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
大多數AI模型仍然期望你盲目信任它們的輸出。 當它們出現幻覺或偏見時,你就得自己承擔後果。 @Mira_Network 完全顛覆了這一點。 每個AI輸出都會被拆分成更小的聲明,並在多個獨立模型之間進行驗證。 只有在達成共識的情況下,結果才會被批准並記錄在鏈上。 沒有單一的失敗點。只有經過驗證的答案。 這種設置已經在醫療、金融和研究等現實用例中顯示出準確性從約70%提高到95%以上。 而且,由於它消除了對持續人類監督的需求, 它最終使AI在不需要監督的情況下可擴展使用。 Mira並不是試圖取代模型。他們正在建立一個信任層,讓你可以安全地使用它們。
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