🤖 The LLM Shift That Changed AI Agents
Nine months ago, when we did the TGE for $CAP on Virtuals Genesis, AI and LLM tech was still very basic. The popular models back then were 4o and 4.1, and there was no real reasoning.
At that time, if a project launched a clean AI terminal with a nice UI and a custom character config, it was already enough to launch a token and sell.
If you wanted to parse human language with an LLM and get the correct request body, you had to write very long prompts and provide many examples. It was not simple.
Today, AI models are evolving at crazy speed. Reasoning models like opus 4.6 and codex 5.3 can understand human chat messages naturally, without too much instruction. Building AI Agents is now much easier. LLM performance is more accurate, more stable, and far more efficient than before.
The architecture of AI Agent systems has also changed. In the past, the Core LLM was hosted on the server, while the client only acted as an AI Agent or AI Terminal. Now, with the launch of OpenClaw, the LLM layer has moved to the client side.
> Bankrbot, Butler_Agent, Captain Dackie are agents that has core LLM at server side.
Agent backends are mainly API systems communicating through API keys. Most major agents today support LLM on both server and client side.
Soon,
@capminal.eth will launch our own new AI Agent framework. You can already experience the intelligence of our new framework at
capminal.ai or tag Captain Dackie on X to try it out.
Stay tuned for our next updates.
