When I think about knowledge management now, the problem I care about most is not “how do I take more notes?” It is how the things I already captured can come back into my thinking when I actually need them.
I used to manage knowledge in Notion. When I found an article, I would copy it into Notion, write down my thoughts, assign a few tags that felt relevant, and later use those tags to search the material back out. That workflow worked, but it depended heavily on whether I tagged things correctly in the moment and whether I kept organizing them afterward.
So the principle I use now is: preserve the source first, layer my own thinking on top, and let AI organize last. The human side keeps the real source and the judgment made at the time. AI, under clear rules, helps turn those fragments back into a knowledge base that can be searched, maintained, and expanded over time.
Core Logic: The Real Burden Is Maintaining Structure
Writing notes is not the hardest part. The real work comes afterward: where should this note live, which topics does it connect to, should an existing page be updated, does this need to become a new concept, and has an older conclusion been changed by new material?
If all of that has to be done manually, the knowledge base quickly becomes another job. The more notes there are, the higher the maintenance cost. The finer the categories get, the easier it is to give up before the organizing even starts. That is why I used to feel that knowledge management was valuable, but hard to keep up over the long term.
The Notion tag workflow did solve part of the problem. I could use tags to search for related material. But tags are more like an index than understanding. They do not actively connect articles, my reflections, and older concepts together. They also do not remind me which pieces should be merged, split, or updated.
The most useful thing AI brings here is not generating more text. It is taking over the repetitive, small, but important maintenance work: adding links, organizing indexes, checking duplicated concepts, updating MOCs, or pointing out which pieces should be merged, split, or verified again.
My Core Method: Preserve Sources, Add Thinking, Let AI Organize
The underlying structure I use is closer to the LLM Wiki idea described by Karpathy. It does not mix all notes into one pile. Instead, it separates sources, the organized Wiki, rules, and operation logs, so both the human and the AI know where each type of content belongs.
The first layer is raw sources. Articles, transcripts, screenshots, and thought fragments are preserved first. I do not rush to turn them into polished conclusions. The point of this layer is traceability. Any insight that gets organized later should be able to point back to where it came from.
The second layer is my own understanding. After reading, watching, or thinking through something, I add my own judgment: where I agree, where I am unsure, and which older concepts it may connect to. This step matters because a knowledge base should not only store information. It should preserve how I understood that information.
The third layer is where AI comes in. Following the Schema, Dictionary, MOC, and log rules, AI connects the new material back into the Wiki. If it should update an old page, it updates the old page. If it should become a new concept, it creates one. If it belongs in an index, it adds it there. That is what I mean by “let AI organize last.”
How Sources Come In: Obsidian, iCloud, and Memo
I choose Obsidian because it keeps notes as local Markdown files. The knowledge base is not locked inside a SaaS database, and it is much easier for Git, scripts, Codex, or other AI agents to read and operate on those files.
For syncing, I currently use iCloud for a very practical reason: my main devices are all inside the Apple ecosystem. iCloud is stable enough for me, and I do not need to move my note-taking workflow somewhere else just for AI.
That does not mean iCloud is the right answer for everyone. If you work heavily across platforms, Obsidian Sync may be more reliable. The real selection criterion is not the brand. The sync layer needs to be dependable, so AI is operating on the same predictable set of Markdown files.
Video Sources: Turning YouTube into Text with Memo
When the source is YouTube, I do not simply drop the video link into the knowledge base. Video is hard to search, quote, and reorganize. I first turn it into a text source that can be read.
Right now I use Memo for speech analysis and subtitle extraction. Its role is not to draw conclusions for me. It turns the video into a transcript, keeps the video link, preserves the necessary time context, and gives me source material that can be referenced later.
Then I layer my own thinking on top: what inspired me in the video, what was different from my previous understanding, and which parts are worth revisiting later. What enters the Wiki is no longer a simple video summary. It becomes “video content plus my judgment at the time.”
Finally, I let the LLM absorb it according to the LLM Wiki rules. It can decide whether the content should update an existing concept, become a new page, link to a certain MOC, or stay in the source layer until there is a better reason to organize it.
This workflow makes video content more than something I have merely watched. Once the video is converted into reliable text and combined with human judgment, AI can help categorize, link, and recover it later as material that remains useful inside the knowledge base.
How I Talk to the Dictionary Through Codex and Multica
My interaction model is not opening a generic chatbot and hoping it understands my knowledge base. I create a dedicated role in Multica, so it knows it is working with a specific Wiki instead of handling a context-free chat.
That role reads a dedicated Dictionary. It understands the terms in the knowledge base, the folder rules, the Wiki structure, MOC entry points, and common interaction patterns. When I ask a question, it considers the question, the Dictionary, the existing Wiki, and the current task together.
For example, I might ask: “I have been thinking about the relationship between AI and knowledge management lately. Help me find which related knowledge points already exist in SecBrain, and which parts have not yet become stable conclusions.” In that moment, the value of AI is not giving me a direct answer. It helps me see what I already know, what is still missing, and which concepts can be connected.
This does not have to be tied only to Multica. As long as a system lets a role read the relevant Dictionary, knowledge base content, and interaction rules, it can also connect to Codex, CoWork, or other agent systems. Multica is simply my current entry point. The important part is the combination of a dedicated role, a dedicated knowledge base, and a repeatable organizing workflow.
Keeping the Knowledge Base Healthy: Ingest, Query, Lint
Whether a knowledge base remains useful over time depends not only on how it is created, but also on how it is maintained. I split the knowledge base bot’s work into three recurring actions: ingest, query, and lint.
Ingest brings new material into the system. It is not just saving text. It first preserves the original source, then checks what the content represents, whether it should update an existing concept, whether it belongs in a certain MOC, and finally writes it into the Wiki layer and log.
Query means returning to the knowledge base with a question. AI should not answer only from chat memory. It should look for MOCs, indexes, or relevant pages first, and check original sources when needed. If a good answer creates new links or insights, those should flow back into notes instead of disappearing into the chat history.
Lint is the periodic health check. Knowledge bases accumulate dead links, duplicated concepts, outdated claims, inconsistent naming, and the same idea drifting across different pages. Once notes are also meant to be used by AI, those issues directly affect answer quality later.
The purpose of these three actions is to prevent the knowledge base from turning into another kind of chat log. New material needs a way in, old material needs to be retrievable, and the system itself needs regular checks. That is how AI can genuinely help extend the boundary of my thinking.
Who This Workflow Is For, and Its Boundaries
- It fits people who already have an Obsidian, Markdown, or local-notes habit. If all your data lives only inside a closed SaaS tool, there is less room for an AI agent to work directly.
- It fits people who want to turn what they have read, watched, and thought about back into searchable material. If all you need is a quick summary of a single article, a normal AI summarizer is enough.
- Raw sources and organized results need to stay separate. The original text should not be overwritten by AI, or it becomes hard to tell whether a conclusion came from the source or from AI inference.
- Important judgments still need to come back to human understanding and confirmation. AI can help archive, link, and check, but it should not decide for me which conclusions are stable knowledge.
- MOCs, indexes, Schema, and logs need to exist consistently, so the next AI session understands the current shape of the system instead of guessing from scratch every time.
- Video transcripts should preserve the video link and necessary time context. My own thoughts should stay distinct from the original transcript.
- The sync layer needs to be reliable. If cross-platform needs become heavier, it is better to consider official sync than to force everything through iCloud.
- This workflow consumes more tokens because each interaction may read the Dictionary, MOCs, Wiki pages, and source summaries. It fits deep organizing and exploration. It is not necessarily worth running fully for every tiny note fragment.
What Notion and Obsidian Are Each Good For
This brings us back to tool choice. Notion is not bad for knowledge management. It is closer to a collaborative document system. When the goal is multi-person editing, multi-person maintenance, and centralized management of documents or project material with a clear theme, Notion works very well. It is a good document hub, project management system, or shared team database.
But if the goal is a personal digital brain, especially one connected to an agent system on your own computer, Notion is less direct. The data lives in SaaS, and AI usually needs to go through APIs, permissions, and data structure conversion to read or write it. That adds maintenance cost.
Obsidian’s advantage is that it is natively a collection of Markdown files. As long as an AI agent can read and write files, it can read the notes, edit them, split them, add links, or organize MOCs. That is why I keep my personal knowledge base in Obsidian and Markdown: it feels more like a local workspace that both humans and AI can operate on.
| Dimension | Notion Is Better For | Obsidian Is Better For |
|---|---|---|
| Use case | Multi-person editing, shared management, team document collaboration | Personal ownership, long-term accumulation, knowledge bases connected to AI agents |
| Content type | Documents with clear topics, project material, management pages | Thought fragments, Markdown notes, MOCs, evolving concept networks |
| AI integration | Usually requires APIs, permissions, and data structure conversion | If AI can read and write files, it can organize and edit directly |
| Main strength | Collaboration, database views, project management, and document management | Local files, Markdown, and direct access for toolchains and agents |
| Best fit | People who need to co-edit documents, manage projects, or maintain a team knowledge base | People who want a personal digital brain or second brain where AI helps organize |
Closing: A Knowledge Base as a Human-AI Workspace
For me, AI is not a replacement for knowledge management. It is a way to keep the part of knowledge management that most people eventually abandon running for longer.
I still need to be responsible for understanding, choosing, and judging. But AI can help connect my thoughts back into structure, write concepts surfaced in conversation back into the Wiki, and periodically check whether the system is starting to drift.
That makes the knowledge base more than a warehouse, and more than a chat history. It becomes a workspace that can be queried, organized, checked, and slowly expanded every time I think through something.
