.png)
You know that feeling when you finish a really good book and think "That was brilliant" but then have absolutely no idea what to do with it?
I get this all the time. I'll read something that genuinely excites me, take decent notes, even feel like I properly understand the concepts. But then I close the book and I'm left staring at my notes thinking "Great. Now what?"
It's not that the ideas aren't useful. It's that there's this annoying gap between "I get this" and "I can actually do something with this."
Most of the time, those brilliant insights just sit in my notes. They become part of my theoretical knowledge bank, but they never quite make the jump to being something I can actually apply when I need them.
Until I accidentally discovered a way to bridge that gap that's so simple I almost didn't think it would work.
This isn't just a me problem—it's the curse of knowledge workers everywhere.
We're excellent at consuming information. We can read, highlight, distill, and organise with the best of them. But there's this invisible wall between understanding something and actually being able to use it when it matters.
You can read about negotiation tactics, but freeze up in your next salary conversation. You can understand project management frameworks perfectly, but still struggle to untangle the mess of your current project. You can grasp marketing principles intellectually, but stare blankly at your own content wondering how to apply them.
The problem isn't comprehension—we get the theory just fine.
Knowing what to do and knowing how to do it are completely different things. It's one thing to understand a framework in the abstract. It's entirely another to look at your specific situation and know exactly how to apply it, step by step.
Most of us are stuck in this gap between theory and practice, with a bunch of really good ideas that we just can't seem to put to work.
This is where I accidentally discovered something that changed how I approach learning entirely.
I'd just finished The Goal by Eliyahu Goldratt—brilliant book about finding bottlenecks in systems. Great story, loved the concepts, but when I tried to apply the theory of constraints to my own work, I was completely lost. I had pages of notes but no clue how to actually spot a bottleneck in my daily chaos.
So I did something that felt a bit lazy: I dumped all my notes into ChatGPT and created a "bottleneck spotter" GPT. Honestly, I was just trying to shortcut the whole process. I figured if I could describe a situation and have it tell me what the bottleneck was, I'd save myself the hassle of figuring it out.
But here's what I didn't expect: each time I used it, I found myself understanding the process and the concepts more. The more I saw AI working through the theory of constraints on my OWN situations, the more it started to click.
I started seeing the patterns and actually began to grasp how to spot bottlenecks myself.
I wasn't trying to learn from the AI—I was just trying to take a shortcut. But it turned out to be exactly what I needed to bridge that gap between theory and practice.
Once I realised what had happened, I started doing this with everything I wanted to learn. And it works ridiculously well.
When I read a good LinkedIn post on how to write great hooks, I whip up a GPT to help me apply it, when I read a great blog post about how to keep viewers engaged through a video I put it into a GPT and start applying it to my scripts.
It's not just a shortcut to the outcome.
If you pay attention it's a shortcut to real learning.
And I think it's this. When you are reading a book, watching a video or ingesting a social media post everything is still theory. But when you see it applied to your own examples it suddenly comes to life.
Let me walk you through how to turn your own notes into learning GPTs. The process is simpler than you'd think.
Step 1: Gather Your Notes
Take whatever you've been reading—book highlights, article notes, video transcripts, whatever. The messier the better, honestly. You don't need perfect summaries.
Step 2: Turn Notes Into Knowledge Context
Now you can structure your notes with purpose. I've set up a custom AI command in TANA that takes any pile of notes and turns them into something a GPT can actually work with.
This is the prompt I use to take my notes and turn them into Knowledge Context.
Step 2: Decide What You Want Your GPT To Do
Now figure out what role you want your GPT to play. Do you want it to analyze situations? Help you apply frameworks? Walk you through processes? Define the specific role and then write up instructions for your GPT.
Step 3: Create Your Learning GPT
Take that knowledge document and your custom instructions and create a new customGPT in Claude, ChatGPT (or even Tana where I do this).
Step 4: Start Practicing
Now comes the good part. Feed it your real situations and watch how it applies the concepts.
Each time you use your new GPTs, you'll understand the process a bit more.
This is the only way I want to do all learning now.
Here's what I didn't expect: this approach turns AI into the best study partner you've ever had.
It's like having someone who never gets tired of walking through examples with you, who can take any messy situation you throw at them and show you exactly how the concepts apply. No clean textbook scenarios—just your actual chaos.
I went from staring at brilliant notes wondering what to do with them, to actually implementing frameworks I'd been stuck on for months. That annoying gap between reading something useful and actually using it? Gone.
Whenever You're Ready To Build A Personal Knowledge Copilot That Actually Keeps up With Your Pace Of Work — Here Are Couple Of Ways I Can Help:
.png)
Every week, I send you one simple system, workflow, or mindset shift to help you ditch outdated PKM habits and build a smarter, AI-powered knowledge system that helps you get real work done—instead of endlessly managing your notes.
