@anneouyang
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Ripple-65B Preview was trained on 12 trillion tokens, supports up to 1M context length, and achieves:
- 80.5% on TerminalBench 2.1
- 79.1% on SWE-Bench Pro v2.0-Hard
- 44.3% on DeepSWE v1.1
All while fitting locally on a MacBook or DGX Spark.View quoted post on X
I maintain that King Ferdinand II and Queen Isabella I were the first venture capitalists.View quoted post on X
Intelligence must draw on memory. The mind must not simply compile data — as an algorithm now does more quickly than we can — but recall lived experiences, which contain depths of meaning that only the human soul can recognize, and connect more deeply with the goodness of these experiences. Everything that is good begins “with the Spirit” and thus possesses a breath, a dynamism and a sense of communion that, once experienced, are engraved upon the heart. vatican.va/content/leo-xi…View quoted post on X
If AI can generate CUDA kernels, what does a kernel engineer do now?View quoted post on X
The work is shifting from hand-writing every kernel toward building the systems that make AI-generated kernels useful in practice — helping agents learn new hardware, improving search and feedback, and building better ways to verify correctness. Generation is getting cheaper, but correctness is becoming a critical problem. A generated kernel can look faster on a benchmark but fail in production because of race conditions, stream interactions, output reuse, or other subtle bugs. That makes verification and deep hardware expertise more central to the job.
At Standard Kernel, we’re building that infrastructure. Our cofounder @anneouyang spoke with Business Insider about how kernel engineering is changing.
businessinsider.com/cuda-engineers…
