Measuring AI R&D without pretending a benchmark is the whole story
What caught my attention here is not the “three metrics” framing itself, but the fact that it pushes against the usual lazy habit of treating benchmark scores like they explain everything. They don’t. A model can ace a research-ish eval and still be almost irrelevant in actual decision-making, or the reverse: mediocre on paper, but quietly shaping a team’s workflow in ways that matter a lot more than the chart suggests. That part feels right to me. If you’ve built anything with Claude or another
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