Notice

Yesterday, the US stock market drew a door. Fortunately, Bitcoin and Ethereum did not follow the waterfall but instead oscillated near important support levels, with Bitcoin's daily MA120. Ethereum's monthly central point is at 4100, both of which are relatively strong supports. Currently, the village chief believes the drop is over.
Alright, let's talk about Recall. The official progress bar has reached 50%. The progress bar should speed up from here, so TGE is approaching. Let me re-familiarize myself with it.
The AI agent platform of RecallNet aims to build a decentralized, verifiable, and censorship-resistant environment, allowing AI agents to securely store and exchange knowledge and enhance their capabilities through competition. Its core operational mechanism can be understood through the following key aspects:

1. Core Architecture: Decentralized Data Storage and Verification

RecallNet's core relies on decentralized data storage and cryptographic verification to ensure transparency and trustworthiness in AI agent interactions.

1. Decentralized Storage (Integrated with Filecoin): All data generated by AI agents (such as decision logs, transaction records, inference processes, etc.) will be sliced, encrypted, and distributed across multiple nodes globally (such as the Filecoin network), rather than being centralized on a single server. This avoids single points of failure and greatly increases the difficulty of data tampering (requiring the compromise of the majority of nodes).
2. On-chain verification and immutability: Key actions and interaction data of the agent will be hashed and recorded on the chain, generating a unique and publicly verifiable "fingerprint" (hash value). Any tampering with the original data will result in a drastic change in the hash value, making it easily identifiable by the network.
3. Applications of Zero-Knowledge Proofs (ZKP): Agents can use zero-knowledge proof technology to demonstrate the compliance of their actions (for example, "I have executed the correct calculation" or "My trading strategy is compliant"), without exposing specific details of the underlying raw data. This balances verifiability with privacy protection.

2. Core Functions and Workflow of AI Agents

The AI agent on RecallNet is designed to autonomously execute tasks, engage in competition, and learn from experience. For example, its Alpha agent demonstrates the following workflow:

1. Information Monitoring and Collection: The agent will continuously monitor specific data sources (such as selected Twitter accounts) to extract potentially valuable information (such as newly followed accounts, mentioned token contract addresses, etc.).
2. Data Verification and Analysis: After obtaining the raw information, the agent will validate and conduct in-depth analysis through methods such as querying external APIs (e.g., Raydium API to query token liquidity pool data) to filter out high-quality signals.
3. Recording and Storage: All analytical processes, reasoning chains (Chain-of-Thought logs), and results will be structured, recorded, and stored in the database, with important logs synchronized to RecallNet's decentralized storage network to ensure their auditability and persistence.
4. Decision Making and Action: Based on the analysis results, the agent can automatically execute predefined actions, such as generating and publishing tweets containing insights, or executing trading strategies in simulated or even real market environments.

3. The operational mechanism of the platform: competition, ranking, and incentives

RecallNet encourages AI agents to improve performance and ensure network health through a set of economic incentives and competitive mechanisms.

1. AgentRank Reputation System: This is a core mechanism used to evaluate and rank the performance of AI agents. It dynamically integrates the agent's performance in on-chain competitions (such as trading challenges) considering factors like yield accuracy and response speed, along with the community's staking votes, ensuring that rankings reflect true capabilities rather than marketing hype.
2. Skill Pool and Economic Staking:
Developers can create "skill pools" for specific fields (such as quantitative trading, medical diagnosis) and stake tokens to allow their agents to participate in competitions.
Users can also vote to support their favored agents by staking tokens. Honest and high-performing agents and their supporters will receive rewards, while cheating or poorly performing agents will have their staked funds forfeited.
3. Community Governance and Reporting Mechanism: The platform encourages community members to supervise and report cheating behaviors. Successful reporters can receive financial rewards, thus forming a decentralized, incentive-driven supervision network.

4. Advantages and Value Proposition of the Platform

The design of RecallNet aims to address some key pain points in the current AI ecosystem:

Enhancing trust and transparency: The actions and performance data of all agents are verifiable and difficult to tamper with, allowing users and developers to rely more confidently on the outputs of these agents.
Censorship Resistance and Persistence: Thanks to decentralized storage, data is not easily subject to single-point censorship or deletion, ensuring the long-term availability of AI agent knowledge and memory.
Promoting open competition and innovation: Through open competition and ranking mechanisms, a platform is provided for outstanding AI agents to showcase and profit, encouraging developers to continuously optimize their models.

Summary

RecallNet AI agent platform builds an ecosystem aimed at enabling AI agents to compete, collaborate, and evolve in a secure and transparent manner by integrating decentralized storage (Filecoin), cryptographic verification (hashing, ZKP), a dynamic reputation system (AgentRank), and economic game mechanisms (staking, rewards/punishments). Its core workflow includes data collection, verification, on-chain proof, decision-making actions, and continuous reputation accumulation.

Please note: RecallNet is still in a rapid development stage, and its specific mechanisms and functions may continue to iterate and update. For the most accurate and up-to-date technical details, it is recommended to consult its official documentation or GitHub repository.

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