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Your eval score went from 89% to 92%. That's not an improvement. ⚡ At 100 test cases, your margin of error is roughly ±8 points. A 3-point win is noise. Most teams ship on numbers that can't survive a stats check. 12 AI evaluation concepts — the method, not the tooling 👇 🟢 THE SET 1. Golden set — expected behaviour, written down and versioned. 2. Slices — 92% overall, 61% on long context. The average is where failures hide. 3. Sample size — n=100 means ±8 points. You need ~1,000 cases to detec
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AI Race between open vs closed models just entered the next level. Crazy takes like calling open models decelerationist by virtue and calling open models a threat and are heading down a dangerous path are only some criticism against open models showing how much threat they pose to close labs' business model. The further commoditization of models is really pushing further pressure on frontier labs especially for some that are eyeing IPO in the next year. #artificialintelligence #openmodels #kimi
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