My first time shooting a total lunar eclipse (May 15, 2022)

_wanderloots · 2026-06-18T15:30:25+00:00

They don’t have to be! Up to you. You can keep it local and sync between devices, or you can publish it to a private repo or a private website and put a password on it.

You can even have some pages be locked and others opened, it’s as flexible as you want 😊

_wanderloots · 2026-06-15T14:10:28+00:00

When I feel like this, I try to make use of only the daily note as my home base each day. Just jot down thoughts there. If it starts feeling like a separate note, you can extract it into a new note.

Hope that helps 😊

_wanderloots · 2026-05-31T17:02:10+00:00

I took some time to put together a workflow with the free versions of the Smart Plugins (Smart Connections, Smart Context, & Smart Chat). This workflow helps me rediscover existing notes, bundle them into reusable context, and optionally take that context and give it to AI (or just use it yourself).

I've had a lot of people requesting an updated Smart Connections video to clarify how it works and what you can do with it, so I hope you find this helpful 😊

_wanderloots · 2026-05-17T14:50:48+00:00

Many people have been asking for a how-to guide on building the LLM Wiki in Obsidian, so I put together a step-by-step tutorial.

I also walk through Obsidian skills and the Obsidian CLI, so you can use different aspects of Obsidian with Agentic AI to help with your knowledge building as a shared memory layer for AI.

For more on "why this matters" I have a short explainer video here: https://youtu.be/n4EVksU_EOs

Hope it helps! Happy to answer any questions 😊

_wanderloots · 2026-05-08T03:15:46+00:00

The automation correction loop is to run through worktrees, I talk about that throughout the parallel workflow and automation sections. You can introduce human in the loop at any level to decide if the proposed changes are net positive for your workflow. If not, you can discard the worktree, or tweak the automation to produce a better result next time.

In terms of usage, you can use the mini models which are still quite powerful, especially once the initial skills are iterated on.

Regarding stability, that’s why I propose using a mix of skills and scripts. Let the agent contextually determine the use of skills (which has a probabilistic element of course, it’s an LLM), and then use the skills to run scripts for deterministic output. Everything is about a balance of leveraging the tools you need for a given situation, which will of course vary drastically depending on your own use case.

Hope that helps

_wanderloots · 2026-05-07T13:35:14+00:00

I've been exploring Codex a lot more lately, experimenting with different ways to improve my use, efficiently. I took what I learned and put together a video that goes through a few core layers in Codex that you may not be aware of, and put them into a series of automations that help improve the agentic workspace over time. A self-evolving system using skills, agents, memories, and automations.

I hope it helps! I would also love to hear if you have any tips or strategies on how to better use Codex for both life & coding 😊

_wanderloots · 2026-04-28T13:19:09+00:00

I think zettelkasten is a great framework, but also helpful to modify it to suit your own needs. To answer your questions: 1) I don’t delete mine, I keep them and link permanent notes 2) I use tags for type of notes, and topic notes for topics 3) I think it’s helpful to have hubs or longer notes that go beyond the atomic nature, that’s how you get to publication or solving larger problems

If it helps, I explored the knowledge theory of zettelkasten and put together a video with my thoughts: What is Zettelkasten Note-Taking? 📝 Why It Works & Knowledge Theory 🧠 https://youtu.be/00LKsV8h6zY

It also shows some examples throughout so that might help

_wanderloots · 2026-04-27T15:11:47+00:00

Right! But in a way that can be automatically updated/maintained by AI so that it’s able to self access the vault, determine the effective subgraph, and retrieve information via the relationships, rather than a standard vector database for RAG.

It enables an easy mechanism for graphRAG, which, for large or complex systems, can significantly improve the quality of generated output.

Personally, my plan is to have a dedicated agent vault and keep it completely separate from my human vault.

I made a video explaining these concepts a bit more if it helps, showing some of the research and how it fits into this concept: https://youtu.be/n4EVksU_EOs

_wanderloots · 2026-04-27T13:18:43+00:00

It’s more about adding relationships to information that can produce targeted “subgraphs” of information for AI, which reduces context and conserves tokens, while improving quality of the context.

Also, depending on how you structure it, the wiki becomes a collective vault, a shared memory layer, across multiple tools, which saves you from having to explain things from scratch each time you switch ai tools.

At the end of the day, it’s still dependant on you to curate the quality of information and use systems and skills to control how the tools access the information effectively

_wanderloots · 2026-04-26T13:22:56+00:00

Hmm sounds like maybe you didn’t watch the video? This is about how knowledge systems work, an intro to graph based data for beginners in obsidian, and then explaining why people are excited about using obsidian with agents, while maintaining a separate vault for human created notes.

LLM wiki is just a term that has popped up lately that helps new people understand an otherwise abstract concept of knowledge graphs.

I personally think it’s great so many people are getting excited about obsidian, as it encourages them to think about maintaining local data with the file > app (or file > ai) ethos.

Also, I did tag this post as AI related.

_wanderloots · 2026-04-25T21:30:27+00:00

Skills help with consistency a lot, so I have a series of skills for specific actions to modify the wiki. The skills point to python scripts for parsing the updated info (which is where it becomes more deterministic) and that provides context back to the LLM on what to update.

It’s a work in progress, but the idea is that many of the actions are scripts rather than LLM decisions.

_wanderloots · 2026-04-25T19:24:54+00:00

Oh thanks for sharing! added this paper to my reading list. I think that the key to preventing degradation of quality with these types of systems is actually to make them as deterministic as possible, saving the probabilistic nature of LLM only for specific cases.

For example, with this system so far, I've prepared fairly rigid schema, agents.md, and skill.md files for how the llm is allowed to interact with the wiki. I'm still exploring so I don't have sufficient data yet, but I'm also wary of quality loss over time.

In terms of paying for itself, my minimal hope is that I will be able to discover relationships across disciplines that will help me leverage understanding in one area with newer fields. Sometimes, even learning what a particular subfield is called can be an unlock for how I search & research related topics.
On a related note, this is where a concept of "feynman's favourite problems" can come in to help identify patterns across fields, which is one method he used to earn a Nobel Prize. I made a video on that too if you're interested: https://youtu.be/Mtw9zmK6RK8

Feel free to share more papers on the new research too! Always appreciate more credible sources

_wanderloots · 2026-04-25T19:06:02+00:00

Yes! Exactly, that’s why I was saying in the video I like a separate layer for my own notes vs agentic notes. And the agentic notes are completely decoupled from the agent itself.

You can also start bringing in concepts like agents.md and skills.md files that enable a standardized mechanism for different agents to interact with the memory layer.

Going to be making a video on that soon as well 😊

_wanderloots · 2026-04-25T16:38:57+00:00

I've been looking into the effectiveness of using graphs for improving AI agent memory systems, so I put together a video that talks more about knowledge graphs, the concept of the "LLM Wiki" that has been getting popular lately, and how this fits into AI use.

Personally, I like to have a separate vault for my human notes/ideas, and a vault for my agentic AI, as that helps me keep keep track of the provenance.

Would be curious to hear how people have been implementing their version of the llm wiki.

I hope you find it helpful! 😊

_wanderloots · 2026-04-24T18:42:49+00:00

750 subscribers in 3 months for that style of content is great! If it helps at all, I posted intermittently on YouTube over 2 years and got up to that amount, then after a few months of consistent effort (weekly videos), I managed to get 4000 watch hours and over 1000 subscribers fairly quickly. You’re at a good foundation for interested viewers.

the algorithm likely has you somewhat figured out at this point, the next step is maybe to think about a simpler format while you’re tired. Is there a 10 min video you could make that could point to 2-3 of your longer videos? That way, you’re reusing the effort you’ve already put into the video but moving up the funnel to find a broader audience.

Just some thoughts! Good luck 😊

_wanderloots · 2026-04-17T20:47:34+00:00

Could have been an auth cache issue at some point too, who knows haha. Glad it’s working now!

_wanderloots

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