I guess we are all tokenmaxxing on xai gpus
COMPUTING
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Shower thoughts: autoencoders as good addition after LoRA
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This was purely based on shower-thoughts :D.
As someone called out, autoencoders would have been a good addition after LoRA. -
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™. -
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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SpaceX launches AI1: AI computing and solar energy in space
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SpaceX has just launched AI1, a satellite designed to relocate AI computing to space and capture energy where it is nearly infinite: the Sun. We are witnessing, before our eyes, the transition from a Type 0 civilization to a Type 1 civilization on the
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SambaNova shows disaggregated inference with up to 2x speed at Computex
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Same prompt. Same model. Two stacks.
— SambaNova (@SambaNovaAI) 9 juin 2026
At #Computex, we demonstrated disaggregated inference live: GPUs handling prefill, SambaNova RDUs handling decode, and CPUs orchestrating agent execution.
The result? Up to 2X the speed of B200-only configurations 🦾 pic.twitter.com/YYP8o6WYrKSame prompt. Same model. Two stacks. At #Computex, we demonstrated disaggregated inference live: GPUs handling prefill, SambaNova RDUs handling decode, and CPUs orchestrating agent execution. The result? Up to 2X the speed of B200-only configurations
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Power shifts to infrastructure in compute-constrained world
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Follow the signal: we are in a compute-constrained world.
— Nina Schick (@NinaDSchick) 9 juin 2026
And that means power doesn’t sit with the models. It sits with the infrastructure.
Most frontier labs don’t own the means of producing Intelligence. They rent it.
A handful of companies provide compute that everyone… pic.twitter.com/iHIEK7ISBYFollow the signal: we are in a compute-constrained world. And that means power doesn’t sit with the models. It sits with the infrastructure. Most frontier labs don’t own the means of producing Intelligence. They rent it. A handful of companies provide compute that everyone
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Skeptic wrong about AI data centers in space due to heat
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Remember that guy who was on here with a video that AI data centers in space would never in a million years happen because of the heat? It's incredible how certain people who are flat out wrong can be just because it's trendy now to be against any sort of technological progress
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Spectacular slope of the precision/cost frontier
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By observing the precision/cost frontier on the FrontierCode benchmark we discussed, we can see the spectacular slope of computation at the time of testing this new model. Look how it rises! Even in the low configuration, the model performs — and costs — more.
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Which Local AI Model Would You Run on Your Infrastructure?
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LOCAL AI MODELS Which model would you run on your own infrastructure? Qwen 3.7 Max DeepSeek V4 Gemma 3 GPT OSS Different strengths: Privacy Control Cost efficiency Performance What are you running today? #AI #LocalAI #LLM #Ollama #OpenSourceAI