Here’s what makes @Mira_Network spicy👇 → Most AIs hallucinate. Mira doesn’t just take their word for it. Every output is broken into micro-claims and verified across multiple independent models. → If they all agree, the answer gets the greenlight ✅ If not? It gets tossed out → Onchain logging means every verified output is traceable. → Think of Mira less like an AI model… and more like a referee. It’s not here to compete. it’s here to ensure fair play. →No need for constant human oversight. Mira handles the heavy lifting, making AI actually usable at scale. My thoughts: This “trust layer” narrative is exactly what AI infra needs. We’ve scaled models, now we need to scale accountability. And if Mira can referee edge cases too? That’s championship material.
SKYLINE🥷
SKYLINE🥷8月3日 02:00
大多数AI模型仍然期望你盲目信任它们的输出。 当它们出现幻觉或偏见时,你就得自己承担后果。 @Mira_Network 完全颠覆了这一点。 每个AI输出都会被拆分成更小的声明,并在多个独立模型之间进行验证。 只有在达成共识时,结果才会被批准并记录在链上。 没有单点故障。只有经过验证的答案。 这种设置在医疗、金融和研究等实际应用中,已经显示出准确性从约70%提高到95%以上。 而且由于它消除了对持续人工监督的需求, 它最终使AI在大规模上可用——无需监督。 Mira并不是试图取代模型。他们正在构建一个信任层,让你可以安全地使用它们。
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