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that your model is really table stakes, but eval and metrics, that's your most important. That's your strategic moat. So, build your eval before you build your technology.
Like I think we've seen internally at Meta um cases where if you can develop the right agentic loop and you have the right eval or the right metric for the agents to optimize, you can have a swarm of agents accomplish more than like a team of 100 engineers in, you know, uh very very handily actually, very very easily.
I think evals, they outlive the harness a little bit, but not by that much. Like an eval might live for maybe one, two, three model generations, but nowadays the you know, we're on the exponential. The model is improving so quickly, very often we just saturate the eval, and then we have to throw it away, and we have to come up with a new eval.
The panel had a generally positive view of Phoenix, with one panelist calling it one of his “favorite open source eval tools.” The tool is positioned as a developer-first, notebook-centric platform.
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