What Nobody Tells You About Learning to Code in the Age of AI

Hiya Lukka — Sat, 06 Jun 2026 06:28:32 +0000

Six months ago, I sat down with a YouTube playlist, a blank notebook, and one goal: learn Python. What I did not expect was how hard it would be, not the Python itself, but figuring out how to actually learn it.

I started with a YouTube playlist. Simple enough. Except nobody tells you what to do after you watch a video. Do you rewatch it? Take notes? Jump straight to code? I had no system. I'd watch a concept, feel like I understood it, open VS Code, and stare at a blank file.

That's when I realized I had fallen into passive learning. And passive learning in the age of AI is a particularly dangerous trap, because it's so easy to confuse activity with progress. I could watch a video, feel good. I could ask Claude to explain a concept, feel good. I could even ask AI to write code, read it, nod along, and feel like I'd learned something. I hadn't. I'd just consumed. There's a difference.

The real moment of honesty came when I was stuck on a coding problem. My instinct, everyone's instinct now is to open ChatGPT or Claude immediately. And I knew, sitting there with the cursor blinking, that if I did that every single time I got stuck, I was building nothing. My brain would never develop the muscle of working through problems. I would be someone who can prompt AI to code, not someone who can think in code. And in a world where AI can already write decent code, the person who can't think independently isn't valuable. They're replaceable.

So I had to build a system that forced me to actually learn. After a lot of trial and failure, I landed on a 5-phase checklist that I wrote out by hand and kept next to my laptop.

Phase 1: is what I call First Contact — watch one focused video, then write a summary purely from memory, then discuss it with an LLM not to get answers but to pressure-test what I thought I understood.

Phase 2: is Deep Understanding — read a written source, write proper notes, map the concept visually, and list every edge case and exception I can find.

Phase 3: is Hands-On Practice — write 3 to 5 small isolated examples from scratch, then intentionally break the code, because understanding why something breaks teaches you more than getting it right.

Phase 4: is the Stress Test — ask an LLM to quiz me until I break, teach the concept back to it, and ask "what would an interviewer ask about this?"

Phase 5: is Cement and Connect — use the concept in an active project, write one thing I'd tell a fellow learner about it, and schedule a spaced review.

The rule I set for myself throughout all of this: AI is a tool for testing and pressure, not a crutch for getting unstuck. When I'm stuck on a problem, I sit with it. I struggle. I come back. Only after genuine effort do I use AI — and even then, I ask it to guide me, not solve it for me. It wasn't comfortable. It still isn't. But six months in, I'm now working through Python libraries and starting ML theory, and for the first time it feels like a foundation, not a facade.

If you're somewhere in the early weeks of learning to code, the question isn't which resources to use. It's whether you're actually building something in your brain, or just moving content through your eyes. The difference matters more now than it ever did before.

I watched a machine do the impossible. Now I'm learning to build one.

Hiya Lukka — Sat, 30 May 2026 05:53:00 +0000

There is a specific kind of silence that happens when you watch something that breaks your understanding of what is possible.

Not loud surprise. Not excitement.
Silence. The kind where your brain is working so hard to process what just happened that everything else goes quiet.

I felt that silence watching a machine play a board game.

Not because the machine won. Because of how it won, by making a move that no human player had ever made, in thousands of years of the game being played. A move the world champion later said he found beautiful. A move that came from no human tradition, no human teacher, no human intuition.

It came from a model that had learned to find patterns in complexity that the human brain cannot hold simultaneously.

That was the night I understood what ML engineering actually is.
And that was the night I decided what I want to do with my life.

What nobody tells you about ML engineering:
The popular version of AI right now is tools. Chatbots. Productivity apps. Automated workflows. And those things are real and useful.

But they are not what stopped me cold that night.

What stopped me was this: a machine that nobody explicitly programmed to win, that learned entirely through playing itself, millions of times, with no human data, no human guidance — found a solution that centuries of human expertise had never found.

That is not automation. That is not a smarter search engine.

That is a new kind of intelligence finding structure in problems that were structurally unreachable before.

ML engineering is the first discipline in human history where a person with the right model and the right problem can reach solutions that no human could have arrived at alone. Not improve on existing solutions. Not iterate faster. Reach solutions that were simply not reachable before.

And the people building those models are not working on productivity apps.

They are working on protein folding. Drug discovery. Climate systems. Problems that have broken every generation of scientists before ours, not because those scientists weren’t brilliant, but because the tools didn’t exist yet.

I want to be one of those people.

Where I actually am right now:
I want to be honest about this part. I am a computer engineering student. I have no internship. No research publication. No industry mentor. I am on Lecture 0 of CS50. I am working through NumPy. I am in the early weeks of ML theory, where everything is still matrices and probability and gradient descent and nothing yet feels like the thing I watched in that documentary.

Before I started this path, I built ClassFlow — an automated timetable generator that handles faculty constraints, room allocation, semester structures, and conflict detection for college departments. It works. It solves a real problem that colleges across India still solve manually in Excel.

But it does not learn. It cannot improve from its own mistakes. It has a ceiling, and that ceiling is the exact reason I am here.

My mornings now start with Python before anything else. Not after breakfast. Not after checking my phone. Before. Then CS50. Then ML theory. Then I watch people who are actually building in this space — not for motivation, but to learn how people inside this field think about problems.

Some mornings the gap between Lecture 0 and where I want to be feels so large it is almost funny.

I sit with that feeling. I do not try to resolve it with motivation or affirmations or a highlights reel.

I just open the laptop and keep going.

et is Google DeepMind. Or somewhere doing work at that level. Somewhere the question on the table is not how to make a product more engaging — but how to make a model capable of something no model has done before.

The students who start before they feel ready, who stay in the chair when the gap feels too large — are the ones who actually arrive.

Demis Hassabis was once sitting in a room full of the most brilliant minds he had ever seen, and he had a thought that most people would have called arrogance: what if we took all of this and aimed it at the problems that actually matter?

He was not yet building DeepMind. He was just in the room, with the idea, not yet knowing how to execute it.

I think about that a lot.

Every ML engineer who ever built something that mattered was once exactly where I am, at the beginning, with the theory still abstract and the application still distant. The difference between the ones who got there and the ones who didn’t was not talent. It was whether they stayed in the chair.

I’m staying in the chair.

If you are somewhere in the middle of this path, what is the one resource that genuinely changed how you think about ML? I am building my stack right now, and I want to know what actually moved the needle for people further ahead.

DEV Community: Hiya Lukka

What Nobody Tells You About Learning to Code in the Age of AI

I watched a machine do the impossible. Now I'm learning to build one.