Recently, after Zama launched its mouth-to-mouth activity, the hype has been quite high, and I am optimistic about this project.
Zama is one of the few high-funding projects currently available. High funding means high market value, and high market value means a higher probability of significant returns.
Zama is an open-source company focused on Fully Homomorphic Encryption (FHE) technology. The project aims to provide end-to-end encryption solutions for blockchain and AI applications, helping developers build privacy-preserving decentralized applications.
The main target audience is Web3 enterprises, as it is a B2B project.
In terms of financing:
Financing and valuation milestones: On June 25, 2025, Series B financing: Zama completed a $57 million Series B round led by Blockchange Ventures and Pantera Capital, with total funding exceeding $150 million. The company's valuation surpassed $1 billion, making it the first FHE unicorn.
Use of funds: Accelerate the launch of the mainnet, ecosystem adoption, and expand into cloud computing and enterprise-level applications.
@zama_fhe @KaitoAI
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
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