Get started with Logseq, my Daily Workflow

Logseq’s daily workflow centers on capturing small, time-stamped notes in a “today” journal, then turning those fragments into searchable context through tags, links, and automatic graph connections. The core insight is that the system doesn’t try to force rigid structure up front. Instead, it starts with whatever comes to mind during the day—morning routine entries, quick highlights, questions from colleagues—and gradually builds a web of relationships that becomes easier to navigate over time.

The workflow begins with the journal’s “today” page: users type whatever arises, then later reuse it. A morning routine becomes a simple on-ramp to the habit: entries like “woke up,” “getting coffee,” and optional notes on how the day feels. From there, “highlights” act as daily success criteria—focus points that define what matters most. Day-to-day journaling is lightweight: small notes are written as they happen, and tags/hashtags are added to mark relevance. Inline tags and block-level hashtags both work; the choice is largely aesthetic, with the bracket method used for inline tagging and hashtags used to label blocks.

The real payoff comes when questions and projects start accumulating. When someone asks about something—“Bob” asking about “project x,” for example—the notes include those identifiers. Later, searching and shift-clicking opens the relevant page in a sidebar, showing the history of everything marked with that person or project. Shift-clicking works not only from search results but also directly from within text, letting users jump instantly to related material without manually hunting through folders. Over time, pages become living summaries: as notes accumulate, the top of a page can be used to maintain a quick recap of what’s been learned.

Meetings follow the same principle but with extra attention to signal. Meetings are added to today’s journal as a session block (e.g., “session about project y”) and grouped with a “meeting” hashtag. Rather than tagging every attendee—which creates noisy, low-value links—the workflow tags only people who contribute something meaningful. For longer discussions, the interface can zoom into a specific block to reduce distraction, and once the meeting ends, the entire meeting block can be folded to keep the journal readable.

The graph feature ties everything together. Early on, the graph’s dots and lines may look pointless, but it becomes a “linking radar” for disconnected topics—areas that exist but aren’t connected to anything else. By inspecting clusters and adding missing links (e.g., connecting “project x” to a “manager” node), users turn scattered notes into structured knowledge. That structure then supports fast research: opening a project reveals linked references and the accumulated context from prior days and meetings. The workflow also helps in business conversations where people know their own priorities but not everyone else’s; typing a person’s name quickly surfaces what was discussed last time, enabling faster, more accurate follow-ups.

In short, Logseq is used as a daily capture system that grows into an interconnected knowledge base—one that makes past conversations, project context, and research threads instantly retrievable without heavy upfront planning or complex tooling.