In the past few days, I chatted with friends working in AI at major Web2 companies about the Sentient project, and they were all quite surprised because Sentient is doing something that previous AI projects have never done before.
Sentient represents a significant step forward in the AI sector, with AGI leading the AI wave.
After discussing the core functionality of Sentient, GRID, let's talk about one of GRID's core open-source large language models (LLM): Dobby.
Dobby's positioning and functionality align with open-source and community goals. Dobby is an open-source large language model designed to democratize artificial general intelligence (AGI) through community collaboration.
Unlike closed-source models (like the GPT series), Dobby's code and training data are public, allowing developers to verify, modify, and customize.
Community alignment means that the model's parameters and behaviors prioritize user needs and ethical standards, avoiding the "black box" problem controlled by large companies.
Dobby's advantages and innovations compared to other closed-source models include:
Open and transparent: Dobby's open-source nature distinguishes it from closed-source models, allowing the community to review and optimize the code, reducing bias and error risks.
Decentralized operation: Through blockchain and distributed computing power, Dobby avoids reliance on centralized cloud services, enhancing privacy and resistance to censorship.
Modular design: Dobby is not a single "omni-model" but part of the GRID ecosystem, focusing on language reasoning and collaborating with other specialized agents to achieve AGI-level capabilities.
However, there are certain drawbacks: compared to closed-source models, Dobby's reasoning capabilities may still lag in certain tasks (such as complex reasoning in ultra-large-scale models).
Sentient is still in the early stages of the project, and there are indeed some flaws in certain areas. As Sentient progresses further in development, these existing flaws will gradually be improved.
@SentientAGI #SentientAGI @KaitoAI
Today, I went out to play for a day during the National Day holiday and also went to our local square to watch the flag-raising ceremony, which was full of ritual.
Limitless @trylimitless has already raised 100 million in pre-sales, which is a bit beyond expectations, and it has also become the first project on Kaito @KaitoAI to exceed 100 million.
Returning to the core project of Sentient @SentientAGI, GRID.
GRID (Global Research and Intelligence Directory) is an open-source, decentralized network that connects specialized AI agents, models, data sources, tools, and computing resources to collaboratively build AGI-level intelligence.
GRID has three main features:
1: Interoperability and Composability: Multi-agent reasoning is achieved through protocols like ROMA (Recursive Open Meta-Agent), enhancing search capabilities with Open Deep Search (ODS), allowing agents to collaborate in real-time to create "workflows," where the output of one agent can serve as input for another.
2: Open-source and Verifiable: All components use model fingerprinting technology to ensure ownership and security, being transparent and preventing unauthorized use.
3: Usability: Users interact through Sentient Chat, a unified interface for discovering, invoking, and combining GRID elements.
Current scale and ecosystem as of October 2025, GRID is developing rapidly:
110+ partners: including 50+ AI agents (e.g., Napkin for generating graphics, Exa for searching, Caldo for finance), 50+ data providers (e.g., The Graph, Kaito), 6+ models (e.g., Dobby, a community-aligned open-source model), and 10+ computing/infrastructure providers (e.g., EigenLayer).
User scale: Over 2 million people on the waiting list, actively contributing to Web2/Web3 use cases.
The reason GRID is the core product: GRID disrupts the "closed laboratory" model of AGI development. Through modular and community-driven intelligence, GRID ensures that AGI is not monopolized by large companies—developers are rewarded, users receive transparent outputs, and the network expands through staking and contributions.
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