Hm, I don't think this looks like Anthropic's design style. Anthropic is kind of doing a Chobanicore + Corporate Memphis design system that I personally find kind of creepy. But the website here just feels fresh and pleasant.
Agreed; that's a beautiful site. The main design style apart from minimalism that I notice is glassmorphism. Well, that and a very well chosen Monet to set the tone.
Well both aren’t “more important”, since that’s illogical. I think recent strides in high performance small LLMs have shown that the tasks LLMs are useful for may not require the level of representational capacity that trillion-parameter models offer.
However: the labs releasing these high-intelligence-density models are getting them by first training much larger models and then distilling down. So the most interesting question to me is, how can we accelerate learning in small networks to avoid the necessity of training huge teacher networks?
This is just blind belief. The model discussed in this topic already outperforms “well made” frontier LLMs of 12-18 months ago. If what you wrote is true, that wouldn’t have been possible.
Absolutely. Plus as these companies become hungrier for revenue and to get out of the commodity market they are in, they are only going to get more aggressive in their (ab)use of customer data.
I would recommend trying oMLX, which is much more performant and efficient than LM Studio. It has block-level KV context caching that makes long chats and agentic/tool calling scenarios MUCH faster.
It wasn't even the local-ness so much. Even if they stored at remotely it would be okay like ChatGPT or Claude but unlike the others for a long time the only way to let it store history on their servers was also allowing them to train on it. I haven't checked if it's changed.
That amount of RAM won’t be necessary. Gemma 4 and comparably sized Qwen 3.5 models are already better than the very best, biggest frontier models were just 12-18 months ago. Now in an 18-36GB footprint, depending on quantization.
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