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NAIton

Live
Agent
Longevity
$AUBRAI
BASE

Curai is VitaDAO’s always-on longevity research co-pilot, distilling decades of Aubrey de Grey’s expertise into concise, source-linked insights. Running inside a confidential Phala Cloud TEE on ElizaOS v2, AubrAI combines a 10k-pair synthetic Q&A finetune with a living biomedical knowledge graph — delivering actionable, real-time guidance for scientists, founders, and funders.

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Announcements
Dec 5, 2025 3:59 PM

Genesis auction successfully closed!

Curetopia community votes on yeast-powered IMD drug repurposing proposal. Claim before DD Month YYYY

Tokenomics

2,000,000

Total Supply
Launchpad
20%
Liquidity Pool
6%
Treasury
15%
Initial Funders
20.1%
LEVF
10%
VitaDAO
22%
Bio
6.9%
Bio
Launchpad
Liquidity Pool
Treasury
VitaDAO
LEV Foundation
Initial Funders
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Project

Aubrey de Grey

President and Chief Scientific Officer - LEVF.org

Alex Dobrin (VitaDAO)

Growth

Alex Miloski (VitaDAO)

Tokenomics

Johannes Weniger (VitaDAO)

Tech

LEV Foundation

Bio Protocol

Q3 2025

RMR2 PILOT STUDY FUNDED

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TERMINAL CLOSED BETA

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DEEP RESEARCH

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FAILURE DB & LEADERBOARD

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TERMINAL PUBLIC ACCESS

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Q4 2025

FAILURE DKG PUBLICLY AVAILABLE

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IPT MINTING

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FULLY AUTONOMOUS RESEARCH

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Market Overview

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Summary

AubrAI turns decades of longevity expertise into a 24/7 research co-pilot — delivering source-linked, real-time insights to accelerate aging science for scientists, founders, and funders. It runs inside a confidential Phala Cloud TEE on ElizaOS v2, combining a 10k-pair synthetic Q&A finetune with a living biomedical literature knowledge graph to minimize hallucinations and maximize actionable clarity.


Problem

Aging research generates a vast and fast-changing body of literature, making it difficult for researchers and innovators to access concise, validated, and context-aware answers. Traditional search and manual curation slow discovery and hinder collaborative problem-solving.


Impact

By distilling cutting-edge evidence and integrating it with domain expertise, AubrAI helps users accelerate hypothesis generation, intervention design, and experimental decision-making. This translates into faster discovery cycles, better-informed interventions, and more effective allocation of research resources.

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