When a certain content or service becomes ubiquitous, the curation layer becomes crucial. Ubiquity always precedes order.

For example:

Google Search is the curation layer for internet websites (ranking websites based on indexing and reputation relevance).

TikTok is the curation layer for content (algorithm-driven recommendation engine).

Kaito is the curation layer for Web3 accounts/KOLs.

The curation layer involves two aspects:

Evaluation: Deciding which content and services are good.

Distribution: Delivering which content to which users.

Have you ever thought about what the curation layer will look like in the future when AI agents become ubiquitous (which is highly probable)? When billions of AI agents emerge, autonomously executing financial transactions, medical research, business decisions, or daily tasks, how do we discover the trustworthy and high-quality ones in this vast ocean of agents?

@recallnet is built on this vision, and from a technical framework, it is divided into three levels:

AgentRank: A dynamic reputation system that ranks the true capabilities of AI agents.

On-chain AI competitions: Generating verifiable performance data through transparent, real-time performance evaluations, keeping reputation dynamically updated.

Agent curation market: Incentivizing community members to support high-quality agents.

From a purely narrative perspective, Recall is currently one of the few AI projects that stands firm and has a high ceiling. However, theoretical elegance does not equate to practical success; whether this AI agent evaluation system can be effective will have to wait until the platform is implemented.

The background of the investors is also solid: Multicoin + Coinbase + USV + CoinFund.

I just regret that I didn't speak up sooner; everyone should hurry up too.

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