AINNA WebCLI is already built and in use as part of our AI 智能体 infrastructure.
背后 it sits NeuralOps - combining 智能路由, 分离式系统, specialised parsers and multi-model orchestration to decide when AI 是必需的, which model to use, and when a task should bypass the LLM entirely.
For routine 工作流, this architecture can reduce unnecessary token usage by up to 90%.
The moat is the architecture, workflow intelligence and execution efficiency behind the agent.
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先存起来，主要是为了90%。
不太同意90%那里，不过整体还是站得住。
看第二遍才注意到multi-model的细节。
这篇文章适合团队用来开始讨论背后 it sits neuralops。 这点我还要再消化一下。
我喜欢文章对which model to use保持务实的态度。
这篇文章把workflow intelligence and execution efficiency讲得比一般的AI介绍更具体。
我会把the architecture, workflow 90%这一段分享给需要了解技术的同事。