Mira Network (MIRA)

This is wild firing the same query at GPT-4o mini, Llama 3.1, DeepSeek-R1 and watching on-chain consensus crystallize in real time via Klok On-chain proof before any DeFi execution flips the script on agent risk, no more blind trust in black-box outputs. Mira's nailing that verification layer we've all been chasing in this AI wild west A few things that make it stand out: 1. Multi-LLM aggregation that's actually decentralized, not just lip service 2. Sub-second consensus without sacrificing security feels provable, like MonadBFT but for claims 3. Ties straight into agent actions, where proof comes before action isn't hype, it's protocol Huge for scaling verifiable AI in DeFi. Been testing it myself, and damn, the transparency hits different $MIRA @miranetwork Onward

[Doonbo Circle] MIRA project one-point lesson 🙃 @miranetwork Mira is still maintaining its ranking, while other projects... 🥲 As a Mira expert, I easily summarized the necessity of Mira~ AI hallucination (false information generation) real cases -Fake case generation: AI creates non-existent US legal cases as if they were real, causing issues in court. -Fake paper/author generation: cases where non-existent papers are generated and called "Nature paper". How does the MIRA Network solve this? -MIRA does not trust a single AI; it combines multiple models, human validators, and an on-chain consensus structure to filter hallucinations. Core mechanisms 1. Compare answers from multiple AIs/agents → remove outliers 2. Validators assess factuality → record on-chain 3. Consensus based on Wisdom of the Crowds (collective intelligence) increases trustworthiness MIRA builds a Trust Layer that filters out hallucinations by not trusting a single AI answer but using collective consensus. Isn't it super easy? 😀👍

[Doonbo Circle] MIRA project one-point lesson 🙃 @miranetwork Mira is still maintaining its ranking, while other projects... 🥲 As a Mira expert, I easily summarized the necessity of Mira~ AI hallucination (false information generation) real cases -Fake case generation: AI creates non-existent US legal cases as if they were real, causing issues in court. -Fake paper/author generation: cases where non-existent papers are generated and called "Nature paper". How does the MIRA Network solve this? -MIRA does not trust a single AI; it combines multiple models, human validators, and an on-chain consensus structure to filter hallucinations. Core mechanisms 1. Compare answers from multiple AIs/agents → remove outliers 2. Validators assess factuality → record on-chain 3. Consensus based on Wisdom of the Crowds (collective intelligence) increases trustworthiness MIRA builds a Trust Layer that filters out hallucinations by not trusting a single AI answer but using collective consensus. Isn't it super easy? 😀👍

Happy Thanksgiving, Mira fam 🦃 https://t.co/3aeynEQhxD

Spent the afternoon wiring an agent to @Mira_Network's Verified Generate API SDKs are tidy, pipeline really is plug‑and‑play, and every response came with cryptographic proofs so I could trace why an output passed or failed Saw the throughput numbers line up too: Mira handled >2B tokens/day in October at ~96% accuracy, verifiers shard randomly so no node sees full data, and the slashing/reputation game actually weeds out guessers. Staked a little $MIRA to test earning verification fees and the UX was smooth enough to make it sticky If your agent makes real decisions, routing it through a trust layer that turns verification into yield matters more than hype who else is testing Mira right now?