Earlier quoted context omitted.
AI summaries are useful, but I agree: summaries only reduce the “reading/organizing” effort — they don’t solve “what should I do next” or “why does this matter to my current goals”. What I’m exploring is the step after summarization: take the summary and explicitly link it to an active goal/project, then force a small decision: 1. ignore it 2. save as reference for Project X 3. extract 1 concrete next action (with a…
What is it that you read that can yield an "action" and what kind of "action"?
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.