[varispeed]

Wouldn't trust AI to run TODO, especially weak models. They can hallucinate tasks, forget to remind etc.

[tapvt]

LLMs are stateless. But given an actual database of task-shaped items and some work, I could see the potential.

With a canonical source of truth, and set input/output expectations, the potential blast radius is quite small.

[wpm]

And the end results is.....? What? A todo app that takes 16GB of RAM?

[bigyabai]

Nothing that Mac and Windows users aren't already used to.

[Forgeties79]

It’s tempting to be flippant about MacOS/windows but in all seriousness, the resources required for an LLM to do the job of a typical lighter weight app/software is a serious consideration. No amount of bloat matches what an LLM needs.

[bigyabai]

> No amount of bloat matches what an LLM needs.

I don't think that's necessarily true. For instance, LinkedIn uses more memory than Gemma E2B inference does.

[Forgeties79]

LinkedIn is an entirely different category and an extreme case at that. We’re not talking about LLM’s replacing LinkedIn either. It’s an entirely different comparison/discussion.