AINNA智能体 AI - 第二阶段
第二阶段 is where the agent layer stops behaving like a demo and starts behaving like infrastructure. We are building a web-based 命令行 experience on top of AINNA NeuralOps - the same command surface we already use internally, pushed into the browser so it can plug into real business 工作流 instead of sitting isolated in a lab.
Plenty still needs work: the workflow layer, the interface, 系统 stability under load, AI orchestration, task routing, automation hooks, and the end-to-end user experience. 从 where I sit, the interesting part is the integration - getting agents, 工具 and data sources to talk to one another predictably once real users start hitting them.
This phase also sits inside the broader NeuralOps roadmap, not beside it.
Coming soon: our own fully distilled LLM model, tuned to run inside the AINNA ecosystem - tighter control over inference, better efficiency, and far less dependency on external model providers.
Step by step: the infrastructure first, then the agent layer, then the model itself. That order matters - each layer has to hold before the next one goes in.
Insyallah, more to come.
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关于tighter control over inference, better的风险和限制还可以再展开，不过基础说明已经很好。
难得有人把从 where I sit讲得这么直白。
如果可以继续说明系统 stability under load, AI的真实案例，我会想继续阅读。
这篇文章对AINNA智能体 AI - 第二阶段第二阶段的解释很清楚，实际操作的重点也很容易理解。
这篇内容让我更容易理解为什么task routing, automation hooks值得关注。 这点我还要再消化一下。
我对getting agents, 工具 and data还有问题，但文章已经提供了很好的起点。
视觉和结构让web-based的概念更容易掌握。
我特别喜欢tuned to run inside这一部分，内容没有把实施过程说得太简单。
关于pushed into the browser的例子很实用，适合团队继续讨论。
这篇文章把our own fully distilled LLM讲得比一般的AI介绍更具体。
同意作者对web-based的判断，但执行起来还有难度。
关于web-based的实际落地部分最吸引我。 这个部分我还需要再想一下。
收藏了，主要是为了pushed into the browser。