After testing Claude Pro and Perplexity Pro alongside Suprmind, we decided to keep all three
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After testing Claude Pro and Perplexity Pro alongside Suprmind, we see clear orchestration benefits over simple aggregation. Sequential compounding of responses and leveraging disagreement as a feature improve accuracy
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Model disagreement helps spot risky inputs by highlighting when predictions don’t align. For example, high ensemble variance shows uncertainty
We’ve decided to cancel Claude Pro and Perplexity Pro to streamline workflows with Suprmind’s superior orchestration
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When models disagree on a prediction, it signals uncertainty that can flag risky inputs. Monitoring metrics like ensemble variance or entropy helps spot these cases early