Logseq Tutorial: 5 Tips to Improve Your Logseq Database

Logseq database cleanup often comes down to small formatting and workflow tweaks—especially when link clutter, code readability, and “missed tasks” start to pile up. A set of five practical adjustments focuses on keeping references tidy, making structured content easier to scan, and using queries and bulk edits to prevent important items from slipping through.

The first fix targets unwanted link references created by inline identifiers. When a number or token (for example, an import ID from Readwise or Omnivore) shows up as a linked reference, switching it to inline code using backticks stops it from being treated like a reference. That simple change helps keep bibliographic or link-heavy sections cleaner, particularly when writing tutorials where linked clutter isn’t desired.

Next comes code block ergonomics. Logseq includes CodeMirror support, which can be inserted by creating a new block and wrapping it with triple backticks. The tutorial shows how to set a language so the code renderer displays the language label in the top-right corner (e.g., specifying HTML). The goal is to make code snippets look and behave more like the familiar Obsidian-style experience, with cleaner presentation and better readability.

For navigation and at-a-glance organization, the guide adds page icons to the left sidebar. This is done through page properties on the first block of a page—adding an `icon` property and then selecting an emoji. The walkthrough notes different shortcuts for opening the emoji picker on Windows versus Mac, so the icon can be chosen quickly.

The most workflow-heavy tip uses Advanced queries inside the Daily Journal to catch tasks that might otherwise be overlooked. A custom query is stored in the global config under “default queries,” then surfaced in the journal to list items scheduled between a week and three months out. The query combines task status (e.g., to-do or later) with a scheduled date, producing a horizon view of renewal and administrative items—like driver’s license or passport-related deadlines—so they don’t fall through the cracks.

Finally, bulk editing in Visual Studio Code is used to clean up repeated template blocks across many Markdown files. After generating a search-and-replace instruction (with help from ChatGPT), the user applies a cautious replacement in VS Code to remove empty or unwanted sections—such as repeated gratitude/dream/affirmation blocks—across dozens of files, then confirms the changes propagate back into Logseq. The emphasis is on verifying before deleting, but the payoff is faster, workspace-wide maintenance without manually editing each page.

Overall, the tips connect presentation (inline code, code rendering, icons) with reliability (advanced queries) and scale (VS Code bulk edits), aiming to make a Logseq database easier to maintain and harder to mismanage over time.