What I'd Tell Myself Before Learning to Code

I spent a Saturday afternoon in 2022 staring at a function I had written — forty lines of nested if statements that checked whether a user's input was a valid email. It worked. It passed the three test cases I had invented. I was proud of it until a friend looked at it and typed a single regex on one line that did the same thing.

That moment captured everything wrong with how I was learning: I was writing code, but I wasn't learning to think in code. The gap between those two activities is where most beginners stall out, and nobody talks about it honestly. Everyone wants to sell you the first month. Nobody prepares you for the middle part — the six to twelve months where you can make things run but can't explain why they work, where imposter syndrome settles in like weather.

Here's what would have saved me months of thrashing if someone had been direct about it.

The first month will lie to you

There is a specific kind of high you get when your first program prints "Hello, World" or when your first webpage renders a styled heading. That feeling is real, and it is misleading. The early wins come fast because the problems are constrained: follow these steps, get this output. You're pattern-matching, not problem-solving.

I remember the crash vividly. Three weeks into learning JavaScript, I could follow any tutorial and reproduce the result. Then I closed the tutorial, opened a blank file, and tried to build a simple calculator from scratch. Nothing. I couldn't figure out how to structure the logic without someone holding my hand through each step. The syntax was in my head, but the thinking wasn't.

That gap — between recognizing code and producing code — is the first real wall. Experienced developers get stuck too, but they've built mental models for diagnosing problems. That intuition comes from hundreds of hours of writing bad code and fixing it. There is no shortcut through it.

The danger of the first month is that it teaches you a pace of learning that won't hold. You learn console.log in five minutes. You do not learn asynchronous programming in five minutes. If you expect the same velocity throughout, you'll interpret the slowdown as personal failure rather than the natural shape of the curve.

Depth beats FOMO (and framework-hopping will wreck you)

Tech Twitter (or whatever we're calling it now) is engineered to make you feel behind. A new framework drops every week. Some influencer declares that the tool you spent three months learning is dead. Resist the gravitational pull of novelty when you're still building fundamentals.

I fell into this trap hard. During my first year, I touched React, Vue, Svelte, and started a brief fling with Angular before retreating in confusion. The result? I was mediocre at four frameworks instead of competent at one. Every switch reset my mental clock. I'd spend two weeks learning the paradigm, start feeling productive, see a shiny new thing, and jump ship.

The accumulated knowledge from any single framework never compounded because I never stayed long enough. When I finally committed to React and stuck with it for six months straight — building projects, reading source code, understanding the component lifecycle at a level deeper than "it re-renders when state changes" — everything accelerated. Depth in one ecosystem teaches you transferable concepts. Breadth across five ecosystems teaches you setup commands.

Don't worry about knowing everything. Know one thing well, and the rest becomes learnable.

Tutorial hell is the cousin of framework-hopping. Both feel productive. Both are avoidance strategies. Following a tutorial feels like learning, but the instructor is doing the hard cognitive work — deciding what to build, how to structure it, which problems to solve first. You're typing along. The moment you close the tutorial, you're back to the blank file.

The fix is uncomfortable: build something before you're ready. Pick a small project you care about — a personal site, a tool that scratches an itch — and fumble through it. The fumbling is the learning. That's how I ended up building this blog from scratch with MDX, and I learned more from that one project than from the previous dozen tutorials combined.

Motivation is a spark, systems are the engine

I've never met a self-taught developer who was consistently motivated for two years straight. Motivation fluctuates. You'll have weeks where you code for four hours after work and weeks where opening VS Code feels like pulling teeth. The people who make it through aren't more motivated — they have better systems.

Here's what worked for me when the initial excitement burned off:

The "no fast track" promise bears repeating. Bootcamps accelerate by providing structure and deadlines, but they do not compress the hours required. You cannot shortcut pattern recognition. Be suspicious of anyone promising a six-figure salary after eight weeks of study — they're selling a fantasy, not a trajectory. The realistic timeline for going from zero to employable is 12 to 24 months of consistent, deliberate work. Some people are faster; most aren't.

You don't need math (probably)

This myth stops more people from starting than any other. The belief that programming requires advanced mathematics is wrong for the vast majority of the industry.

Some domains demand it — machine learning, computer graphics, quantitative finance, cryptography. Most web development, API design, tooling, and automation rely on logic and problem decomposition, not linear algebra or calculus. If you can break a problem into ordered steps and search effectively when you're stuck, you have the core cognitive skills. I built full-stack applications for a year before I needed anything beyond basic arithmetic and boolean logic. Math can be learned later when your work demands it, and learning it with a concrete application in mind makes it stick faster than abstract coursework ever did.

Code reviews will teach you faster than any course

The single biggest accelerator in my learning wasn't a course, a book, or a YouTube playlist. It was the first time someone tore apart my pull request.

I had submitted what I thought was clean code — a React component for a side project I was building with a friend. He left fourteen comments. Fourteen. He pointed out that I was mutating state directly, that my useEffect had a missing dependency (and explained why that mattered), that I had hardcoded values that should be constants, and that my variable names read like someone naming pets rather than describing data.

I was embarrassed for about an hour. Then I fixed everything, re-read the comments, and realized I had learned more from that single review than from the previous month of solo study.

Code review exposes blind spots you don't know you have. When you're learning alone, you develop habits — some good, some terrible — and without external feedback, the terrible ones calcify. Getting reviewed forces you to articulate why you made a choice, not just what you did. And reviewing other people's code, even when you're new, trains your ability to read unfamiliar codebases — a skill you'll use every day professionally.

The "no experience" loop is solvable

"We need someone with experience." "How do I get experience without a job?" This loop sounds inescapable, and it isn't.

Companies hire for demonstrated ability, not exclusively for employment history. The gap between "I have no professional experience" and "I have proof that I can build things" is closeable without anyone's permission. Build projects that solve real problems — not todo apps, but tools you'd use yourself. Contribute to open source; even documentation fixes and bug reports count because they show you can navigate a real codebase, follow contribution guidelines, and communicate with maintainers.

Join hackathons — the time pressure forces you to ship, and the team experience mirrors real work. When I built and documented my terminal-first dev setup, the process of writing about my tooling choices taught me more about those tools than months of passive use. Document what you build, explain what you learned, and put it where people can find it. A portfolio with three well-documented projects beats a resume with ten buzzwords.

AI is a power tool, not a replacement for understanding

This section didn't exist two years ago. Now it's mandatory.

GitHub Copilot and ChatGPT have fundamentally changed what it feels like to learn to code. You can describe a problem in English and get working code back in seconds. This is extraordinary. It is also a trap if you don't use it carefully.

I've watched beginners (and caught myself) copy-pasting AI-generated code without understanding what it does. It runs. The tests pass. You move on. Then something breaks, and you're staring at code you cannot debug because you never understood it in the first place. This is a new flavor of tutorial hell — except the tutorial is infinite and personalized, which makes it even harder to recognize.

Copy-pasting AI output without understanding it is tutorial hell with better autocomplete.

Here's the line I try to hold: use AI to get unstuck, not to avoid thinking. When I'm wrestling with a regex pattern or can't remember the exact syntax for a TypeScript utility type, Copilot saves me a trip to the docs. Useful. When I ask ChatGPT to "build me an authentication system" and drop the result into my project — that's outsourcing the learning I'm supposed to be doing.

The distinction matters because the goal isn't shipping code. The goal is becoming someone who can ship code without the crutch. AI makes the floor higher (you can produce working software faster) but it doesn't raise the ceiling unless you understand the output well enough to modify, debug, and extend it.

The Dunning-Kruger curve is real, and the valley is where growth lives

This graph captures the learning arc better than any advice thread I've read.

You start overconfident — "I built a website in a weekend, how hard can this be?" Then you hit real problems: asynchronous code that behaves in ways you can't predict, state management that spirals, deployment pipelines that break silently. Confidence craters. You enter the valley where you know enough to see how much you don't know, and it feels worse than the beginning because at least the beginning had momentum.

The valley is where most people quit. It's also where the actual learning happens, because you're finally encountering problems complex enough to force deep understanding. The inflection point comes when debugging stops feeling like magic and starts feeling like detective work — when you read an error message and immediately know which file to open, which assumption to question, which log to check.

You don't cross that threshold by watching someone else debug. You cross it by debugging your own broken code at 11 PM, tracing a null reference through four function calls, and finally understanding why your data was undefined. That specific frustration is the learning. Every resolved bug deposits a small amount of pattern recognition that compounds over years.

The middle part — that stretch I mentioned at the top, where you can write code that runs but can't explain why — doesn't last forever. It feels permanent while you're in it. It isn't. The developers you admire went through it. They didn't have a secret; they had time and stubbornness.

If you're in the valley right now, the only thing that separates you from the people who made it to the other side is that they didn't stop. Not because they were more talented or more motivated on any given Tuesday, but because they built systems that kept them writing code on the days when they didn't want to. The inflection point is coming. You won't recognize it when it arrives — you'll only notice it months later when you realize that the problems that used to paralyze you now feel routine.

That's the compounding. That's the payoff. Keep going.