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sReinwald 3 hours ago

Like I said, the first examples are fairly trivial, and you absolutely don't need an LLM for those. A good agent architecture lets the LLM orchestrate but the actual API calls are deterministic (through tool use / MCPs).

My point was specifically about the news filtering part, which was something I had tried in the past but never managed to solve to my satisfaction.

The agent's job in the end for a morning briefing would be:

  - grab weather, calendar, Todoist data using APIs or MCP  
  - grab news from select sources via RSS or similar, then filter relevant news based on my interests and things it has learned about me  
  - synthesize the information above
The steps that explicitly require an LLM are the last two. The value is in the personalization through memory and my feedback but also the ability for the LLM to synthesize the information - not just regurgitate it. Here's what I mean: I have a task to mow the lawn on my Todoist scheduled for today, but the weather forecast says it's going to be a bit windy and rain all day. At the end of the briefing, the assistant can proactively offer to move the Todoist task to tomorrow when it will be nicer outside because it knows the forecast. Or it might offer to move it to the day after tomorrow, because it also knows I have to attend my nephew's birthday party tomorrow.