Fable is really smart and replies very differently from other models
GENERATIVE AI
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A model more capable than a year ago, priced below Opus 4.1
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If you really look at it, this model, much more capable than what we had a year ago, is released with a price below Opus 4.1.
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LLM shadow-banning was not planned for 2026
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The shadow-banning of LLM development was certainly not on my bucket list for 2026.
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Limiting Claude for cutting-edge LLM development widens the gap
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“limiting Claude’s effectiveness for requests aimed at developing cutting-edge LLMs” And that’s how they plan to widen the gap even further
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34 days from signing deal to Mythos-class model GA launch
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for those keeping track at home it was 34 days between signing this deal and launching Mythos-class model GA to the world. https://
x.com/leerob/status/
2052059466821198061?s=20
… building on @nvidia stack means you can just do things™. -
User ran out of tokens, generation cut off at ‘End World’
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Didn't have enough tokens so it cut off at 'End World'
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Anthropic’s new Fable 5 safeguards quietly limit effectiveness
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Anthropic’s new Fable 5 safeguards are fascinating. When the model is used for frontier LLM development, it apparently does not simply refuse or warn the user. Instead, it quietly limits its own effectiveness through techniques like prompt modification, steering vectors, and
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Apple’s Core AI runs models entirely on-device
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Apple finally did it. Its new framework, Core AI, runs models entirely on Apple silicon, so inference happens on the user's device with zero server calls and zero token bills. That means Qwen, Mistral, and SAM3 running natively across iPhone, iPad, Mac, and Vision Pro. It's a
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Anthropic would limit capabilities to maintain competitive advantage
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Pretty crazy this that is being shared where Anthropic would be limiting the model's capabilities when used to improve and create better LLMs. They sell it as a security measure but it is clear that they do it to maintain their competitive advantage.
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OSCAR: 2-bit KV cache for LLMs without accuracy loss
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Can LLMs run on ultra-low-bit memory without tanking accuracy? Researchers from Together AI, University of Sydney, and UIUC present OSCAR — a method that uses offline, attention-aware covariance analysis to design fixed rotations and clipping thresholds for 2-bit KV cache