Just finished researching the verification layer of @Mira_Network, and I suddenly realized: the reliability of AI is the key!
The pain points hit the mark, with speed lags and skyrocketing costs creating illusions everywhere, and products stuck at the toy level unable to progress.
But their Verified Generation is impressive, breaking down units for cross-model consensus, with a 96% accuracy hitting the soul; stored on the Base chain forever, Irys offers low-cost instant checks, and the cost of Learnrite has been slashed while Delphi takes a hit, and Klok is all connected.
The 0.1% $MIRA incentive for creators is clear, and KaitoAI's strategy is straightforward, using citation analysis to climb the rankings, reliable.
Don't get lost in the model hype; first build a trust layer, let AI output become a data stream, and long-term profits will be stable with $MIRA.
Just researched the verification layer of @Mira_Network and realized that solving AI's "reliability" is the right path.
Pain Points
Slow speed, high costs, and common hallucinations; it's hard for products to transition from toys to business.
How to Achieve It
Verified Generation breaks outputs into verifiable units, cross-model consensus (GPT-5/Claude 3/Llama 3.3) for cross-checking, averaging 96% accuracy; verification results are recorded on-chain, permanently logged on the Base mainnet; in collaboration with Irys, low-cost programmable storage and sub-second retrieval.
Implementation
Learnrite charges $5 per question → minute-level generation, reducing costs by over 90%; Delphi Digital cuts research costs to 1/10 using smart caching and routing; real businesses like Klok have already been successfully implemented.
Creator Incentives
KaitoAI Earn S2 reset, 0.1% $MIRA directed to top researchers; strategy: clear citations, publish rigorous analyses, and climb the rankings with proof.
Don't chase model popularity; first, build a trust layer; turning AI outputs into verifiable data streams is the only way to make money in the long run with $MIRA.
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