I know some people still treat AI like a "smart autocomplete" — you write a line and it finishes the rest. But the truth is, AI in web development passed that stage a long time ago. It's now a true partner in the development process: it understands context, suggests architectural solutions, writes complete code, and works with you to solve problems. In this article, I'll share how AI has changed my workflow as a web developer, from the early GitHub Copilot days to the tools we use today.
The Assistant Phase (2022-2023) was the first appearance of GitHub Copilot. The idea was simple: you write a comment or the beginning of a function and AI completes the code. It wasn't much, but it genuinely saved time. I'd write a comment like "// function to validate Egyptian phone number" and Copilot would write the entire function. But it had many issues: it suggested code from outdated libraries, didn't understand the bigger context of the project, and sometimes hallucinated functions that didn't exist. In my projects like Esma3 Radio, it helped with small tasks like writing CSS or utility functions, but anything beyond that I had to write myself.
The Partner Phase (2024-2025) changed everything. Tools like Cursor appeared and understood that AI needs to understand the whole project, not just the file you're working in. Cursor reads all files, understands the architecture, and suggests changes consistent with the rest of the project. In the Elmokhber project, I wanted to add a new feature to the game room. Instead of explaining everything to AI from scratch, Cursor understood the context on its own because it read all the code. It suggested a solution that was consistent with the pattern I was using throughout the project. That's the real difference — AI now understands context instead of just guessing.
Using AI for debugging was a major turning point. Before, when I found a bug in an app like Battle of Questions — say, players not seeing the same scores at the same time — I'd sit and manually analyze the Socket.io events, read logs, and try to reproduce the problem. Now I tell AI "players are seeing different scores" and it analyzes the code and finds that the issue is a race condition in the event handler. AI doesn't solve it for you, but it gets you to the solution much faster.
In design, AI has become a real assistant. Tools like v0 can turn a text description into a complete UI component. In the Eah ElKalam app, I wanted a card design for Egyptian trends with an image, title, and share count. I described the design to v0 and got a complete card with Tailwind that was almost exactly what I imagined. I tweaked it a bit and was done. Previously, I would have spent an hour designing that card from scratch.
Testing is one of the areas where AI has had the biggest impact. Writing tests was always one of the things developers hate most — tedious, time-consuming, and easily forgotten. In Eleqbal Form, I used AI to write comprehensive tests for the registration form. The agent wrote tests for everything: invalid email, Egyptian phone numbers in different formats, empty fields, SQL injection. What would have taken me a full day of testing took half an hour.
So what does AI still struggle with? First: understanding the real intent behind a request. Sometimes I ask for something and AI interprets it differently and produces something almost right but not quite. For example, in Tammeny, I asked AI to "add a notification feature when someone accesses your location" — it understood I wanted Push Notifications, but I meant an in-app notification. The difference is subtle but important. Second: AI loves over-engineering. You ask for something simple and it responds with a complex solution full of Design Patterns and techniques you don't need. That's why you always need to be clear and specific in your requests.
One of the most frequently asked questions: will AI replace developers? My personal opinion: no, but it will replace developers who don't use AI. The difference is like someone writing with a pen versus someone using a keyboard — both write, but the latter is much faster. A developer using AI can build in one day what a traditional developer takes a week to do. But there are things AI can't do: understand real client needs, make architectural decisions based on project context, and be creative in solutions. Critical thinking and creativity are still human.
My advice for any developer: start integrating AI into your workflow gradually. You don't have to use it for everything from day one. Start with things that take a lot of time and are tedious: writing tests, documentation, refactoring. Then gradually use it for bigger tasks like designing components and writing APIs. The important thing is to review everything AI generates — don't forget that you're ultimately responsible for the code. AI is a powerful partner, but a partner needs someone to guide them.
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