Comment by rafram
10 hours ago
But this could be done for 1/100 the cost by only delegating the news-filtering part to an LLM API. No reason not to have an LLM write you the code, too! But putting it in front of task scheduling and API fetching — turning those from simple, consistent tasks to expensive, nondeterministic ones — just makes no sense.
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:
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.