At first, I was caught up in the hype too.
Like many people, I imagined that I could write a prompt, generate something, make money from it — maybe even get rich overnight.
But before I started, I asked myself a simple question:
If writing software, building applications, and coding can make you rich, shouldn't every software developer be rich?
Looking at reality, that clearly wasn't the case.
So I put that thought aside.
My real goal was simply to see what I could actually do.
I started experimenting.
One prompt.
The result: a pile of code that didn't work.
Another prompt.
Same result.
Two, three, four attempts...
Nothing changed.
That was my first important realization:
AI wasn't perfect.
More importantly, I couldn't believe everything it told me.
It could tell me that it had built something that worked.
But in reality, it didn't.
My second realization was more personal:
Simply prompting AI to build something wasn't possible for me — at least not at that stage.
I had two choices.
Give up.
Or figure out how to make it possible.
I chose to learn.
But I didn't take the traditional route.
I didn't want to spend months watching software engineering courses, programming lessons, and AI courses.
I needed a different way of learning.
So I started working like a bee.
A bee doesn't carry the whole flower back to the hive.
It takes what it needs.
Then it goes back to the hive.
I started doing the same thing.
Whenever I wanted to build something, I focused on what I needed to learn at that moment.
I found the information.
I read it.
I tried it.
If it didn't work, I researched again.
Then I tried again.
I didn't learn in order to build.
I learned while building.
And over time, it started working.
I learned GitHub.
Branches.
Commits.
Repositories.
Then the terminology of software development.
Then I started understanding the AI tools and coding agents I was using.
I didn't try to learn everything at once.
I learned whatever I needed to solve the problem in front of me.
And eventually, I started getting my first results.
They were simple things.
But they mattered.
Because I was no longer just writing a prompt and waiting for AI to produce something.
I was building something, trying to understand what it had actually done, investigating why it failed, and trying again.
Then I noticed something else.
The more I used AI and coding agents, the more I started seeing their problems.
Where an agent failed.
Why it kept repeating the same mistake.
What information it forgot.
And why, when I switched to another agent, I had to explain everything all over again.
At some point, I thought:
Maybe instead of constantly fixing agents' problems, I could build tools to solve those problems.
One of the first projects that came out of this was LEVH.
When I was using multiple AI agents, I kept running into a very simple but frustrating problem:
Every agent started each session from zero.
I could spend days giving one agent context about a project.
We would make decisions.
Define rules.
Solve problems.
Then, because of context limits or simply because I needed to use another tool, I would switch to another agent.
And start all over again.
Explain the same decisions.
The same rules.
The same problems.
Everything.
That started to feel ridiculous.
Instead of waiting for agents to become smarter, I asked:
Why can't they share a memory?
So I started building LEVH.
Today, LEVH is a local-first memory layer that gives different AI tools and agents on the same machine access to persistent shared memory.
I still use it myself.
And I'm still developing it.
[LEVH — GitHub repository] [https://github.com/ali-ulu/levh]
Feel free to use it, explore it, and contribute if you find it useful.
But LEVH taught me something else:
**Sometimes the real value of working with AI doesn't come from seeing how good its answers are.
It comes from noticing where it fails.**
The more problems I saw in agents, the more small tools I started building to solve them.
And eventually, that led me to another question:
What if we could control not only an agent's memory, but also what it does?
That question led me to HUQAN.
And that's where the next part of the story begins.
I have not failed. I've just found 10,000 ways that won't work -Edison