One Article, Three Depths: What If We Found a Common Language for the Internet?
How to write for curious beginners, busy readers, and developers who want to see the actual code, without turning one article into three different articles.
I think I accidentally discovered something while writing about AI.
And no, I didn't discover a new model, invent a better neural network, or teach an AI agent to pay my bills. Sadly. 😂
I was writing an article about what I call the Third Layer of AI: the possibility that AI systems might eventually do more than improve existing models. They might create entirely different kinds of systems to solve problems their current architecture cannot handle.
It's a pretty deep topic.
Research papers. Algorithms. Experiments. Benchmarks. Code. A lot of things that make perfect sense to developers, but can make an ordinary reader feel like they've accidentally opened a university exam.
So I tried something simple.
At the beginning of the article, I added a quick breakdown of the main idea. Something a reader could understand in seconds, before deciding whether to spend another hour exploring the details.
And then I stopped and thought:
Wait a second. What if this is not just a better introduction? What if this is a better way to write articles in general?
Let me explain.
The problem isn't always the length. It's the entrance.
Imagine opening an article on your phone.
The title looks interesting. You tap it.
And immediately, you're staring at a wall of technical text.
Definitions. Acronyms. Long paragraphs. References to papers you've never read.
Your brain has approximately one question:
Do I really want to work this hard right now?
Sometimes the answer is yes. Often, it's no.
You close the tab.
Not necessarily because the article is bad. Not because you're stupid or lazy. Maybe you have five minutes on the bus. Maybe you're curious about AI but don't know the terminology. Maybe you've spent eight hours debugging production code and the last thing you want is another wall of code.
Or maybe you're a developer who actually wants the technical details but needs to know whether the article contains anything worth your time.
Different people. Different needs. Same internet.
And here's the interesting part: we often try to solve this problem by writing different content for different audiences.
One simplified article for beginners.
One technical article for developers.
One short social media post to get people's attention.
Three pieces of content. Three different explanations. Sometimes three different versions of the same idea.
What if we could do it with one article instead?
Meet the 3-in-1 writing model
The idea is straightforward: build one article in three layers of depth.
Not three separate articles glued together. Not a beginner section that talks down to people, followed by an expert section that nobody can understand.
One idea, with three possible entry points.
Layer 1: The first few seconds
Give me the idea before asking for my time.
The reader should quickly understand what the article is about, why it matters, and what makes it interesting.
No long introduction explaining how important the topic is.
No five paragraphs of terminology before reaching the actual point.
Give me a question, a surprising example, a relatable problem, or a simple analogy.
For example, when talking about AI systems creating other AI systems, you could start with this:
«What if the next big step in AI isn't building a smarter model, but building a system that can invent a completely different kind of system when the old one isn't good enough?»
You don't need to know what an evolutionary algorithm is to understand that question.
You don't need to know how a neural network is trained.
You just need to be curious.
And if you're not curious about that particular question, that's fine too. At least you discovered it quickly instead of being forced through ten paragraphs before finding out what the article was trying to say.
The first layer isn't about making people consume content faster.
It's about helping them decide whether they want to go deeper.
Layer 2: Explain it like we're talking over coffee
Now imagine the reader says:
Okay, that's interesting. But what does it actually mean?
This is where we stop talking like a scientific paper and start talking like human beings.
Use stories. Examples. Analogies. A little humor.
Explain the problem before introducing the terminology.
Suppose your AI system keeps trying to solve a problem with tools that aren't suitable for it.
You could spend a page explaining an architecture for generating and evaluating specialized agents.
Or you could say:
Imagine hiring one employee to do every job in your company. Accounting, security, materials research, customer support, and fixing the coffee machine.
At some point, you might realize the solution isn't to make that employee work harder. Maybe you need different specialists.
Now imagine your employee can help design those specialists, test whether they're any good, and reject the ones that fail.
Congratulations. You've just introduced a surprisingly complex idea without requiring the reader to memorize six new terms.
The technical explanation still matters. We'll get to it.
But the reader now understands the problem the technology is trying to solve.
That's the point of the second layer.
We're not simplifying the truth. We're simplifying the route to understanding it.
And yes, we can have a little fun along the way. Technical writing doesn't have to sound like someone has confiscated the author's sense of humor.
Layer 3: Show me the actual thing
Now comes the part developers care about.
How does it work?
What architecture does it use?
What did you actually test?
Where is the code?
What were the results?
What failed?
Can I reproduce the experiment?
This is where you put the implementation details, research papers, benchmark results, test methodology, limitations, and all the uncomfortable questions that separate engineering from marketing.
Because a great analogy is not evidence.
A convincing story doesn't prove that a system works.
And a heading that says "self-improving AI" doesn't magically make an AI system capable of recursively improving itself.
If we're discussing an experimental approach, say so. If a benchmark covers ten cases, don't make it sound like proof of universal reliability. If something works only in simulation, explain that boundary.
The third layer is where the idea faces reality.
And here's the crucial part: the first two layers must not promise more than the third layer can demonstrate.
Otherwise, we've just invented a very friendly way to mislead people.
Here's where the three layers become interesting
Think about three readers opening the same article.
Reader A is fifteen years old, scrolling on a phone. They have a few seconds to decide whether the topic interests them.
They read the opening, understand the central idea, and learn something.
Maybe they leave after twenty seconds.
That's okay. They still got something useful.
Reader B is interested in AI agents but doesn't write code. They understand the analogy, follow the explanation, and spend ten minutes exploring the problem.
They now understand something they might previously have considered too technical.
Reader C is a developer.
They understand the introduction, skim the explanation, and jump straight to the implementation, tests, and limitations. They want evidence, not another inspirational speech about the future of AI.
All three readers encounter the same central idea.
None of them needs a separate article written for their supposed intelligence level.
They simply choose how deep they want to go.
That's what I find interesting about this model.
The layers aren't a ranking of readers. They're different depths of engagement with the same idea.
A beginner isn't a failed expert.
An expert doesn't need to read every introductory paragraph.
And a person who spends only thirty seconds on an article isn't necessarily incapable of understanding it. Maybe thirty seconds was all the time they had.
Let people choose their depth.
Is this just clickbait with extra steps?
Fair question.
We already have headlines, summaries, bullet points, TL;DR sections, and short videos explaining long videos.
So what's different?
Nothing here is completely new on its own.
The interesting part is the combination and the intention behind it.
A typical summary tells you what the article contains.
A good first layer should also give you a meaningful understanding of the central idea.
A typical simplified explanation removes technical complexity.
The second layer should preserve the actual problem while making it understandable.
A typical technical appendix stores the details for specialists.
The third layer should give readers enough evidence to evaluate the claims, not merely decorate them with impressive terminology.
Together, the three layers create a path from curiosity to understanding to verification.
And that path works in both directions. Someone can start with a simple explanation and go deeper. An experienced developer can scan the article, find the evidence, and come back to the larger argument.
This isn't about hiding complexity from beginners.
It's about making complexity accessible without hiding it.
A common language doesn't mean everyone speaks the same language
This is probably my favorite part of the whole idea.
When you explain a technical problem to someone outside your field, you face a choice.
You can demand that they learn your vocabulary before you explain why the problem matters.
Or you can start with something they already understand and build from there.
Neither approach makes the technical vocabulary unnecessary. You still need precise language when precision matters.
But you don't need to make technical language the entrance fee for curiosity.
Think about how many potentially interesting ideas people never explore because the first explanation assumes too much background knowledge.
A person might understand why an autonomous agent needs to know when to stop, even if they've never written an agent.
They might understand why memory needs to distinguish useful information from noise, even if they don't know how embeddings or retrieval systems work.
They might understand why an AI system should be tested independently, even if they cannot read the test suite.
And if they become interested enough, they can learn those things.
The first explanation opens the door. It doesn't have to contain the entire building.
That's what I mean by a common language: not removing differences between people, but creating a shared starting point from which those differences don't prevent understanding.
The internet has a lot of content. It could use more accessible depth.
We know how to make content short.
We know how to make it entertaining.
We know how to make it technically detailed.
The harder challenge is combining those qualities without sacrificing honesty or depth.
A short article can be shallow.
A long article can be brilliant but difficult to enter.
A funny article can be memorable without teaching anything.
A technically impressive article can still fail to explain why anyone should care.
The 3-in-1 model is my attempt to bring these pieces together.
Start with a thought worth understanding.
Explain the idea in ordinary language.
Then show the evidence and let the reader inspect the machinery.
And don't force everyone to travel the whole distance.
One article. Three depths. One shared idea.
When the image tells a different story
Here's something I noticed while looking at the AI-generated illustration accompanying this article.
The agent appears to take the role of an ordinary developer, while the humans fade into the background.
But isn't this article about creating a shared language, rather than replacing one perspective with another?
The image may suggest something different from what the text is trying to say.
If we're looking for a common language, shouldn't we also learn to notice when the things we create say something we never intended?
I noticed this while looking at my own illustration. I'm not an AI researcher. I simply looked at it and thought:
Wait a minute. Something here doesn't quite fit.
That doesn't prove the illustration is objectively wrong, or that an AI agent can perceive three layers simultaneously. An image cannot establish what a system actually understands. That depends on its design, its inputs, and how it processes them.
But the mismatch raises a useful question.
We can ask AI to help us express an idea, yet the result may communicate something we never intended. The words can describe collaboration while the image quietly suggests replacement. The intention and the output are not always the same thing.
And noticing that difference doesn't require an advanced technical degree.
Sometimes, it begins with an ordinary person looking at something and asking a simple question.
Maybe that's part of building a common language, too: not just making ideas easier to understand, but learning to question whether the things we create actually communicate what we mean.
Let's be honest about what we don't know
I don't have evidence yet that this structure will automatically increase readership, retention, or meaningful discussion.
It would be easy to turn this into a grand claim about revolutionizing technical writing. I'm not going to do that.
It's a working idea, and the sensible next step is to use it on real articles and see what happens.
Do readers move beyond the opening? Do developers find the evidence they need? Do people with different backgrounds engage with the same idea? Or does the structure simply make articles easier to navigate without changing anything else?
Those are questions worth testing.
Also, when I say the first few seconds matter, I'm describing a practical writing challenge, not claiming that every human brain has a scientifically fixed fifteen-second attention span. People differ. Context matters. And attention is more complicated than a single dopamine explanation.
The goal isn't to manipulate readers into staying.
The goal is to make the value of continuing clear, while respecting their choice to stop.
There's a difference.
So, what if we tried writing this way?
Imagine a scientific article that doesn't make ordinary people feel excluded before they reach its central idea.
Imagine a beginner who can understand the problem, a curious reader who can follow the explanation, and a developer who can inspect the implementation, all starting from the same page.
Imagine not having to choose between an article that everyone can enter and an article that specialists can take seriously.
That's what I'd like to explore with this model.
Not shorter articles at any cost.
Not simplified science that removes the difficult bits.
Not technical documents disguised as casual conversation.
Just one piece of writing that lets different people discover the same idea at different depths.
Because the purpose of writing isn't to prove how much the author knows.
It's to help someone else understand something they didn't understand before.
And maybe, just maybe, the best technical article is not the one that makes every reader reach the final paragraph.
Maybe it's the one that gives every reader a meaningful place to begin.
Your turn
I'm curious how other developers approach this.
When you write about a complex technical subject, do you write separately for beginners and specialists? Or do you try to make the same article work for both?
And if you had to design a technical article for the entire internet, what would your structure look like?
I'm calling this 3-in-1 writing for now. It's a working name, not a proven formula.
Let's see whether it survives contact with actual readers. 😄
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