Transcription
I've been running an experiment for the past few weeks. I gave my AI agent access to some key tools, my notes and knowledge base in Obsidian, my tasks and project management in Linear, and a direct line to me at any time through Slack. Something really interesting happened, and it stopped feeling like a tool and really started feeling more like a teammate.
In this video, I'm going to show you the exact system. Three tools, one AI agent, and how they work together. No theory, just the actual setup I use every day. And if you want to try this yourself, I put together a full setup guide with every config file, every template, step by step. I'll tell you how to get it at the end. But make sure you stick around because the setup only makes sense after you see how all the pieces fit together.
So, the problem most people hit with AI assistance is you ask it to do something, it gives you an answer, and then it forgets that you even exist. Every conversation starts from zero. It doesn't know what you're working on. It doesn't know what you decided last week. It really has no memory. And if you've tried to fix that by pasting context into every prompt, you know that that doesn't really scale. You end up spending more time managing that darn AI than the AI is saving you.
Well, what I wanted was an AI that already knows the context, that has access to my notes and my tasks, and can communicate with me in real time without me having to babysit it. I've recently achieved this, and the key was picking the right tools for the job.
Now, the system has three layers, and each one does something very specific. So, Obsidian is the shared brain. This is where I keep all my notes, project context, daily journals, ideas. But here's the thing: my AI agent reads and writes to that same vault. We essentially share a second brain. It creates expanded memory support for, well, both of us, because at the end of the day, that's why you keep notes, right? Is to help you remember them.
Now, Linear, this is where structured work happens. When I need something done, not a quick question, but actual work. I create an issue in Linear and assign it to my agent. It picks it up, it does the work, and it updates the status, leaves a comment when it's done, just like human teammates would.
Slack is the real-time layer. Ad hoc questions, quick requests, proactive check-ins. My agent messages me when something needs attention, and I message it when I need something really fast. And tying all this together is OpenClaw. That's the platform that gives AI agents a persistent identity and connects it to all these tools.
Let me show you each one. Let's start with Obsidian, because this is really the foundation. I use a system called PARA, P-A-R-A, which stands for Projects, Areas, Resources, and Archive. So, every note has a home, and my AI agent, I call it Gibson, has full read and write access to this vault. So, when Gibson needs context on a project, it doesn't ask me; it reads the project file. And when it finishes work, it updates the project notes. When I write something in my daily notes, Gibson can reference it later.
Let me show you an example. Here's today's daily note. I captured some action items this morning. Gibson can see these. And here's the project context file. In theory, this is a shared document, but let's be honest, Gibson is really the one that maintains it. Every time it does work on a project, it updates the context file with what changed, what's next, and links to any artifacts that it created. I occasionally just read it if I need to catch up or try and figure something out.
Now, this is what makes it different from just chatting with an AI. The knowledge persists. Gibson is memory files, daily notes, and a long-term memory document that it curates over time. When I start a conversation with Gibson, it already knows what we've been working on. The vault is the source of truth for both of us.
And the way that I made that happen was through a file called agents.md. It's basically standing instructions. So, in that file, I told Gibson, "Every time you work on a project, update the context file. Every time you learn something important, write it down. Don't keep it in just a mental note; write it to a file." That one convention absolutely changed everything because now the agent builds its own institutional memory over time.
Now, Obsidian handles knowledge, but what about actually getting things done? That's where Linear comes in. If you haven't used Linear, it's a project management tool, kind of like Jira, but more modern and well-designed. I'm a design guy, after all. What makes this work is that Gibson is installed as an actual agent in Linear. It shows up as a team member. I can assign issues to it, mention it in comments, and it responds just like a human collaborator.
Let me show you a real workflow. Now, I created this issue, I assigned it to Gibson, and set the priority, added some context in the description, and Gibson picked it up, moved it to "in progress," did the work, and then moved the status to "in review" with a comment explaining what it did and linking to the actual artifact. This is the key difference between this and just asking an AI to go do something in chat. There's a paper trail, there are status updates, there's accountability, and I can see what's in progress, what's done, and what's blocked, just like managing a real team.
And that communication goes both ways, because if I leave a comment on an issue with additional direction, Gibson picks that up, too. It's a conversation, but a well-structured one. And I can always find the things when I need them. No more searching around for the right conversation thread in Claude or Cloud Code.
Now, the third piece is Slack. This is the ad hoc layer. The stuff that doesn't need a task or a note. I can message Gibson directly, and it responds in real time. Quick questions, brainstorming, asking it to look something up. But what makes it interesting is that Gibson is also proactive. It actually checks in with me a few times a day. If there's an important task, it'll flag it. If I have a deadline coming up, it gives me a heads up. In fact, at 5:00 p.m. every day, it sends me a reflection prompt to help me close out the day and think about what I accomplished.
It also responds in threads, so when you're working through something, the conversation stays organized. And because Gibson has access to Obsidian, it can reference my notes in any Slack conversation. I don't have to context switch. Slack is the lightweight layer. It's where the relationship feels most natural, like texting a coworker.
And here's what's interesting: the lines between these tools, they really start to blur. When Gibson finishes a Linear task, the notification comes through Slack. When I reply in Slack with feedback, Gibson updates the Linear sheet. I don't think about what tool I'm in anymore; I'm just communicating, and Gibson is routing the work to the right place. The surfaces overlap, and that's kind of the point.
So, here's where it all comes together. Each tool is useful on its own, but the magic is that they're connected through one agent with persistent memory. Let me walk you through the real loop here. I created an issue in Linear, and this one is to write the script for the next YouTube video. I assign it to Gibson. Gibson picks it up, checks Obsidian for context on my channel, things like past video performance, brand guidelines, what's worked before, maybe the next topic that's queued up, and it writes a draft script, saves it into a project folder in Obsidian, updates the Linear issue to "this is now in review," and sends me a Slack message that the script draft is ready. Here's the link. I open Obsidian, I read the draft. I can leave some notes right in the document. Gibson sees those notes and iterates. The whole thing happens across three tools, but it feels like one workflow.
That's the system. Obsidian is the brain. Linear is the structure. Slack is the pulse. And OpenClaw is what makes an AI agent that actually lives in your workflow instead of sitting in a chat window waiting to be prompted.
Now, if you want to set this up for yourself, I put the whole setup guide together. Every tool, every config file, step-by-step instructions. All you have to do is drop a comment below saying "send me the guide," and I'll reply with the link. Seriously, comment below, and I'll make sure that you get it. I'll also link to OpenClaw in the description. That's the platform that makes the entire agent layer possible.
Also, I encourage you, if you found this useful, please do subscribe. I'm going to continue building on this system and sharing what I learned. In fact, the next video I'm going to show is how I've set up a team of specialized AI agents that Gibson can delegate work to. There's a coding agent, a writing agent, a research agent, even a creative director agent. They're all running in parallel, and they can tackle things together. It's really well. So, be sure to tune in for that next one.
And in the meantime, I really appreciate, I really appreciate you watching. Thanks again. See you soon.