After the tour at Fudan University, @SentientAGI is sharing again at Shenzhen University. It's interesting that Sentient chose universities as the starting point for promotion.

Universities are a hub for AI talent and innovative thinking, and they are also the group most open to embracing open concepts. By establishing developer communities through campus roadshows, a talent foundation is laid for subsequent ecological development.

⚡️Core Highlights
Sentient positions itself as an operating system for AI. The core is the GRID network: integrating over 40 AI agents and more than 50 data and tool interfaces, enabling real-time collaboration between different models through standardized messaging and task routing. Unlike the approach of a large model package dominating the scene, Sentient breaks down the main tasks into distributable subtasks, assigning them to the most suitable agents for completion, and then performs unification and verification at the end of the chain.

Solved two core problems under traditional AI models:
1️⃣Model Island: Different models operate independently, with high migration costs and low reuse rates. GRID uses a unified orchestration layer to facilitate calling and feedback.

2️⃣Data Quality: Inconsistent context and unreliable sources. Through multi-source cross-validation, trusted data pathways, and backtracking. Reduce AI hallucinations and biases.

Sentient is more like an open protocol: it provides open access and revenue settlement for models, agents, data providers, and application developers. OML emphasizes traceability, composability, and billability.

Currently serving about 2 million users, the launched products include financial report generation, research assistant, personalized news briefings, and more. Unlike standalone applications, the value of network products lies in compounding improvements: with each new agent or data source added, the overall understanding of the network is enhanced.

Closed-source models have advantages in single-point capabilities and ecological binding, but lack composability and verifiability. Open-source models are fragmented in the ecosystem but have a fast pace of innovation.

My understanding of @SentientAGI is that they are trying to establish an "orchestrable - billable - verifiable" middle layer between "open-source models" and "closed-source models."