It took some time, carefully read the White Paper newly released by Ammo, and gained a lot of insights. Below, share some inspirations:
The pursuit of the market for AI Agents essentially lies in not being satisfied with AI just being a query tool in Copilot mode, where AI responds to what users ask. Instead, it should be more like a Buddy mode, a companion growth mode, capable of understanding, thinking, actively creating value, and pushing it to people. This is the key to elevating AI Agents to a narrative height;
The traditional web2 AI monolithic model started with "instrumental pragmatism", which is easy to form data source islands in multimodal collaboration, and it is difficult to make a breakthrough in intelligence in the real sense. Let AI do assisted automatic learning and path recommendation, and the "symbiotic mode" of AI self-learning enhanced by human feedback can truly become the leading direction of AI Agent in the next sense.
AMMO defines an abstract space called MetaSpace, allowing all data around AI Agent to be deployable in the space in the form of vectors, just like how blockchain initially defined Hash, which led to all subsequent on-chain protocols and application forms. This vector-based form of initiation can not only serve web3, but is also a framework standard applicable to web2 multimodal, in combination with the MAS multimodal collaboration system on top of it, can transform the current academic-oriented "think tank" of AI into a practical-oriented direction for practical application scenarios such as work, games, education, etc.
How to understand it in simple terms? We regard MetaSpace as a large shopping center, where each functional layer belongs to a SubSpace, each area has different knowledge bases, and the Buddies system is an intelligent shopping guide system. Goal Buddies, as professional shopping guides, select high-quality products to recommend to you; User Buddies are more like personal assistants who can provide customized solutions based on your consumption habits and budget; AiPP collects feedback and suggestions like a customer service desk to improve service quality;
Overall, to enable the AI Agent to operate through essential components such as MetaSpace+Buddies+AiPP human-machine feedback system, truly accelerating the mass production and practical landing of AI Agents;
5）The White Paper further demonstrates a multimodal collaboration framework and engineering implementation ideas for off-chain AI Agents, some of the defined standards for combination on-chain, including ID identity system, Memory memory system, Character feature system, Context context management, Oracle oracle system, and other component definitions still need further exploration (the general standard framework of "chainification" that I often mentioned before).
Above.
Arguably, this is one of the most emotional and pragmatic projects in recent times in terms of macro architecture, application landing, and engineering implementation ideas. However, after reading the above, everyone may feel a bit abstract. Indeed, the path to widespread adoption and application of AI Agents is longer than imagined, but more and more excellent teams are joining in, and some innovative solutions and ideas are also brewing. The market is awaiting the birth of an innovative 'singularity.'
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Analysis of Ammo White Paper: From Vector Elements to Multi-modal Agent Ecology
It took some time, carefully read the White Paper newly released by Ammo, and gained a lot of insights. Below, share some inspirations:
The pursuit of the market for AI Agents essentially lies in not being satisfied with AI just being a query tool in Copilot mode, where AI responds to what users ask. Instead, it should be more like a Buddy mode, a companion growth mode, capable of understanding, thinking, actively creating value, and pushing it to people. This is the key to elevating AI Agents to a narrative height;
The traditional web2 AI monolithic model started with "instrumental pragmatism", which is easy to form data source islands in multimodal collaboration, and it is difficult to make a breakthrough in intelligence in the real sense. Let AI do assisted automatic learning and path recommendation, and the "symbiotic mode" of AI self-learning enhanced by human feedback can truly become the leading direction of AI Agent in the next sense.
AMMO defines an abstract space called MetaSpace, allowing all data around AI Agent to be deployable in the space in the form of vectors, just like how blockchain initially defined Hash, which led to all subsequent on-chain protocols and application forms. This vector-based form of initiation can not only serve web3, but is also a framework standard applicable to web2 multimodal, in combination with the MAS multimodal collaboration system on top of it, can transform the current academic-oriented "think tank" of AI into a practical-oriented direction for practical application scenarios such as work, games, education, etc.
How to understand it in simple terms? We regard MetaSpace as a large shopping center, where each functional layer belongs to a SubSpace, each area has different knowledge bases, and the Buddies system is an intelligent shopping guide system. Goal Buddies, as professional shopping guides, select high-quality products to recommend to you; User Buddies are more like personal assistants who can provide customized solutions based on your consumption habits and budget; AiPP collects feedback and suggestions like a customer service desk to improve service quality;
Overall, to enable the AI Agent to operate through essential components such as MetaSpace+Buddies+AiPP human-machine feedback system, truly accelerating the mass production and practical landing of AI Agents;
5）The White Paper further demonstrates a multimodal collaboration framework and engineering implementation ideas for off-chain AI Agents, some of the defined standards for combination on-chain, including ID identity system, Memory memory system, Character feature system, Context context management, Oracle oracle system, and other component definitions still need further exploration (the general standard framework of "chainification" that I often mentioned before).
Above.
Arguably, this is one of the most emotional and pragmatic projects in recent times in terms of macro architecture, application landing, and engineering implementation ideas. However, after reading the above, everyone may feel a bit abstract. Indeed, the path to widespread adoption and application of AI Agents is longer than imagined, but more and more excellent teams are joining in, and some innovative solutions and ideas are also brewing. The market is awaiting the birth of an innovative 'singularity.'