惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

月光博客
月光博客
Stack Overflow Blog
Stack Overflow Blog
L
LangChain Blog
Jina AI
Jina AI
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
雷峰网
雷峰网
T
Tailwind CSS Blog
MongoDB | Blog
MongoDB | Blog
博客园 - 【当耐特】
博客园 - 聂微东
V
Visual Studio Blog
博客园_首页
Engineering at Meta
Engineering at Meta
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
The Cloudflare Blog
人人都是产品经理
人人都是产品经理
Apple Machine Learning Research
Apple Machine Learning Research
阮一峰的网络日志
阮一峰的网络日志
Microsoft Security Blog
Microsoft Security Blog
GbyAI
GbyAI
F
Fortinet All Blogs
C
Check Point Blog
罗磊的独立博客
H
Hackread – Cybersecurity News, Data Breaches, AI and More

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant
Why I’m Still Learning to Code Even With AI
Juan Carlos · 2026-05-11 · via DEV Community

A while ago, I caught myself wondering:

"Am I too late?"

Not because I started coding late.

But because every week there seems to be a new AI demo building apps, writing features, fixing bugs, and explaining code faster than I can even think through the problem.

When you’re still learning, that can get in your head.

You start asking yourself:

If AI can already do this, what exactly am I trying to learn?

I’ve been there.

I’m still early in my journey. I’m building projects, learning Laravel, breaking things, fixing them, and slowly getting better at understanding how software actually comes together.

Lately I’ve been working on a team task management app.

At first it felt straightforward.

A team has members. Owner can invite other members. Tasks can be created and assigned.

Simple.

Until it isn’t.

While working on the invitation feature, I started moving logic out of controllers and into service classes. Then I added form requests. Then policies.

And suddenly I realized something that felt bigger than the feature itself.

**The hard part wasn’t writing code.

The hard part was deciding what code should exist.**

That changed how I look at AI.

AI can absolutely generate code.

Sometimes it gives surprisingly solid code.

But while building that feature, AI wasn’t the one deciding things like:

  • Should this logic live in the controller or a service?
  • What happens if someone sends the same invitation twice?
  • Who should be allowed to invite people?
  • What happens when someone leaves a team?
  • How do I structure this so future me won’t hate present me?

Those decisions were still mine.

And honestly, that’s the part I actually want to learn.

When I first started coding, I treated code like a puzzle.

I kept adding pieces until the feature worked.

Once it worked, I usually didn’t want to touch it again, because I knew changing one thing might break three others.

Lately, I’ve been trying to move away from that.

I’ve started paying more attention to system design—not in the “big tech interview” way, but in the practical way.

I want to be more intentional.

Not just:

“How do I make this work?”

But also:

“Why should it work this way?”

That shift has made coding feel different.

And honestly, more interesting.

Because the more I learn, the more I realize coding isn’t mainly about syntax.

It’s about tradeoffs.

It’s about structure.

It’s about deciding what belongs where.

It’s about understanding the problem well enough to build something that won’t immediately collapse the moment you add a new feature.

That’s why AI doesn’t discourage me anymore.

If anything, it clarified what actually matters.

AI can help me write code faster.

It can explain concepts.

It can help me compare approaches.

It can even point out mistakes I didn’t notice.

But it still doesn’t replace judgment.

It doesn’t automatically know the tradeoffs inside my project.

It doesn’t know the shortcuts I took last week.

It doesn’t know what future me will struggle to maintain.

That part still comes from learning.

So if you’re just starting—or thinking about starting—and AI makes you wonder if coding is still worth learning, here’s my honest take:

This might actually be one of the best times to start.

Because now you can get unstuck faster.

You can ask better questions.

You can explore more ideas in less time.

But the real learning still happens when you decide:

  • what to build
  • how to structure it
  • which tradeoffs you accept
  • what code stays
  • and what code needs to be deleted

That part still belongs to you.

I’m still learning too.

Still writing code I later refactor.

Still realizing how much I don’t know.

Still trying to go from “it works” to “this makes sense.”

And right now, that feels like progress.

AI didn’t make me want to stop learning.

It made me realize something more important:

The value isn’t just in writing code.
The value is in understanding why that code should exist at all.


Note: I use AI while learning, not to skip the work, but to understand faster, explore alternatives, and challenge my thinking.