The flow I’m exploring is: you define a small number of active targets (e.g. “ship feature X”, “prepare for interview Y”). Then when you save/read something, the system searches your existing library (notes/links/email/posts/etc.) against that target and suggests a few candidate next steps or plans that are specifically useful for that target. You pick one (or dismiss them), so it’s more “menu of options” than “AI tells you what to do”.
Example 1 (technical): target = “build a small Kotlin app”. From a Kotlin article + your saved repos, it might suggest: “start with template A”, “try library B for state management”, or “do a 30-min spike to validate architecture C”.
Example 2 (research/learning): target = “write a short brief on topic Z”. From your saved posts, it might propose: “3 key claims + 2 counterpoints”, plus a short outline you can accept/edit.
So “action” = a target-linked next step or plan proposal, chosen by you — not turning every summary into a task.
And you’re right that many people don’t collect enough personally — that’s why I’m also considering a hybrid where your own saves provide personalization, but a shared/managed collection (or public sources) fills the gaps.
In your case, would you find this useful if the output was “one good plan/next step per target” even when your personal saves are sparse, or do you prefer it to be entirely web-driven unless you opt in?
But it could just be that I don't collect that kind of notes or something. Maybe this would work for somebody else.