How to Get Started with Logseq | Videos I wish I had (Part 2)

Logseq’s real value comes from how people structure relationships between notes—not from linking everything blindly. Using a Wikipedia-style “wiki” approach (hyperlinks that jump between pages) can quickly produce a chaotic knowledge graph full of empty or low-context nodes, while adding lightweight structure—especially tags and contextual links—turns scattered text into something that’s easier to navigate and reuse.

A practical comparison makes the trade-offs clear. In a Wikipedia-like setup, clicking through concepts such as “capitalism” to “economic system” creates lots of connected pages, but the resulting graph can become a “rabbit hole” of linked words with little usable content—especially when many pages are effectively placeholders. A tags-based approach is different: entering “capitalism” with tags like “social systems” and “market theory” creates context even when the tagged pages themselves contain no definitions. The graph becomes more legible because concepts cluster around shared meaning rather than just hyperlinking to other pages. A journal-only approach goes even further toward cleanliness: daily entries can produce a super-clean graph with minimal connections, but it also removes the explicit links that help surface relationships later. The same information can therefore feel either navigable or opaque depending on whether context lives in pages, tags, or journal entries.

To frame why these choices matter, the creator maps note systems onto a hierarchy: data at the bottom, information above it, then knowledge, and finally wisdom as the hardest goal. In that model, plain text and raw wiki navigation sit closer to “data,” while tags add “information” by connecting dots with meaning. Knowledge emerges when those connections are used to build something greater than the sum of parts—turning linked concepts into reusable understanding.

From there, the discussion shifts to how to create hierarchy without heavy query logic. Content “hubs” (implemented via content types) act like structured entry points—similar to folders, but more flexible—so users can group related material such as video projects under a single hub node. For discovery, filter-based browsing can replace complex tag queries: selecting what to include and excluding what to omit lets people refine results through interaction rather than Boolean logic.

A concrete cleanup example shows what happens when structure is missing. Stock-picking notes initially became clutter because each stock had its own page, even when the rationale wasn’t written and the pages didn’t contribute useful knowledge. Consolidating those items into a single “Stock picks” page—and then optionally creating category pages like “insurance stocks” or “big data stocks”—reduces noise while keeping the ability to find specific stocks later via unlinked reference search.

Finally, the workflow isn’t locked inside Logseq. Because notes are stored as local markdown files (journals, pages, draws, and assets), users can edit in bulk using tools like VS Code—such as find-and-replace to remove a tag across the entire database—then refresh Logseq to see the changes. The transcript also notes practical tool choices (including importing from Roam Research via JSON) and addresses mobile expectations: Logseq doesn’t have an app yet, but an app is discussed as being in the pipeline, with a tentative timeframe of a few months aimed first at UX and a canary/beta release.