Vibe Coding Gets You Started. Product Knowledge Gets You to Production.

A few months ago, I would've said the hardest part of building software was writing code.

Today, I don't think that's true anymore.

With tools like Claude, ChatGPT, Cursor, and GitHub Copilot, writing code has become dramatically faster. Features that once took days can now be prototyped in hours. Entire UI screens appear from a simple prompt, and boilerplate code almost writes itself.

This new way of building has a name now: Vibe Coding.

And honestly?

I love it. It has made me significantly more productive.

But after spending months building OrganizeEmail, one lesson became impossible to ignore:

Code is no longer the bottleneck. Everything around the code is.

The Illusion of Progress

One evening, I asked an AI assistant to build a Gmail integration.

Within minutes, it generated:

• A clean OAuth authentication flow
• Token management utilities
• Standard Gmail API calls
• An email listing logic
• A beautiful, working UI

Everything compiled. Everything looked correct. It felt like the feature was 95% finished.

In reality... I hadn't even started.

Real Products Don't End at "It Works"

Connecting to Gmail isn't simply calling an API. The moment you want to put that feature in front of real users, you enter Google's complex ecosystem.

That means understanding:

• OAuth scopes: Which permissions are absolutely necessary versus nice-to-have.
• Restricted permissions: Why certain scopes trigger deep, mandatory platform security reviews.
• User consent: How to build confidence before a user clicks "Allow."
• Security requirements & privacy expectations: How user data is handled, stored, and protected.
• Verification processes: How to satisfy platform-specific compliance criteria.

None of this is solved by generating more code.

The Gmail API Was the Easy Part

For OrganizeEmail, we needed access to Gmail so users could organize their inbox intelligently.

Writing the integration wasn't particularly difficult. Getting permission to use it in production was.

We had to understand:

1. Which OAuth scopes were appropriate for our exact use case.
2. Why some scopes trigger additional, highly stringent reviews.
3. How to explain our data usage transparently to both Google and our users.
4. How Google's verification process actually works.
5. What exact information users should see before signing in.

In the grand scheme of things, the code represented only a small part of the journey.

Then Came the CASA Assessment

This was something I'd never dealt with before.

If your application requests certain Google data (specifically restricted scopes), you may need a CASA (Cloud Application Security Assessment).

No AI model can magically complete a CASA assessment for you. It requires you to fully understand and document:

• Security architecture
• Data storage and encryption protocols
• Internal access controls
• Third-party dependencies
• Risk management frameworks

These are product-level and organizational responsibilities, not coding problems.

Outlook Was a Completely Different World

After Gmail, adding Outlook sounded simple. "Just connect another provider," I thought.

Except it wasn't.

Microsoft Graph operates under a completely different paradigm:

• Different OAuth flows and authorization endpoints
• Different permission models and consent structures
• Different APIs and payload formats
• Different tenant and application approval processes
• Different documentation styles

While the UI on the frontend barely changed, the underlying product knowledge required to make it production-ready changed completely.

The Invisible Layer of Shipping Software

Building OrganizeEmail wasn't just about writing Android or web code. It also meant learning and addressing things that AI prompt boxes don't prepare you for:

• Token refresh strategies that don't leave users logged out mid-session.
• API quotas and rate-limiting handling.
• Background synchronization logic that doesn't drain the device battery.
• Privacy policies that are legally compliant.
• Play Store compliance and review guidelines.
• Security disclosures and vulnerability management.
• Email provider limitations and specific header formats.
• Graceful error handling across completely different providers.

None of these problems disappear because AI writes code faster. If anything, they become more prominent.

Sayne Taught Me the Same Lesson

The same lesson applied when we built Sayne.

Creating a beautiful website wasn't the difficult part. The difficult part was designing an experience that actually communicates trust and authority.

Questions we had to answer included:

• How should the product be positioned in the market?
• What should the homepage communicate first to capture interest?
• How do we explain complex privacy practices in plain English?
• How do we structure documentation so it's useful but not overwhelming?
• How do we introduce features step-by-step?

Those decisions weren't programming tasks. They were product decisions.

Vibe Coding Is an Accelerator—Not a Replacement

I think many developers misunderstand what AI is changing.

AI isn't replacing engineers. It is replacing repetitive implementation. That is a huge difference.

Instead of spending six hours writing boilerplate, we now spend six hours understanding the actual system and problem we're trying to solve. Ironically, this makes domain and product knowledge more valuable than ever.

The Best Developers Will Look Different

A few years ago, strong engineers were measured by how quickly and cleanly they could write code.

Today, the best engineers are measured by how quickly they can understand systems:

• Can they navigate OAuth and compliance?
• Can they safely integrate third-party platforms?
• Can they make good, user-centric product decisions?
• Can they communicate clearly with users and platforms?

These skills don't become less important because AI exists. They become your core competitive advantage.

What Building OrganizeEmail Changed for Me

Before starting OrganizeEmail, I thought software development was mostly about engineering.

Now, I think it's closer to building a business. You are constantly learning about:

• APIs and integrations
• Security and compliance
• User Experience (UX)
• Scalable infrastructure
• Product strategy
• Platform policies
• Establishing user trust

Writing code is just one piece of that puzzle. And thanks to AI, it may no longer be the biggest piece.

Final Thoughts

I still use AI every single day. I use it to write code, review architecture, generate ideas, and speed up development. I wouldn't want to build without it.

But I've stopped believing that faster coding automatically translates to faster products.

Because the real challenge isn't making software compile. It's making software trustworthy, secure, compliant, scalable, and genuinely useful.

Vibe coding can get your prototype running. Understanding the ecosystem is what gets your product into users' hands.