STORAGENT

STORAGENT price
STORAGENT
#3924

$0.00002593  

5.76% (1d)

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STORAGENT statistics

Market cap
$25.93K

0.00%

Volume (24h)
$125.55K

3.08%

FDV
$25.93K
Vol/Mkt Cap (24h)
484.11%
Total supply
999.98M STORAGENT
Max. supply
--
Self-reported circulating supply
999.98M STORAGENT
STORAGENT to USD converter
STORAGENT
USD
Price performance
24h 
Low
$0.00002451
High
$0.00002743
All-time high
Jan 11, 2025 (3 months ago)
$0.003001
-99.14%
All-time low
Apr 08, 2025 (18 days ago)
$0.00002447
+5.97%
See historical data


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About STORAGENT

STORAGENT - A new way to Secure Storage with AI Agent.

Decentralized storage solutions have become a critical component of the web3 ecosystem, offering robust, censorship-resistant alternatives to centralized cloud storage providers.

However, existing solutions face challenges in scalability, cost eficiency, and usability, limiting widespread adoption.

This document introduces a decentralized storage architecture guided by an AI Agent, addressing these challenges with a novel approach that is being tested for the first time.

This pioneering implementation seeks to open new horizons in decentralized systems by integrating advanced AI capabilities into storage processes. Ipfs Integratıon

Web3 Storage Archıtecture The foundation of this system is a decentralized storage protocol guided by an AI Agent, which ensures high performance, security, and fault tolerance by leveraging web3 technologies. The use of an AI-supported approach, implemented for the first time, constitutes a groundbreaking innovation in decentralized storage. Data is stored on the InterPlanetary File System (IPFS), a peer-to-peer hypermedia protocol that enables decentralized and distributed file storage. An AI Agent assigns a unique content identifier (CID) to each file based on its content, ensuring data integrity and efficient de duplication.

The integration of AI into this process is a first in the industry. To optimize storage capacity and ensure data availability, files are fragmented and encoded using erasure coding techniques such as Reed-Solomon codes. This allows the original data to be reconstructed even if some fragments are lost or corrupted.

The integration of AI into the erasure coding process marks a transformative approach, being implemented for the first time in this architecture.

 
 
 
 
 
 

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