Over the last several months, I have spent a ridiculous amount of time building WordPress plugins using AI coding tools.

Not toy projects.

Real plugins.

Things involving:

  • ⚙️ WordPress hooks

  • 🔄 AJAX

  • 🛒 WooCommerce

  • 🎨 CSS conflicts

  • 📱 responsive layouts

  • 🎮 game systems

  • 🏠 lobby systems

  • 🖼️ image handling

  • 🐞 debugging

  • 🧩 plugin architecture

  • ⚡ realtime interactions

And after using multiple AI tools heavily, I realized something important:

💡 Every AI coding tool has a personality.

Some are better at reasoning.
Some are faster.
Some are cleaner.
Some are better at debugging.
Some are more creative.
Some feel like they are fighting you while coding.

This is not a scientific benchmark. This is simply my real-world experience as someone actively building WordPress plugins and dealing with actual development problems.

🧠 OpenAI GPT

OpenAI

This is the one that surprised me the most overall.

Especially the higher reasoning models.

When dealing with difficult debugging problems, plugin architecture issues, or complicated logic, GPT models tend to break problems down extremely well.

One thing I noticed immediately:

🔍 They analyze deeply.

Sometimes almost too deeply.

But when a problem becomes complex, that analytical behavior becomes incredibly valuable.

I have had situations where lighter models from multiple providers struggled for nearly an hour trying to fix an issue, then a higher reasoning GPT model solved it in minutes.

That stood out to me.

⚠️ The downside?

Token usage.

The stronger reasoning models can burn through usage limits very quickly if you are not careful. For large WordPress projects, this matters.

That is why I eventually started balancing between lighter GPT models and higher reasoning models depending on the task.

For example:

  • 🎨 CSS tweaks

  • 🔧 small plugin adjustments

  • 📐 layout changes

The lighter models are usually enough.

But when things become deeply technical:

  • 🧩 architecture

  • 🐞 debugging

  • 🔄 conflicting systems

  • ⚡ complex flows

that is where the stronger reasoning models shine.

💬 Anthropic Claude

Anthropic Claude

Claude feels different.

It often feels more conversational and more natural during development.

Sometimes it produces cleaner frontend code and UI structures than other models.

I actually like Claude quite a lot.

But I noticed something during larger projects:

⚠️ When fixes become messy, Claude sometimes avoids restructuring existing code properly.

Instead of cleaning up the old system, it may build around the mess.

This can create situations where:

  • 🧱 old code remains

  • ➕ new code gets layered on top

  • 🌀 debugging becomes harder later

So the result depends heavily on how organized the existing project already is.

Still, for many frontend tasks and general development flow, Claude is genuinely enjoyable to work with.

🛠️ GitHub Copilot

GitHub Copilot

Copilot is probably one of the most practical tools overall.

Not necessarily because it is always the smartest, but because it integrates naturally into workflow.

One thing I appreciate:

✅ It keeps working.

Even when higher-tier models become unavailable, you are often not completely cut off from development.

That matters.

Because during real projects, consistency matters more than marketing benchmarks.

Copilot also feels less aggressive about restrictions compared to some other platforms.

For WordPress work:

  • ✍️ autocomplete

  • 📦 repetitive structures

  • 🧰 boilerplate

  • 🔌 utility functions

Copilot can speed things up significantly.

Especially inside an IDE environment.

🧪 DeepSeek

DeepSeek

DeepSeek is extremely analytical.

Sometimes impressively analytical.

The issue is:

🐢 That same strength can also slow it down dramatically.

I have had moments where it spent several minutes reasoning through what looked like a relatively small problem.

But at the same time, DeepSeek often catches logic details other models skip.

So for deep reasoning tasks:

  • 🔍 debugging

  • 🧠 logic tracing

  • 📊 code analysis

it can actually be very strong.

You just need patience.

It feels less like:

fast coding assistant

and more like:

careful technical analyst

🚀 Cursor

Cursor

Cursor honestly impressed me.

What stood out immediately was how integrated the experience felt.

It does not just generate code.

⚡ It feels like it actively participates in the project.

Sometimes Cursor:

  • 🌐 loads pages

  • 🔎 analyzes outputs

  • 🧵 traces issues

  • 🧪 tests flows

That creates a very powerful development feeling.

The reasoning quality is also extremely good.

Especially for debugging.

There were moments where Cursor tore apart complicated problems almost immediately.

The downside for me personally was not the quality.

It was payment flexibility.

Otherwise, it probably would have become one of my primary environments.

🧠 Amazon Kiro

Amazon Kiro

Kiro is interesting.

I actually like it.

But my experience was mixed.

Sometimes Kiro produces genuinely impressive work very quickly.

Other times, when asked to fix existing systems, it may skip cleaning the original implementation and instead introduce parallel logic.

That can create chaos if you are not watching carefully.

In other words:

⚠️ Kiro works best when the developer remains actively involved.

You cannot fully “hands off” complex projects.

Still, it has strong creative potential and can move quickly when guided properly.

🔄 What I Eventually Realized

At some point, I stopped asking:

❓ “Which AI tool is best?”

Because the real answer became:

✅ Different tools are good at different things.

For my workflow:

  • ⚡ lightweight models handle repetitive work

  • 🧠 reasoning models handle deep debugging

  • 🎨 some tools feel better for UI

  • 🏗️ some feel better for architecture

  • 🚀 some feel faster

  • 🛡️ some feel more stable

And honestly, WordPress development is one of the best stress tests for AI coding tools because WordPress environments are messy by nature.

You are constantly dealing with:

  • 🎨 themes

  • 🔌 plugins

  • 🌍 shared CSS

  • 📱 responsiveness

  • 🪝 hooks

  • 🔄 AJAX

  • ⚠️ compatibility

  • 🔗 frontend/backend interactions

It exposes weaknesses quickly.

🏁 The Biggest Lesson

The biggest lesson I learned is this:

🤝 AI coding tools are not magic replacements for developers.

They are accelerators.

A good developer with AI becomes dramatically faster.

But the developer still needs:

  • 🧠 judgment

  • 🏗️ architecture awareness

  • 🐞 debugging ability

  • 🎨 design understanding

  • 📋 workflow discipline

The AI helps you move faster.

But you still need to know where you are going.

💭 Final Thoughts

Right now, AI-assisted development feels less like replacing developers and more like giving developers superpowers.

Some tools are analytical.
Some are creative.
Some are fast.
Some are stable.
Some are frustrating.
Some are brilliant.

But overall, there is no question in my mind that AI coding tools are changing WordPress development permanently.

And honestly?

🚀 Once you get comfortable using them properly, it becomes very difficult to imagine going back.

 

by Dwight Collins