AI labs are built on foreign talent. That's a fact. Now the US is apparently testing restrictions on 'foreign persons' access to cutting-edge models. 'The Trump administration seems to have targeted only Anthropic
AI
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Governance for agentic era: control AI actions and context
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We need governance built for the agentic era. As agents increasingly act on enterprise data, governance can no longer stop at access. It has to control what AI can do, give agents the context they need to understand the business, and offer the choice to work across clouds,
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AI benchmarks correlations: value in disentangling them
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Everything is correlated in AI benchmarks. The value would be picking apart the correlation
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Artificial Analysis useful but index lacks real-world validity
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I think artificial analysis fills a useful spot in the ecosystem for independent assessment, but the index has very little validity compared to real world tasks. It is just lucky that basically every measure is correlated so you can pick any set of benchmarks and they kinda work
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Critique of AI benchmark using AI evaluation on public questions
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This was not a good benchmark before it was updated and it is not a good benchmark now. Having AIs evaluate the work of other AIs on publicly available questions from a different closed benchmark doesn’t tell you very much. And it is unclear how they establish the human ELO.
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SambaNova ready for RaiseSummit 2026: inference 2.0 with GPU and RDU
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We're ready for @RaiseSummit 2026 🙌
— SambaNova (@SambaNovaAI) 16 juin 2026
Training built the models. Inference put them to work. Inference 2.0 is about disaggregating the workload.
GPUs for prefill. RDUs for decode. The right chip for the right job.
Join us at RAISE Summit in Paris to see what's next:… pic.twitter.com/FNThE5nXi8We are ready for @RaiseSummit 2026 Training built the models. Inference put them to work. Inference 2.0 is about disaggregating the workload. GPU for prefilling. RDU for decoding. The right chip for the right task.
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Combining hardware architectures to split shards and run models simultaneously
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It means combining different hardware architectures to split shards / run models across all of them at the same time
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New article by LeCun: visual learning via temporal differences
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Another interesting article supervised by Yann LeCun! "You Don't Need Strong Assumptions: Visual Representation Learning via Temporal Differences" This article proposes Temporal Difference in Vision (TDV), which is a simple idea for learning vision from videos.
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Stanford method reduces training demands for AI scaling laws
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Scaling laws help AI developers predict the performance of large language models, but they require expensive compute power. Stanford scholars developed a new method that reduces training demands: