PRODUCTIVELY Learning New Things Using Obsidian

Learning new skills in a way that actually sticks comes down to a repeatable loop: acquire knowledge, remember it with spaced repetition, and practice it through real projects. The core idea is that study works best when it feeds into immediate or near-term use—otherwise it tends to fade like school notes that never get applied. From there, the method breaks learning into three parts: a way to consume information, a system to recall it over time, and a mechanism to turn it into something you can do.

For acquiring, the approach depends on the domain. Languages might come from teachers; law or medicine might come from professors. Self-directed learners can use videos, blogs, courses, and other resources to gather material. But consumption alone doesn’t create long-term retention. That’s where spaced repetition enters: it uses flashcards and schedules reviews based on how well each card was recalled. If a card is marked “hard,” it reappears sooner; if it’s marked “easy,” it’s delayed further—keeping attention focused on weak spots until they strengthen.

Practice is the third leg, and it’s treated as non-negotiable for practical subjects like programming, Linux, or languages. The strategy is to start with small, simple projects that force new concepts to surface, then iterate into more complex work. In a Python example, once the basics are known, a learner builds something small and expects to hit unfamiliar ideas. Each new concept gets captured as notes and flashcards, then the learner returns to the next project—repeating the cycle. Over time, familiar concepts recur (now with real understanding), while new ones keep getting added. The result is described as “exponential” learning because projects continuously generate both review material and fresh gaps.

To implement spaced repetition inside Obsidian, the workflow centers on the Space Repetition plugin (created by Stefan). After installing it via Community plugins, the key setup is telling the plugin which tags to treat as flashcards—by default it uses #flashcards, but the system can be customized (for example, tagging notes with #Linux). Reviews are run through a dedicated command (“review flashcards”), where each card shows a prompt, reveals the answer, and then the user rates recall as easy/good/hard to control when the card returns.

The transcript also details practical ways to structure flashcards. Inline flashcards store prompt/answer pairs in a single note, often using a YAML header with the relevant tag. Another approach spreads flashcards across multiple notes (e.g., “Linux networking”), so when a card is missed, the user can jump directly to the note containing that card for context and clarification. The plugin supports multiple flashcard types—inline, inline reversed, multi-line, multi-line reversed—and even “cloze/blank” style cards for context-dependent answers, such as language learning. There’s also a notes-level review feature for entire notes, though it’s presented as optional.

Finally, the transcript argues for prioritizing one’s own flashcards over community-made ones. The reasoning is that flashcards should reflect what was encountered during active work, not what someone else found useful later. The system is meant to keep limited review time focused on concepts that matter right now, then add new cards only when they become relevant through real projects. Brilliant is mentioned as an alternative way to learn STEM interactively by doing, with lessons curated from basics to advanced.