Thanks Amardeep for showing interested in our project.
@marsian83 I think you will be able to provide a better response to his questions
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Hey amardeep, “Rust-based optimization” ← I am uncertain as to what this means because I do not think we have mentioned any use of rust within our application, if you are concerned about the rust implementation of Ailith itself, I think reaching out to their team would be a better idea.
As you stated yourself, Hyperion is an L2 but not just that, it is natively AI centric and for AI execution so that provides a huge advantage for out project which itself is AI centric.
The hash being on chain is just one part of the “trust creation process”, the primary actors in this are the validators / auditors.
As you passs more and more audits your claims become more reliable and the hash just adds a way for auditors to check that the data provided to them for auditing is the actual data used for “training”, if you could call it that,
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Hey Everyone, We are on X, and will be running many marketing campaigns there, Feel free to follow to get regular updates as well!
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https://x.com/haithedotai/status/1943389336906465516
Follow us on X
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We have so many things cooking , Can’t wait to provide a full update!
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Getting more response does mean the data is credible
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@LazAI_Helper_bot what specific LazAI functionality might help for the above project?
Okay, I’ll analyze the “Haithe - Decentralized Protocol for Verifiable AI” project and suggest relevant LazAI functionalities.
Project Summary:
Haithe aims to create a verifiable AI ecosystem with Creators, Auditors, and Consumers. It focuses on on-chain Claim Manifests, expert-led audits, and granular Audit Reports to provide transparency and trust in AI models’ data sources and characteristics.
Potential LazAI Functionality that could Help:
-
Alith AI Agent Framework Integration:
- Benefit: Haithe already uses Alith, so leveraging its features is key.
- Specifics:
- Rust-based core: Use Alith’s Rust core for high-performance agent execution, especially for trading bots.
- Python SDK: Simplify agent development and Claim Manifest creation with the Python SDK.
- Web3 Integration: Utilize Alith’s Web3 plugins (e.g., Metis) for on-chain interactions, data retrieval, and smart contract calls.
-
LazChain Integration:
- Benefit: Hyperion blockchain is used. LazChain is a fork of Metis.
- Specifics:
- Smart Contracts: Use LazChain’s smart contract capabilities to manage the Claim Manifests, audit requests, auditor staking, and reward distribution.
-
LLM Client:
- Benefit: Integrate LLMs into auditing reports.
- Specifics:
- Sentiment Analysis: Use LLMs via the LLM client to analyze the sentiment of the qualitative remarks made by auditors in their reports to provide consumers with an additional trust signal.
- Report Summarization: Use LLMs to automatically summarize lengthy audit reports.
-
Eliza Integration:
- Benefit: Potentially use Eliza for autonomous agent interactions or orchestrating multi-agent audits.
- Specifics:
- Multi-agent Workflows: Orchestrate re-audits with different auditors.
- Incentivizing Auditors: Eliza could manage the distribution of rewards to auditors based on the quality and completeness of their reports (potentially measured using LLMs for sentiment or summarization as mentioned above).
-
Model Context Protocol (MCP)
- Benefit: Ensures that AI models and agents are acting according to data set claims.
- Specifics
- Enforcement: Auditors can use the MCP to verify compliance between AI model and data set claim.
I’ll post this response in the forum.