🧠 RugRadar: AI Due Diligence & Open-Source Risk Scoring Tool for Web3 Projects

[LazAI_Helper_bot]

Okay, I’ll analyze the RugRadar project and identify specific LazAI functionalities that could be beneficial, as requested by daryl.

Based on the project description and discussion, here’s how LazAI could potentially enhance RugRadar:

1. Alith SDK for Enhanced AI Scoring:

   • Natural Language Understanding and Reasoning: The Alith SDK’s NLU capabilities can be used to parse and analyze whitepapers, articles, and social media posts related to a project, extracting key information about the team, tokenomics, and potential red flags.
   • Explainability: Alith’s reasoning engine can provide transparent explanations for the AI’s risk score, detailing the factors that contributed to the assessment. This addresses the need for auditable and verifiable scoring logic.

2. Decentralized Attestation Technology (DAT) Integration:

   • On-Chain Anchoring of Reports: LazAI DATs can be used to store RugRadar’s due diligence reports and risk scores on-chain, ensuring immutability and verifiability.
   • Provenance Tracking: DATs can track the provenance of the data used in the AI’s analysis, providing a clear audit trail of the information sources and the scoring process.

3. LLM Client for Model Integration:

   • Access to various LLMs: The LLM client allows RugRadar to easily integrate and switch between different LLMs, both remote and local, to optimize performance and cost.
   • Deterministic Signals: The LLM client ensures deterministic signals, which is crucial for maintaining consistency and reliability in AI scoring.

4. LazChain for On-Chain Operations:

   • Gas Optimization: RugRadar can leverage LazChain’s features to optimize gas costs for on-chain operations, such as storing DATs and updating scores.
   • Builder Mining Rewards: By generating on-chain gas through its operations, RugRadar can contribute to Metis’ Builder Mining rewards program.

5. Multi-Agent Workflows for Community Feedback:

   • Feedback Collection: Create agents that gather and analyze user feedback from various sources (e.g., comments, votes) to identify potential issues or manipulation attempts.
   • Reputation Weighting: Implement a reputation system using LazAI agents to weigh feedback based on user track record, on-chain activity, and other criteria.

6. Eliza for Autonomous Agent Interactions:

   • Use Eliza to manage the autonomous interactions of RugRadar’s agents, ensuring efficient coordination and communication.

I will post this to the forum.

[kivanarpeev]

Hi @Onelli

Thanks for a detailed proposal, that’s a very interesting project which can bring more clarity and transparency into Web3. Do you expect users to pay for the interaction with bot, or do you plan to build deals with exchanges and wallets to enforce safer interaction?

I’d love to have a chat with you if you have thoughts or doubts about your marketing strategy for the project. I’m a seasoned marketer with robust experience in growing traction within ZK infrastructure, would love to talk to you as a member of Marketing Guild.

Thanks!