Nobody has this figured out. I certainly don’t. But I think I’m closer than most, and what I’ve learned so far is worth sharing , not because I have the answers, but because the journey itself is the lesson.
I’ve been deep in the AI trenches for months now. Claude Code, OpenAI, Obsidian, OpenClaw, Telegram, WordPress, Brave Search, autonomous agents, command-line workflows, the whole thing. And if I’m being honest, this week I had to stop and reorganize everything. Not because something broke. Because I realized I was getting messy.
There’s a difference between messy and sloppy.
Messy is natural. Messy is what happens when you’re moving fast, trying things, iterating. Messy means you’re in it. You’ve got notes everywhere, half-finished experiments, three different tools doing overlapping things. That’s fine. That’s the process.
Sloppy is when you stop caring about the mess. When you keep building on top of a shaky foundation because stopping feels like losing momentum. When you burn through API tokens because you haven’t figured out the right workflow yet but you keep going anyway. When you know the system isn’t working but you keep pushing forward because admitting that means backtracking.
I was getting sloppy.
And the thing about sloppiness is it compounds. One messy folder becomes five. One unorganized workflow becomes a dozen. You wake up one morning and realize you can’t find anything, you don’t know what the AI actually did versus what you did, and you’re spending more time managing chaos than doing the work you set out to do.
So I stopped. Reset. Started over in the places that needed it.
The Organizational Overhead Is Real
Here’s what I think most people don’t talk about with AI: the organizational overhead is real.
It’s not just “learn the tools.” It’s:
- How do you structure your notes so the AI actually has useful context?
- How do you separate what the AI writes from what you write?
- How do you hand off tasks to agents without burning through your budget?
- How do you keep your personal life, your day job, your side projects, and your experiments from bleeding into each other?
None of this is hard. None of it is easy either. It’s just work. The kind of work nobody posts about on Twitter because it’s not sexy. It’s organizing folders. It’s writing documentation for yourself. It’s sitting down on a Wednesday morning and restructuring your entire Obsidian vault because you realized the way you had it set up three weeks ago doesn’t work anymore.
I spent hours this week reorganizing my workspace. Moving files. Creating clear boundaries between Christopher’s output (my content AI) and Sil’s output (my research AI). Building folder structures that make sense not just today but three months from now when I need to find something.
Why does this matter? Because when you’re running AI agents, context is everything. If the AI can’t find your notes, it can’t use them. If your folder structure is chaos, the AI inherits that chaos. If you haven’t documented how something works, the AI will guess. And AI guesses, even good ones, burn tokens.
This is AI literacy. Not prompt engineering. Not knowing which model is best. It’s the boring, unsexy work of building systems that actually hold up under pressure.
Token Economics and Reality Checks
I’ve been running a crew of AI agents through OpenClaw. Think of it like managing a team, except the team lives in the cloud and runs on tokens. And I love it. I learned more about orchestration, delegation, and workflow design in the last month than I did in years of managing human teams.
But I was burning tokens like they were free. They’re not.
A few realizations hit me this week:
First: Most of what I was having the agents do didn’t need to be automated. It needed to be done once, properly, with clear documentation. Then I could run it myself at the command line in 30 seconds instead of spinning up an agent that costs 20 cents every time it runs.
Second: The value wasn’t always in the output. Sometimes the value was in forcing myself to think through the problem clearly enough to brief an agent. If I couldn’t explain it to the AI in a way that produced good results, I probably didn’t understand it well enough myself.
Third: Agents are incredible for iteration. Less incredible for one-off tasks. I was using them backwards.
So this week I stepped back. Reset. Started thinking about what actually needs an agent running versus what I can do myself. Turns out, a lot of the value wasn’t in the agents running 24/7. It was in the thinking I did while setting them up. The architecture. The guardrails. The structure.
The agents taught me how to think about work differently. And now I can apply that thinking with or without them running.
That’s not a failure. That’s learning.
The Power Forward Role
I think the thing that keeps me going is that I know my role. I’m not the cutting-edge AI researcher. I’m not building foundation models. I’m not even the best prompt engineer.
I’m the power forward.
If you watched 90s basketball, you know what I mean. Charles Barkley, Charles Oakley, Dennis Rodman. These guys weren’t the flashy point guards or the dominant centers. They were the ones chasing loose balls, boxing out, taking charges, doing the dirty work that made great teams great. Never the most celebrated, but always the most essential.
That’s how I feel about my role in this space. I’m the bridge between the deep technical people and everyone else. I take complicated things and make them make sense. I get in the trenches and figure it out so I can tell you what actually works versus what just sounds good on a podcast.
The deep technical people are building the tools. The researchers are pushing the boundaries. The early adopters are trying everything. I’m the one a few steps behind them, figuring out what’s actually practical, what’s worth the effort, what translates to real work for real people.
I’m comfortable there. That’s where I do my best work. Not on the cutting edge, but close enough to see it. Close enough to understand it. Far enough back to have perspective on what matters and what’s just noise.
And right now, what actually works is: slow down, organize, then go.
What Reorganization Actually Looks Like
So what did I actually do this week?
I cleaned up my Obsidian vault. Moved files into logical folders. Created clear separation between client work, personal projects, agent output, and research notes. Built templates for recurring tasks so I’m not starting from scratch every time.
I documented my workflows. Not for the AI. For me. So when I come back to something in three weeks I remember why I set it up that way and what I was trying to accomplish.
I pruned my agent roster. Some agents I was running every day. Some I hadn’t touched in two weeks but they were still sitting there in the config taking up mental space. I archived the ones I’m not using. Tightened the instructions on the ones I am.
I stopped chasing every new tool. There’s a new AI product every single day. Most of them are solving problems I don’t have. I made a list of what I actually need, cross-referenced it with what I’m actually using, and deleted everything else from my mental stack.
None of this is revolutionary. It’s maintenance. It’s the kind of thing you do when you realize you’ve been moving so fast you forgot to check if you’re still heading in the right direction.
And here’s the thing: it felt good. Slowing down didn’t feel like losing momentum. It felt like building a foundation that can actually support what I’m trying to do.
For Everyone Feeling Overwhelmed
So if you’re feeling overwhelmed by all of this, the tools, the models, the workflows, the constant stream of new things, I want you to know: same. Everyone is. The people who look like they have it figured out are just better at hiding the mess.
The goal isn’t to eliminate the mess. The goal is to not be sloppy about it.
Take a morning to reorganize. Reset your expectations. Figure out what’s actually working and what you’re just doing because it feels productive. Build the boring stuff. The folder structures, the documentation, the workflows. Because that’s what everything else sits on top of.
Cut the things that aren’t working. Simplify the things that are. Stop running agents just because you can and start running them because they solve a real problem better than you could solve it yourself.
It’s not glamorous. But it’s how you actually make progress.
Messy, not sloppy. That’s the standard.
