Today, a project called Principals Network, with its token $PNET (note: not PNUT the squirrel), has been rising continuously within 24 hours of token issuance, with its market value increasing from around 200k initially to 15M currently. There are already scattered discussions about it, but it remains outside mainstream narrative.

The project's goal is very clear: to create a decentralized education network using AI agents.

From a narrative perspective, this is groundbreaking. Creating a personalized AI agent education assistant and private tutor seems to be a first-of-its-kind approach. Coupled with AI hackathon support for discovering practical projects, PNET's expectations have been further elevated.

PNET's core design revolves around three key elements:

1. AI Teaching Agents These are not simple Q&A machines, but specially trained domain expert AIs. Each agent focuses on specific subject areas like blockchain, artificial intelligence, or personal development. Moreover, they can understand the learner's cognitive level and develop personalized learning paths.

Through continuous interaction with learners, AI continuously optimizes teaching strategies, making teaching itself a process of training and tuning. This functionality is integrated into the AI Engine layer, unified and dispatched by the Headmaster module.

1. Decentralized Knowledge Graph This layer is built within Academies, connecting knowledge points across various disciplines. Continuously expanded and optimized through community contributions, it helps AI agents understand the relationships between different knowledge areas.

This design can provide learners with multi-dimensional knowledge exploration paths, supporting cross-disciplinary learning and knowledge integration.

1. On-chain Proof of Learning This feature is built on the EDU Chain layer, recording every step of the learner's progress and generating verifiable skill proofs. Most critically, after completing learning, it combines with the $PNET token incentive mechanism, providing learners with additional external motivation.

Theoretically, these three points can form a self-sustaining cycle: AI teaching agents obtain teaching content and associated information through the knowledge graph, the learning process is recorded through on-chain proof, and feedback is used to optimize AI agents; the knowledge graph is continuously enriched and validated during the learning process.

Diving deeper into the project architecture reveals that PNET's core is a decentralized education network built on the Solana ecosystem, employing a three-layer design: AI Principals, Academy System, and EDU Chain.

The AI Principals layer is the system's core, with specially trained AI Agents serving as personalized tutors. These AI Agents can not only customize learning paths based on learners' characteristics but also provide real-time coaching and Q&A. The Academy System layer is responsible for managing educational content and resources, building a dynamically evolving curriculum system, and coordinating interactions between learners and AI Principals. The bottom EDU Chain layer, built on Solana, primarily handles core functions such as educational certificate certification, credit management, and token incentives.

Today, a project called Principals Network, with its token $PNET (note: not PNUT the squirrel), has been rising continuously within 24 hours of token issuance, with its market value increasing from around 200k initially to 15M currently. There are already scattered discussions about it, but it remains outside mainstream narrative.

The project's goal is very clear: to create a decentralized education network using AI agents.

From a narrative perspective, this is groundbreaking. Creating a personalized AI agent education assistant and private tutor seems to be a first-of-its-kind approach. Coupled with AI hackathon support for discovering practical projects, PNET's expectations have been further elevated.

PNET's core design revolves around three key elements:

1. AI Teaching Agents These are not simple Q&A machines, but specially trained domain expert AIs. Each agent focuses on specific subject areas like blockchain, artificial intelligence, or personal development. Moreover, they can understand the learner's cognitive level and develop personalized learning paths.

Through continuous interaction with learners, AI continuously optimizes teaching strategies, making teaching itself a process of training and tuning. This functionality is integrated into the AI Engine layer, unified and dispatched by the Headmaster module.

1. Decentralized Knowledge Graph This layer is built within Academies, connecting knowledge points across various disciplines. Continuously expanded and optimized through community contributions, it helps AI agents understand the relationships between different knowledge areas.

This design can provide learners with multi-dimensional knowledge exploration paths, supporting cross-disciplinary learning and knowledge integration.

1. On-chain Proof of Learning This feature is built on the EDU Chain layer, recording every step of the learner's progress and generating verifiable skill proofs. Most critically, after completing learning, it combines with the $PNET token incentive mechanism, providing learners with additional external motivation.

Theoretically, these three points can form a self-sustaining cycle: AI teaching agents obtain teaching content and associated information through the knowledge graph, the learning process is recorded through on-chain proof, and feedback is used to optimize AI agents; the knowledge graph is continuously enriched and validated during the learning process.

Diving deeper into the project architecture reveals that PNET's core is a decentralized education network built on the Solana ecosystem, employing a three-layer design: AI Principals, Academy System, and EDU Chain.

The AI Principals layer is the system's core, with specially trained AI Agents serving as personalized tutors. These AI Agents can not only customize learning paths based on learners' characteristics but also provide real-time coaching and Q&A. The Academy System layer is responsible for managing educational content and resources, building a dynamically evolving curriculum system, and coordinating interactions between learners and AI Principals. The bottom EDU Chain layer, built on Solana, primarily handles core functions such as educational certificate certification, credit management, and token incentives.