I first learned about Obsidian in 2020 when someone on Zhihu posted a screenshot of a purple icon.
My first reaction was: this icon looks like a rock.
Later I learned that Obsidian means volcanic glass—hard, sharp, and clean-cutting, just like the hard black stone in Minecraft.
That metaphor still holds up today.
I have a habit: when I like a tool, I frantically collect everything about it. Obsidian is the one I've collected most aggressively. In my YouMind BOARD alone, there are over a dozen articles related to Obsidian: beginner tutorials, deep research, AI integration practices, product philosophy, and I even made emojis for it.
Today's post is what I want to tell you after reviewing all that content. Warning: it's a long read; I suggest bookmarking it first.
Let's clarify my relationship with Obsidian first
I am a heavy Obsidian user, having used it since 2020—six years now. To support it, I became an Obsidian VIP supporter. I've written about my experiences, pitfalls, and compiled an Obsidian Feishu knowledge base that is fully public and has been used by over five figures of netizens.
Link: https://my.feishu.cn/wiki/PPW4w6eEYi01dfkUgc3cGYsVnkh
I've also publicly released the Obsidian vault I currently use, including my favorite plugins, selected themes, Agent system prompt examples, memory archiving cases, and custom CSS to make the graph more beautiful.
Link: https://my.feishu.cn/wiki/GccZwP13MikuGJk07mTcOXAfnAg
So you can read this article with a healthy dose of skepticism. Where I praise it, I'll try to provide sources; where I criticize it, I'll try to provide evidence.
Pain points first, tools second
Have you ever had this experience: you clearly remember saving a great article, but when you actually need it, you search your entire note library and just can't find it? WeChat, Zhihu, web clippings, PDFs, video transcripts, AI chat screenshots—your bookmarks get fuller and fuller. Three months later, you can't remember the title or keywords, and searching yields nothing. Folders get messier, and notes are recorded but never used.
I'm not the only one with this problem. Almost every tutorial I've collected starts with the same thing: hundreds of articles lying in bookmarks, and dozens of half-finished projects in notes. The biggest problem with traditional notes is: all input, no output. You're not saving a knowledge base; you're building an ever-growing warehouse. Everything is there, but no one organizes it, no one indexes it, and no one tells you what's related or what's outdated.
I particularly remember a line from the article "How to Build a Minimalist Personal Knowledge Management System with flomo + Feishu in 2025?":
Lifelong learners, self-media creators, engineers, and designers are already recording their own notes, but they haven't systematized them yet. They need cases to stimulate their ideas for building a system.
Link: https://my.feishu.cn/wiki/P7BSwiqqAi8tSNkbfPMcz6IFnic
47 plugins and a vault deleted ten times
In 2020, I met Obsidian. My first reaction was: Wow, local storage! Bi-directional links! Knowledge graph! My second reaction was: How can a note-taking app be so sci-fi? Then I did the classic move—creating a vault. Then classic move 2.0—frantically installing plugins. 47 of them. Do you know what it's like to have 47 plugins running simultaneously? It means by the time Obsidian opens, your food delivery has arrived.
For the next few years, I was stuck in a loop: find a new plugin → get excited → frantically configure → find it useless a week later → see someone else's vault looks better → delete and rebuild → back to step one. I went through this loop at least 10 times. Once, at 3 AM, while watching a Bilibili tutorial, I deleted a note library I had been writing for half a year. I woke up the next day looking at the empty folder and said: Who am I? Where am I? Where are my notes? 5,000 notes, piled there, became a Cybertronian planet.
Above is my previous vault. Does it look good? Yes. Is it useful? No. I once recorded a thought in flomo: "99% of the time you spend on Obsidian is wasted." An hour fiddling with skins, two hours fiddling with plugins, looking at best practices in the community, but writing very little in the vault. This is typical "tool worship"—you think you're learning, but you're actually just procrastinating. (I know this sounds like I'm scolding, but the one who deserves it most is myself.)
I have more beginner tutorials in my bookmarks than you can imagine
Conclusion first: I've bookmarked no less than six beginner tutorials for Obsidian. Note these are "beginner" ones, teaching you how to download, install, create a vault, and sync plugins. I've read every single one, and each has a different focus.
"The Strongest 2026 Obsidian Nanny-level Tutorial: Build Your Second Brain in 10 Minutes" is the most restrained one I've seen. Original here. It focuses entirely on one thing: creating a vault, three Markdown writing styles, two shortcuts, and bi-directional links. It tells you not to study YAML, advanced queries, themes, or community plugins yet. It presents Obsidian simply: a folder better suited for long-term storage, searching, and connecting text. Every note is a real file on your computer; if you stop using Obsidian, you can still open them with any text editor. It doesn't lock your notes in a proprietary format. At the end, there's a 3-minute exercise: create a note, write a title, a list, a link, and check the backlinks. If you can do that, your vault, Markdown, and links are working.
"Obsidian Complete Beginner Tutorial (Local Knowledge Base)" takes another route. Original here. It starts with a question: In the AI era, everyone needs their own knowledge base—why? Because AI itself has no stance, no experience, and no aesthetics. If you ask it a question directly, the answer is correct but soulless. The difference lies in whether you can feed it "your own materials, methodologies, experiences, and judgments." The same model, fed with your stuff, outputs something with your imprint. I really agree with this. It also explains how to choose between MOC, PARA, and Zettelkasten: write for half a month first, see which need arises naturally, and then consciously use the corresponding method. You can mix all three.
Then there's "Obsidian Plain Language Tutorial for Beginners." Original here. It describes Obsidian as an "indestructible, extremely safe, and freely modifiable electronic notebook." Typesetting is as simple as talking, and functions are as free as building Legos. It's friendly to non-programmers. The vault is a drawer, the interface is a desk, and plugins are the furniture store. Even my mom could understand this metaphor.
And "Don't Just Install Obsidian and Let It Gather Dust." Original here. It starts from an empty warehouse, creates a vault, names it, picks a location, builds directories, writes a homepage, makes links, starts a diary, looks at the graph, and finally hands it over to Codex. One sentence stuck with me:
A vault is not a mysterious database; it's just a regular folder.
It breaks the knowledge base process into five steps: collect → inbox → organize into notes → link → find again from the homepage → output articles or plans. Run this process first; plugins and automation come later when you actually face problems.
Oh, and another "Obsidian Zero-Base Tutorial." Original here. It teaches using GitHub for cloud sync and even how to use .gitignore to prevent uploading workspace files. The images are high-def, and the steps are screenshot-level—the kind you can just follow along.
Stop looking for tutorials; the essence is in the forum
I also bookmarked a tweet titled: "Stop looking for Obsidian beginner tutorials everywhere; the essence is in the forum."
This is true.
If you want more professional content, go to https://community.obsidian.md/. This is the official community, where plugins and news are even more comprehensive.
Deeply researching this document completed my understanding of Obsidian
I have an "Obsidian Deep Research" document with a massive amount of information. It reveals Obsidian's roots. Obsidian was created by Shida Li and Erica Xu in 2020 during the COVID-19 quarantine. Both graduated from the University of Waterloo and previously made an outliner tool called Dynalist. Erica said in an interview that the basic idea for Obsidian was in her head for two years; she once felt like a weirdo for wanting such a personal knowledge base. To her surprise, many people told her they wanted the same thing. On March 30, 2020, Obsidian released its public beta, coincidentally the same week the WHO declared COVID-19 a global pandemic. In 2023, Steph Ango (known as kepano), the designer famous for the Minimal theme, joined as CEO.
The team structure is: Shida Li as CTO, Erica Xu as COO, kepano as CEO, plus a few engineers—about 7 people in total, plus a kitten (yes, you read that right).
With just these few people, they built a product valued at $350 million with over 1.5 million monthly active users, completely self-funded without external investment. There's a slogan on the website I looked at for a long time:
Your knowledge should last
Your notes are stored as plain text Markdown files on your local device. Even if the Obsidian company disappears tomorrow, your data remains intact and readable. You can open them with VS Code, Vim, or any text editor. This "data durability" promise is incredibly attractive in an era where SaaS tools can shut down or change pricing at any time. Moreover, current AI Agents can easily access these plain text files.
Their business model is also interesting: the core app is free, Sync service is $4/month, Publish is $8/month, and Catalyst is a one-time donation starting at $25. They only charge for value-added services, maintaining high profit margins with a tiny team.
Links over categories—this deserves its own section
The deep research document has a core view: links are prioritized over categories. Traditional note tools encourage you to put notes into folders—a tree structure where each note belongs to one place. Obsidian's core proposition is: use bi-directional links to connect notes, letting knowledge naturally form a network. Every link automatically creates a bi-directional edge, and the knowledge graph emerges from the notes themselves. This is closer to how the human brain associates ideas. Simply put, folders only tell you "where this note is," while links tell you "what this note is related to." This is the fundamental difference between Obsidian and Word. Word documents are usually separate; even if two discuss the same thing, there's no obvious link. In Obsidian, you use [[Note Name]] to connect them. Later, opening one not only lets you jump to the other but also shows which old notes mentioned it. The more notes you have, the more valuable this web becomes.
Its plugin ecosystem is a furniture store
As of March 2026, the Obsidian community has over 2,500 plugins. Every week, 6-14 new plugins are released and 80-100 are updated. The r/ObsidianMD subreddit has over 256,000 subscribers. Representative plugins include Dataview (querying the vault like a database), Templater (advanced template engine), Excalidraw (hand-drawn style charts), Calendar, and Kanban. There are also many AI integration plugins. In May 2026, the official team launched a new automated review system to further improve the safety and quality of the ecosystem. This marks the transition from "wild growth" to "sustainable governance."
But many plugins are also a trap. The trap of over-customization is real: 2,500+ plugins mean endless configuration possibilities. Many users fall into "fiddling with the tool" rather than "using the tool." There's a famous self-deprecating joke in the community:
I spent 3 hours configuring my productivity system instead of being productive.
I understand this all too well.
Competitors: Notion, Logseq, and Roam
Notion is suitable for team collaboration and project management; Obsidian is for personal deep thinking and long-term accumulation. Notion's data is in a proprietary cloud format; Obsidian's is local Markdown. Notion is strong in collaboration; Obsidian basically doesn't support it. Notion has a flat learning curve; Obsidian's is steep. Data ownership: Notion's belongs to their servers; Obsidian's belongs entirely to the user. AI features: Notion has native AI ($10/user/month); Obsidian relies on plugins.
Logseq is Obsidian's closest competitor—also local-first, Markdown-based, and free. The core difference: Logseq is a block-level outliner where every bullet point is an independent unit; Obsidian is document-level. Logseq is better for rapid fragmented capture and journal-driven workflows; Obsidian is better for long-form writing and structured documents. Logseq is open-source; Obsidian is proprietary but free.
Roam Research triggered the "bi-directional link" craze in 2019-2020. But its $15/month price and proprietary cloud storage led many users to Obsidian. Roam's block-level referencing is finer, but Obsidian's free strategy, local storage, and plugin ecosystem won in terms of user scale. I suggest anyone wanting to switch note tools read these comparisons. (Sources: PCMag Review, Obsidian Website
Karpathy's tweet turned knowledge bases into code repositories: LLM-Wiki is here
https://x.com/karpathy/status/2039805659525644595
Karpathy posted a tweet about how he uses LLMs to build and manage a personal knowledge base. He said most of his token consumption is no longer for writing code, but for "manipulating knowledge." I read it repeatedly. What he's doing matches what I've been messing with in Obsidian, but he used a word I couldn't find: "Compile." Compiling raw materials into structured knowledge. Obsidian Vault is the code repo; LLM is the compiler. This analogy makes everything click. If you've written code, you'll get it instantly: Software Engineering → Knowledge Base Engineering. src/ → raw/ (raw materials); build/ → wiki/ (knowledge entries); logs/ → outputs/ (Q&A archives); Compiler → LLM; IDE → Obsidian; Lint/CI → Health checks; Incremental compilation → only processing new/changed raw files.
It boils down to three things:
- Layering: Raw materials, compiled products, and runtime outputs are separate. You don't mix .class and .java files; don't mix notes either.
- Incremental: No need to rebuild everything; just compile the new articles daily.
- Traceability: Every knowledge entry can be traced to its source. It's not "I think I saw this somewhere," but the AI telling you: "Source is in raw/articles/2026-03/, third paragraph."
What his Vault looks like
I applied Karpathy's methodology to my own Vault. The directory structure looks like this: Under Vault/, I have raw/, wiki/, outputs/, and platform-specific directories like x/, official_account/, xiaohongshu/. raw/ holds original materials (unchanged). wiki/ holds compiled products maintained by LLM (indexes, concepts, summaries). outputs/ holds runtime outputs. Finished products are for readers; the wiki is for myself—don't mix them. Ingestion has three entries: Web Clipper for articles (forcing source_url, author, etc.); Podwise for podcasts (auto-transcript + AI summary); manual clipping for X tweets. This step is vital: clipping without metadata is useless.
Compilation is the key. After gathering 5-10 raw files, do the first "compilation." Let Claude read raw/articles/, generate summaries, extract concepts, and update indexes. Quality is ensured by CLAUDE.md, which contains compilation specs: summary templates, concept fields, naming rules. The first time might take an hour, but incremental updates are fast.
AI Output to files: the most brilliant design
In Karpathy's methodology, there's a design I find particularly clever: saving AI outputs as files. Previously, I'd ask AI a question, get an answer, and close it. Next time, I'd ask again. The answers were stuck in chat history, which is useless. Now, every complex query to the knowledge base is saved as a Markdown file in AI outputs/, including the question, time, source, conclusion, and uncertainties. After three months, I have dozens of these files—they are knowledge entries themselves because they contain the reasoning process. Every time you chat with AI, the knowledge base grows. I force AI to only output to AI outputs/ so its generation doesn't mix with my own thoughts. Most people miss this step.
Knowledge bases have technical debt and need health checks
Karpathy suggests regular Health Checks for three things: consistency, completeness, and islands. Consistency: Are there conflicting definitions in wiki/concepts/? Completeness: Which entries lack definitions or sources? Islands: Which notes have fewer than 2 incoming/outgoing links? Reports are saved to outputs/health/ weekly. After a few weeks, the biggest feeling is: when I find a note, I dare to use it directly. I no longer have to wonder if it's reliable.
Don't build RAG right away
This must be said because too many people take detours here. At the mention of "AI + Notes," people think of RAG: picking embedding models, setting up vector databases, tuning chunking strategies. They spend a month on architecture while the library only has 20 articles. Karpathy recommends: when the library is small (e.g., 100 articles), maintaining a few index files is enough. Let the LLM read the index to locate, then read the content directly—simple, reliable, zero extra cost. When you hit 10,000 notes and can't find things, then consider RAG. Process first, infrastructure later.
Karpathy's last sentence was:
This stuff currently looks like a bunch of hacky scripts, but there's room to make an incredible new product.
It reminds me of version control before 2006—svn, cvs, git command lines used only by programmers. Then someone made GitHub, and collaboration changed. Personal knowledge bases might be at a similar turning point.
Kepano's reply: the most thought-provoking human-AI symbiosis case
Then Obsidian CEO Kepano replied, and his words made me think deeply.
Keep your personal vault clean and create a messy vault for your agents.
Just that one sentence. He prefers his personal vault to have a high signal-to-noise ratio with known sources. Second, he believes in separating personal creations from agent creations to prevent "polluting" your main vault with untraceable ideas. Third, if you mix them too much, Obsidian is no longer a representation of "your thoughts." Search, backlinks, and graphs will no longer be about your knowledge, but a hybrid of you and AI. At that point, we can't tell if it's an AI knowledge base or your own. Fourth, only when an agent workflow produces something truly useful should it be introduced to the main vault, and even then, put it in a clippings folder.
Combined, these points ask a sharp question: If your knowledge base is full of AI-written stuff, is it still "your" knowledge base? This hit me because, over the last 6 years, I've walked that exact detour.
When AI came, I almost made the same mistake again
When Claude Code could operate Obsidian directly, when the Claudian plugin let me chat with AI in the sidebar, when Skills and MCP workflows were everywhere—I got excited again. I installed Claudian, let AI organize notes, find hidden links, fix paths, and create MOCs. It did in 3 minutes what took me 30. But one day, I stopped. I looked at the summaries and structures AI wrote for me and realized: I didn't recognize them. They were neat and professional, but they didn't grow from my brain. It was like copying homework—the work was done, but did I learn anything? That's when I understood Kepano: if mixed too much, Obsidian is no longer "your thoughts."
I wrote in flomo: "Don't just put AI answers in; don't get anxious over cool ClaudeCode+Obsidian skills. Essentially, this 'efficiency' is outsourcing your thinking."
Two knowledge bases, two philosophies
Karpathy and Kepano represent two philosophies. Karpathy's is efficiency-first: let AI be the master of knowledge; you just feed data and focus on questioning and decision-making. This is a game-changer for research. Kepano's is thinking-first: your knowledge base is a mirror of your thoughts; every piece of content should have a traceable source. AI can help, but products must be isolated; only what you've filtered enters the main vault. These aren't contradictory, but they represent different goals. One is about handling information flow; the other is about the value of the thinking process itself. After 6 years of fiddling, I lean toward the latter.
Don't outsource thinking
I have a record in flomo: "flomo is my 'collection station' or 'data lake.' ... I compress all information into text manually. In this era, it seems 'stupid,' but it's how I practice the principle of 'not outsourcing thinking.'" Yes, AI can do it faster and better. But when you compress a video's core points into one sentence in your own words, that compression is thinking. When you tag or link a note, you're training your cognitive framework. If you give this to AI, you save time but lose the thinking. I wrote: "The standard of successful knowledge management isn't how many notes you have, but whether they help you think and act better." Because I typed those words myself, they are in my brain. If AI summarized them, I'd forget them in a glance.
Evergreen Notes: the anchor I found after 6 years
I found an anchor: Evergreen Notes. This concept was proposed by Andy Matuschak in 2020. Original here. The core idea is simple: each note should be conceptual (one idea), atomic (one thing), and organized by links rather than tags. Allow for half-finished drafts, but keep writing and linking. I set three standards: "1. Must have a bolded title; 2. Must explain one thing clearly; 3. Must have links." No need for 47 plugins or fancy themes. I even turned on Safe Mode and installed zero third-party plugins. "Write, think, output—use the simplest way with the most powerful software to do the most important thing. This is 'using heavy tools lightly'." If the second brain is a vault, evergreen notes are the cash, and the standard for writing them is the printing press. Your knowledge base's value depends on your "printing press" standards. This is a basic skill AI cannot replace.
AI isn't useless, but the order matters
"Not outsourcing thinking" doesn't mean rejecting AI. Karpathy's solution is optimal for specific scenarios like digesting 100 papers. I use AI too. I recorded: "AI only helps those who can distinguish good content from bad—build a basic cognitive framework first, then use AI to help build content." Judgment is the multiplier. If you don't know what valuable insight looks like, you can't verify AI's output. My current approach is separation: my main Obsidian vault only has my own evergreen notes—every word is mine, every link manual. AI has its own workspace. Only when AI produces an insight I truly agree with do I rewrite it in my own words for the main vault. The act of rewriting is the act of internalizing.
File over App
Kepano's other core concept is "File over App." I noticed this when flomo crashed once—I panicked because thousands of notes were there. Later, Feishu, ChatGPT, and Claude all had outages. I realized: "All software is transient, but files are different—you need to control the accessibility of your files." This is why Karpathy chose Obsidian: data is local .md files, independent of cloud services. Your knowledge should be on your hard drive in a format you can always open. This is crucial in the AI era because tools iterate fast, but .md files don't. Markdown has been a universal format for 22 years. "The more powerful AI and software become, the more you need a minimalist mindset." Tools change, but files and your thinking remain.
AI + Obsidian practices in my bookmarks
I have a pile of practical content on landing LLM Wikis. "Goodbye Manual Organizing: How to Build an AI-Driven Personal Knowledge Base" by Jin Chenma Original summarizes it into five tasks: continuous compilation, cross-material linking, Q&A backflow, network consolidation, and health checks. It turns your knowledge base from dead to alive. It provides prompts for Codex or Claude Code to initialize structures. Directories: raw/ (warehouse), wiki/ (bookshelf), outputs/ (workbench). Rules: don't build RAG first, don't save content without sources, don't mix raw and wiki, don't let AI overwrite originals, don't leave Q&A in chat history.
"Hermes+Obsidian+LLM Wiki" Original discusses the Hermes Agent, which has a learning loop. It has an llm-wiki skill: you say "write this to the knowledge base," and it extracts entities, creates files, and adds links. Crucially, it only acts when you explicitly ask, so your vault isn't polluted by random chats.
"Building a Self-Growing Personal Knowledge Base with Codex + Obsidian" by Cang He Original uses three layers: Raw (evidence), Wiki (understanding), and Schema (rules). It provides 10 rules for AGENTS.md, like "search existing pages before processing new data." It also mentions WeSight, which integrates these steps into Obsidian. It notes that "self-growing" doesn't mean fully automated; judgment is still human.
Finally, "This is the God of Obsidian Creation!" Original introduces Ailu, a super assistant inside Obsidian for memory, creation, and publishing to platforms like WeChat and X. It shows the upper limit of Obsidian plugins.
Is Obsidian worth the effort?
My answer: Yes, but don't focus on the "effort" of fiddling. Obsidian is one of the best digital tools for Zettelkasten and Evergreen Notes. Its local Markdown storage is ideal for long-term maintenance. But it has weaknesses: poor collaboration, steep learning curve, and mediocre mobile experience. Over-customization is a trap. My current attitude: use heavy tools lightly. Use the simplest way to do the most important thing.
Dissuasion: Who shouldn't use Obsidian
If you want zero-threshold, open-and-write software, Obsidian might not be for you. Its learning curve is steep. PCMag says it's for "power users who don't need collaboration features." If you need real-time team collaboration, don't use it; Notion is better. If you just want to record fragmented thoughts, flomo is better. Obsidian is the final destination for evergreen notes—those polished through capture and thinking. If you have no intention of outputting, don't bother. And if you're a perfectionist who wants a perfect folder structure before writing, Obsidian will drive you crazy. Write first, organize later.
Competitors: a fair word
Notion is strong in tables, collaboration, and templates, but its cloud-only data can be a bottleneck. Logseq is close but better for fragments; Obsidian is better for long-form and structure. Roam Research is expensive and cloud-based. flomo is the best collection station but it's a data lake, not a knowledge base. There's no perfect tool, only the right workflow. (Obsidian is the final home for evergreen notes; that's its irreplaceable spot.)
Back to the original question
When AI can organize everything, do you still need to think? My answer: You need it more than ever. When information and organization costs approach zero, judgment becomes the scarce resource. Knowing what to keep, what's profound, and finding hidden connections—these come from daily writing and thinking. AI can find the notes, but the "Aha!" moment, the spark of insight, can only ignite in your own neurons. "Your second brain is not a warehouse, it's a kitchen." A warehouse just stores; a kitchen creates. And the person standing in that kitchen must be you.
Final thoughts
I've noticed that among the Obsidian content I've collected, tutorials and AI practices each take a third, and the rest are warnings to "stop fiddling." The consensus is forming: tools are means, thinking is the end. Obsidian's value depends on whether you leave something truly yours behind. I'm preparing to release my current Obsidian vault, including plugins, themes, and Agent prompts. I'll test it in my fan group first. I'm curious: how many notes are in your Obsidian? How many did you write yourself? What does your graph look like? See you in the comments.
I am Jackywine, AI creator, product designer, and Obsidian VIP. I focus on AIGC, Agent products, and personal branding in the AI era.
Sources: 2026 Obsidian Nanny Tutorial, Obsidian Beginner Tutorial, Karpathy's LLM Knowledge Base Tweet, Andy Matuschak: Evergreen Notes, Obsidian Official, PCMag Review.
