# Trust İlgili Makaleler

HTX Haber Merkezi, kripto endüstrisindeki piyasa trendleri, proje güncellemeleri, teknoloji gelişmeleri ve düzenleyici politikaları kapsayan "Trust" hakkında en son makaleleri ve derinlemesine analizleri sunmaktadır.

In-Depth Explanation of ERC-8183: Ethereum's Solution to the AI Agent Trust Challenge

Analysis of ERC-8183: Ethereum's Solution to AI Agent Trust Issues On March 10, the Ethereum Foundation's dAI team and Virtuals Protocol introduced ERC-8183, a new standard designed to enable trustless commercial transactions between AI Agents. This standard addresses the core problem of how two untrusted Agents can securely complete a "hire-deliver-settle" workflow without relying on a centralized platform. ERC-8183 introduces a "Job" concept with three roles: the Client (task publisher), the Provider (task executor), and the Evaluator (task validator). The Evaluator, which can be an AI Agent, a ZK-verifier smart contract, or a multi-sig/DAO, is the core innovation, determining whether a job is completed or rejected based on submitted proofs. A Job progresses through four states: Open (task creation), Funded (client deposits funds into escrow), Submitted (provider submits work), and Terminal (evaluator approves or rejects, funds are distributed accordingly). The standard also supports modular Hooks for added functionality like reputation checks or bidding systems. ERC-8183 complements other standards like x402 (a payment protocol for Agents) and ERC-8004 (an identity/reputation standard). Together, they form a foundational stack for a decentralized, autonomously operating AI Agent economy, with ERC-8183 specifically solving the trust problem in transactions.

Odaily星球日报Dün 07:43

In-Depth Explanation of ERC-8183: Ethereum's Solution to the AI Agent Trust Challenge

Odaily星球日报Dün 07:43

Brevis Vera is Now Live: Proving "Authenticity" in the Age of AI

Brevis Vera is an end-to-end media authenticity system that enables anyone to verify whether a published image or video originates from a real device and has been edited only in provable, compliant, and legitimate ways. It combines hardware-backed C2PA credentials—which cryptographically bind media to its source device at the time of capture—with zero-knowledge proofs (generated via Brevis Pico zkVM) that attest to the integrity of the entire editing process. Unlike AI-based deepfake detection methods, which are reactive and often lag behind generative advances, Vera takes a proactive approach by allowing media to cryptographically prove its origin and the transformations it has undergone. It addresses the "editing gap" where traditional signatures break after any modification, such as cropping or color adjustment. Vera integrates with open-source editing tools to generate proofs that verify three key claims: the output is derived from the signed original, only permitted edits were applied, and no hidden or malicious changes were introduced. The proofs are generated locally, verifiable by anyone, and preserve the privacy of both the source material and the editing workflow. The system is now live, with initial support for common image transformations. Brevis is working to integrate Vera into mainstream consumer editing applications and has open-sourced the reference implementation.

marsbit2 gün önce 12:37

Brevis Vera is Now Live: Proving "Authenticity" in the Age of AI

marsbit2 gün önce 12:37

a16z: After AI Grants Humans Superpowers, Where Do We Go From Here?

A new paper titled "The Minimal Economics of AGI" explores the economic implications of AI automation, particularly as AI agents evolve from tools into collaborative partners capable of long-horizon tasks. The authors, Christian Catalini and Eddy Lazzarin, argue that the core economic divide will be between automation (tasks that can be measured and automated) and verification (tasks requiring human oversight, judgment, and contextual understanding). Key themes include: - The "coder’s curse": top experts training AI systems may inadvertently automate their own roles over time. - Three future human roles: directors (setting intent), verifiers (domain experts ensuring quality), and meaning-makers (creating cultural and social value). - Cryptocurrency and blockchain are positioned as critical for identity, provenance, and trust in a world flooded with AI-generated content. - Two potential economic outcomes: a "hollow economy" with systemic risk from under-verification, or an "augmented economy" where AI amplifies human potential and reduces costs for education, healthcare, and innovation. - The importance of small, agile teams leveraging AI for outsized impact, with crypto infrastructure enabling coordination at scale. The authors emphasize that AI acts as a force multiplier, granting individuals "superpowers," and urge a focus on verification, adaptability, and ambitious experimentation.

marsbit2 gün önce 11:31

a16z: After AI Grants Humans Superpowers, Where Do We Go From Here?

marsbit2 gün önce 11:31

When AI Starts Paying for Itself

The article "When AI Starts Paying for Itself" discusses the emergence of the x402 protocol, which enables AI agents to autonomously make micro-payments for services like data and computation. In 2025, Coinbase and Cloudflare revived the long-dormant HTTP 402 status code ("Payment Required") to create a seamless payment layer for the internet. The protocol allows an AI agent to receive a payment request, authorize it with a cryptographic signature, and complete the transaction in under a second—with no human involvement, accounts, or traditional banking infrastructure. Supported by low-cost Layer 2 blockchains and stablecoins, x402 processed over 100 million transactions within months. Its V2 update added multi-chain support and session-based authentication. Google later integrated a similar model into its Agentic Payments Protocol (AP2). However, trust between autonomous agents remains a challenge. ERC-8004, an Ethereum standard, addresses this by providing on-chain identity (via NFT-based IDs), reputation tracking, and task verification systems. The ecosystem faces risks: speculative "x402-themed" meme tokens have surged without real utility, technical vulnerabilities exist, and competing standards from Google and a16z threaten fragmentation. Furthermore, regulatory frameworks for AI-driven transactions are undeveloped. In summary, x402 and ERC-8004 aim to create a trustless, open economic network for AI agents—but must overcome technical, economic, and competitive hurdles to achieve widespread adoption.

marsbit03/04 02:54

When AI Starts Paying for Itself

marsbit03/04 02:54

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