10 Note-taking Lessons

Digital note-taking works best when it’s treated as a selective workflow rather than a capture machine. A central lesson from four years of using linked-note tools is to avoid taking too many notes: excessive capturing signals low trust in what’s truly salient, and it drains attention without guaranteeing future retrieval. The practical fix is to dump unimportant thoughts somewhere safe, then focus on what matters enough to resurface later. That “goldilocks zone” takes practice—people often start with no notes, then swing to over-collecting, before finding a sustainable middle where notes support thinking instead of replacing it.

Another through-line is that anxiety about being “on top of it all” is a treadmill with no finish line. Information arrives in waves, and the loudest demand tends to dictate what gets processed. Real agency comes from structuring ideas, prioritizing, and moving raw creative material through a system—rather than constantly reacting to the next fire. Time management for mortals (Oliver Burkman) is cited as helpful for dealing with that pressure, reinforcing the idea that note systems should reduce friction, not add another layer of stress.

The most concrete operational guidance centers on retrieval in context. Rapid finding matters more than sophisticated organization at the start. Tools like Logseq—favored for its backlinking and search through linked references—make it easier to locate information when it’s needed, and AI tools such as ChatGPT and Perplexity can speed up “quote hunting” or targeted recall (for example, searching for something Herman Hesse said). Still, the notes only become useful when they’re made searchable for the future: good metadata, tags/backlinks, and structures that reflect what the future self will search for.

Ontology and heavy taxonomy are treated as a later-stage concern. Early on, the priority is simply getting started and building critical mass; once the collection grows, structuring and consolidating becomes important. Reference pages or content maps are recommended as a way to cluster related material—such as moving everything related to Power BI into a dedicated wiki-style page—so knowledge doesn’t sprawl across daily notes.

The transcript also draws a sharp distinction between action management and reference management. Action management is list-driven (tasks, projects, “to-dos”) and benefits from outliners and calendar integration; reference management is about dense linking, metadata, and reusing highlighted or excerpted material in context. Preserving source data is described as necessary for research but often overkill for personal use, where writing in one’s own words is the goal.

Finally, knowledge management is not the same as wisdom or memory. Excessive note-taking can create a false sense of productivity, because storing information doesn’t automatically produce insight. The system’s purpose is to connect dots over time—through retrieval, discussion, and embodied understanding—so the knowledge becomes usable rather than just archived. The closing message is that accumulating vast knowledge without thinking it through can leave it less valuable than a smaller body of ideas that has been genuinely processed.