Why you need a commonplace book and how to build one in Logseq

A commonplace book—an organized storehouse for ideas, quotes, observations, and useful snippets—is positioned as the antidote to information overload, because it turns scattered inputs into reusable knowledge over time. Instead of chasing “remember everything” productivity hacks, the approach treats personal knowledge like a living resource: capture what matters, keep it searchable, and later recombine those pieces into writing, speaking, decisions, and projects. The concept traces back to figures such as Isaac Newton, Leonardo da Vinci, Marcus Aurelius, and others who maintained notebooks to preserve insights for future use.

In practice, the commonplace book is defined as a central repository for “gems” gathered during life and learning. It can include both structured and messy material: reference numbers from service calls, links, screenshots, code snippets, and random tidbits that don’t seem important yet. When ideas come from other sources, the method emphasizes capturing them as building blocks rather than forcing them into a rigid taxonomy immediately. Leonardo da Vinci’s “collection without order” becomes a guiding metaphor: analog note-taking is onerous and hard to retrieve, but a digital system can keep the mess while making it findable.

To build a digital commonplace book, the transcript focuses on Logseq and frames personal knowledge management through a “five PS” workflow: planning, plowing, planting, propagating, and probing. The segment concentrates on “planting,” meaning the capture and storage stage. Four categories of planting are offered: (1) reference materials (links, images, code, font guides), (2) source-based writing (notes and highlights pulled from articles and videos, organized with metadata so they can be triangulated later), (3) freeform thinking and writing (journal entries mixing reflections, emotions, and plans, organized with tags), and (4) project-based thinking and writing (meeting notes, decision tracking, drafts, and scripts managed with templates and queryable tags).

The core advantage of Logseq is retrieval—making it easy to resurface information when it’s needed. The transcript contrasts this with the “Apple Notes vs. overthinking systems” meme: if search and keyword structure are strong, information becomes usable without elaborate upfront design. Logseq’s outliner structure, backlinks, indentation, and filters provide “minimum structure” that supports later organization. Blocks can be dragged and dropped, moved with shortcuts, and rearranged without losing context. Queries add another retrieval layer, letting users pull up specific threads or topics by searching titles, text, or metadata.

Finally, the method argues against premature structuring. Categories should emerge from what the user actually searches for, not from a top-down plan. As the database grows, structure should evolve, and the system should allow ad hoc restructuring. With local-first storage in markdown plus optional syncing (including Sync and services like iCloud), the commonplace book is framed as a long-term project designed to survive app changes—growing into a personal knowledge wiki that can be mined for future work and ideas.