insane amount of money has been spent in the US, while China managed to do this on a fraction of the budget. The AI Investment Surge graph here puts things in perspective. https://hai.stanford.edu/news/inside-the-ai-index-12-takeawa...
My prediction is that they're going to angle to become a vendor of record for the government and get bailed out. That's the only path at this point because there won't be any competition from China in this niche.
how is that related. It downgrade it to opus 4.8 #2 most capable model after claude 5. for a vast majority of topics it will not downgrade. I've been using it for 2 days to talk about architecture etc. and it was absolutely great with no downgrades.
Why wouldn't getting more customers the plan? Anthropic doesn't acquire companies to have a lower market share. There is clearly a consolidation and a rush to get as much of the developer market as possible.
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