My AI Website Stack in 2026: How I Actually Build Websites With AI

A real look at the AI website stack I use in 2026, including ChatGPT, Cursor, v0, GitHub, Supabase, and Vercel — what each tool does and what I learned using them together.

Ryan LeeAugust 29, 2026Updated: September 30, 202610 min read

My AI Website Stack in 2026: How I Actually Build Websites With AI

Not long ago, if someone had told me I would be building complete websites myself, I probably would not have believed them.

Not just designing a homepage.

I mean actually building websites with databases, authentication, SEO, deployment, content, domains, and ongoing updates.

I am not a traditional software developer.

I did not spend years learning JavaScript, databases, server infrastructure, or deployment pipelines.

Yet today, I can move from an idea to a functioning website much faster than I ever thought possible.

The reason is not one AI tool.

It is the way several tools work together.

Over the past few months, I have experimented with ChatGPT, Cursor, v0, GitHub, Supabase, and Vercel across real website projects.

Some worked immediately.

Some confused me.

Some cost me money because I did not understand how to use them properly.

But together, they have completely changed the way I build.

This is the AI website stack I currently use in 2026 — and more importantly, what I have learned from actually using it.

1. ChatGPT — Where Most Projects Begin

For me, ChatGPT is usually where a project begins.

But not in the way I first expected.

When I started using AI, I often asked questions like:

"Build me a website."

"Write this page."

"Fix this problem."

The results were sometimes useful, but often inconsistent.

Eventually I learned that ChatGPT becomes much more powerful when I use it before I start building.

Today, I use it to think through:

website structure
user journeys
database requirements
SEO strategy
content architecture
technical problems
feature priorities

The biggest change was simple.

I stopped asking AI to replace my thinking.

I started using it to clarify my thinking.

That difference improved almost everything that came afterwards.

It is the same lesson I learned when AI did not save me time at first: the tool became more useful when I became better at working with it.

2. v0 — Turning Ideas Into Interfaces Quickly

Once I understand what I want to build, v0 is often useful for turning that idea into something visual.

This was one of the first AI website tools that genuinely surprised me.

You can describe a page, interface, dashboard, or component and see something usable appear remarkably quickly.

But I also learned its limitations the expensive way.

When I first started using v0, I kept rebuilding things.

I changed direction halfway through projects.

I experimented without a clear plan.

And I used far more credits than I expected.

That experience taught me an important lesson:

AI tools become expensive when your thinking is unclear.

Today, I try to define the structure before generating the design.

v0 works much better for me when the question is:

"Create this specific interface."

rather than:

"Help me figure out what I want."

I wrote the practical version of that lesson in How to Use v0 Without Wasting Credits in 2026, including the workflow I now use before I start generating.

3. Cursor — Where the Website Becomes a Real Project

v0 can help create the interface.

Cursor is where I spend more time turning that interface into a functioning website.

Cursor was also one of the tools that frustrated me the most in the beginning.

I did not understand Agents properly.

I asked it to change too many things at once.

Sometimes a simple fix created another problem somewhere else.

Other times I burned through usage solving problems I did not fully understand.

At first, I thought the problem was Cursor.

Eventually I realised the problem was my workflow.

Now I try to work differently.

I define the problem first.

I understand which files are involved.

I make smaller, intentional changes.

And I check what changed before moving on.

The better I understand the project, the more useful Cursor becomes.

That pattern keeps repeating across almost every AI tool I use.

I wrote a deeper account of that change in How to Use Cursor for Website Development in 2026, including the workflow that finally made Cursor feel less like magic and more like a tool I could control.

4. GitHub — The Safety Net I Did Not Appreciate at First

When I first started building websites, GitHub felt unnecessarily technical.

Repositories.

Branches.

Commits.

Pull requests.

All of those words felt like something developers needed to understand.

Now I see GitHub very differently.

It is one of the most important parts of my workflow.

AI makes development faster.

But faster development also means you can break things faster.

GitHub gives me a history of what changed.

If something works, I can preserve it.

If something breaks, I can compare it.

If I want to test an idea, I can work on it separately before it reaches the production website.

AI helped me build faster.

GitHub helped me become less afraid of breaking things.

That combination is surprisingly important.

5. Supabase — When a Website Needs to Become an Application

There is a major difference between a website that shows information and a website that manages users and data.

That is where Supabase started becoming important for me.

On one of my real projects, I needed more than static pages.

The site needed things such as user accounts, authentication, profiles, approval status, permissions, and structured information.

A few months earlier, that would have sounded completely outside my ability.

Supabase made the backend much more approachable.

That does not mean every website needs Supabase.

If a project is mostly pages and content, adding a database would only create unnecessary complexity. I use Supabase when a project genuinely needs persistent data, authentication, permissions, or application-like features.

And even then, it is not effortless.

Authentication still needs to be understood.

Permissions matter.

Row Level Security matters.

Database structure matters.

AI can help write the code, but you still need to understand what the system is supposed to protect.

That has been one of the biggest lessons from using Supabase:

AI can make backend development more accessible, but it does not remove the responsibility to understand the data.

I wrote a deeper account of that transition in Why Use Supabase for a Web App? Auth, RLS & Real Project Lessons, including the point where frontend-only building stopped being enough.

6. Vercel — From My Computer to the Real Internet

There is something surprisingly satisfying about watching a website move from a local project to a real domain.

For most of my current projects, Vercel handles that step.

The workflow is now remarkably simple.

Code changes go into GitHub.

Vercel detects the changes.

A preview deployment is created.

I can check whether everything works.

Then the production version can be updated.

What once sounded like a complicated deployment process now feels like a natural part of the workflow.

I explain that release process in How to Use Vercel for Website Deployment: My 2026 Workflow.

The most useful troubleshooting lesson came later: if the same code works locally or in Production but fails in Preview, I now check the environment before rewriting the code. I documented that process in Vercel Environment Variables Not Working? 7 Supabase Fixes.

And that is probably the most interesting thing about this entire stack.

None of these tools alone changed the way I build.

The connection between them did.

The Real Stack Is the Workflow

It is tempting to think the secret is finding the best tool.

I used to think that way too.

Which AI is best?

Which website builder is best?

Which coding assistant is best?

I no longer think those are the most useful questions.

The better question is:

How do the tools work together?

My current process often looks something like this:

Idea → ChatGPT → v0 → Cursor → GitHub → Vercel

And when the project needs accounts, data, or backend logic:

Idea → ChatGPT → v0 → Cursor → GitHub → Supabase → Vercel

ChatGPT helps me think.

v0 helps me see.

Cursor helps me build.

GitHub helps me control changes.

Supabase helps me manage data and users when the project needs it.

Vercel helps me put it online.

The value is not any single tool.

The value is the workflow between them.

Practical guides for each part of the stack

If you want to go deeper into the parts I use most:

What AI Still Does Not Do for Me

This is also where I think many discussions about AI website building become unrealistic.

AI still does not decide what the business actually needs.

It does not automatically understand the customer.

It does not know which feature is unnecessary.

It does not always recognise when an answer is technically correct but practically wrong.

And it certainly does not eliminate mistakes.

I still make plenty of them.

The difference is that mistakes are now much faster to test, identify, and correct.

AI has not removed the learning process.

It has compressed it.

That is also why I now think a useful website matters more than a perfect one.

What I Would Do Differently If I Started Again

I would use fewer tools at the beginning.

I would spend more time defining the project before generating anything.

I would use GitHub earlier.

I would make smaller changes in Cursor.

I would understand the database before asking AI to build it.

And I would stop treating AI credits as unlimited experimentation.

The tools are incredibly powerful.

But clear thinking still saves more time and money than any subscription.

If you are preparing an AI-assisted website for launch, I turned many of these lessons into a practical AI Website Launch Checklist covering purpose, content, mobile use, technical foundations, search, trust, and post-launch review.

What Comes Next

My stack is still evolving.

There are other tools I am beginning to explore more seriously.

Resend is interesting for transactional email and automated communication.

Higgsfield opens another direction around AI-generated video and advertising.

There are also tools for automation, content creation, analytics, and business workflows that I want to test in real projects.

I will write about those as I actually use them.

That distinction matters to me.

AI Workbench Lab is not meant to become a catalogue of tools I have never touched.

I want it to remain a record of what happens when these tools meet real work.

What works.

What breaks.

What costs more than expected.

And what eventually becomes useful.

Final Thoughts

Not long ago, building a website felt like a specialised technical skill.

Today, the barrier is much lower.

But I do not think AI has made developers unnecessary.

What it has done is give people like me a much better starting point.

I can learn while building.

I can test while learning.

And I can turn ideas into real projects before I understand everything perfectly.

That may be the biggest change AI has created for me.

Not that I suddenly became a developer.

But that I stopped believing development was something I could never learn.

The tools helped.

The mistakes helped more.

And the workflow continues to get better every time I build something new.

That is what AI Workbench Lab is really about: practical AI, real experience, and building in public.

#AI Website Stack#ChatGPT#Cursor#v0#GitHub#Supabase#Vercel#AI Web Development