What Is White Coding? A Smarter Take on Vibe Coding

White coding is what happens when you plan the build on a whiteboard first, then let AI agents write it. It fixes vibe coding's core problem: shipping code without anyone reading it.

That problem traces back to the term's origin. Andrej Karpathy coined "vibe coding" in February 2025 and told people to "forget that the code even exists." White coding keeps that speed but moves the thinking to before you prompt.

What is white coding? The 30-second answer

White coding is a way of building software where you sketch the design collaboratively on a whiteboard, agree on how the thing should work, then hand that plan to AI agents that write the code. The term comes from Capgemini architect Iwan van der Kleijn, who coined it to pair whiteboard thinking with vibe coding.

The name is a little tongue-in-cheek, but the point is serious. The human decides the architecture and the boundaries, and the AI does the typing.

Bottom line: vibe coding starts from "what can the AI build if I just talk to it?" White coding starts a step earlier, from "what should we build, and does the code match what we agreed?"

How does white coding work?

White coding runs in two moves. First, the whiteboard: a developer and whoever owns the problem draw the data model and user flows, down to the rules the app can't break. Then the agents turn that agreed picture into working code.

That order is the whole point. The planning is cheap, and the rework is expensive, so you pay up front, before a single line exists, and a bad idea caught on a whiteboard costs minutes instead of a sprint.

The workflow usually looks like this:

1. Sketch the design together. Whiteboard the screens and data with the people who'll use the app, including edge cases. This is where intent gets settled.

2. Write it down as a spec. Turn the drawing into a short spec the agents can follow. Deepak Singh, an AWS VP who leads the Kiro team, points out that senior engineers already do this. The first thing they do with a problem, he says, "is to write a specification."

3. Let the agents build against it. The AI generates the code, tests included, and measures it against the spec instead of against a vibe.

4. Validate. You read the diff, run the tests, and confirm the code does what the whiteboard said. If it wanders from the plan, you fix the spec first, then regenerate the code from it.

In vibe coding, you course-correct after you notice something's off. The difference is where the structure sits.

As Singh put it, an LLM by itself is good, but "it's the scaffolding around it that makes it useful." In white coding, the whiteboard is that scaffolding.

White coding vs. vibe coding: what's the difference?

The main difference between white coding and vibe coding is when the thinking happens. Vibe coding thinks while it types, whereas white coding settles the thinking first and then types fast. Both run on natural-language prompts to an AI, so they split on everything that happens around the prompt. That difference is what separates a weekend hack from enterprise-grade software.

That difference isn't just conceptual; it shows up in the defect numbers. A December 2025 CodeRabbit analysis found security issues ran up to 2.74 times higher in AI co-authored code than in human-written code.

Reviewing against a spec is how white coding stops those from reaching production. The diff, not the vibe, is where the bug gets caught.

What white coding gets right, and its limits

White coding pays off on anything you'll still be running in six months, though it asks for upfront patience.

What works:

• Intent survives. The spec holds the reasoning that vibe coding loses. As long as the spec stays current, six months in you can still see why the app works the way it does.
• Review stops being a favor. Because the code gets checked against a spec, the "I never looked at it" problem goes away, since every diff gets read before it merges. Simon Willison draws the line here: if an LLM wrote every line but you reviewed, tested, and understood it, "that's not vibe coding in my book, that's using an LLM as a typing assistant."
• Non-engineers shape the build. The whiteboard is a language product managers and analysts already speak, so they weigh in during design instead of waiting on a ticket.
• Rework drops. A design flaw you catch on the whiteboard is a five-minute edit. The same flaw caught in production is a rewrite nobody budgeted for.

Where it strains:

• The whiteboard step slows you down at the start. For a weekend toy it just feels like overhead, and for a five-screen app that never touches a database, vibe coding does the job.
• The spec can rot. A spec nobody updates rots, and a rotted spec is worse than none because people still trust it. Someone has to keep it current.

Should you use white coding?

Match the method to the stakes. Sergey Tselovalnikov, a software engineer at Canva quoted by MIT Technology Review, drew the line on where raw vibe coding stops being safe. He put it at "a tiny game or a small app that doesn't store any data."

Once the app stores data or has to work next quarter, you want the discipline. That's the threshold where white coding stops being overhead and starts paying for itself.

White coding is worth it when:

• You're building an internal tool your coworkers will depend on.
• The app connects to a database, an API, or customer records.
• More than one person has to understand and maintain the code later.
• A mistake would leak data or cost money, not just annoy you.

Skip white coding if:

• You're prototyping to see if an idea even feels right.
• The app is disposable and touches nothing sensitive.

For that second bucket, reach for vibe coding and skip the whiteboard. Tobin South, an AI security researcher at the MIT Media Lab, quoted in the same MIT piece, captured its appeal: it's "having a vision that you can't execute, but AI can."

White coding takes over when that vision has to hold up over time. Picking from the best enterprise vibe coding tools is where that work starts.

How to start white coding in 5 steps

The five steps below turn the idea into a habit.

1. Whiteboard before you prompt

Get the people who own the problem in a room, in person or remotely, and draw the data and the flows, then the rules the app must never break. Decide what the app can't do before you decide how it looks.

2. Turn the sketch into a written spec

Write the agreed design down in plain language the agents can follow. Keep it short. A spec people actually read beats an exhaustive one they ignore.

3. Generate against the spec, not the vibe

Prompt the agents to build to the spec, tests included. When the output strays, correct the spec first, then regenerate, so the plan and the code stay in sync.

4. Review every diff

Read what the AI wrote before it merges. Veracode's 2025 GenAI Code Security Report found 45% of AI-generated code samples contained at least one OWASP Top 10 vulnerability without human oversight. The diff is where you either catch that or inherit it.

5. Scan for secrets and bad packages

Run automated checks for hardcoded credentials and hallucinated dependencies before anything ships. GitGuardian counted 23.8 million secrets leaked on public GitHub in 2024 alone. Generated code is a fresh source, so scan every commit before it lands.

Pro tip: across all five steps, one habit helps most: start on a single low-stakes internal app. Prove the whiteboard-to-agents loop on something safe, and the team will trust it on the apps that touch live data and paying users.

White coding best practices

A few habits decide whether white coding sticks or slides back into vibe coding.

• Treat the spec like code. Version it, review changes, and keep it next to the repo. A living spec earns trust; a stale one loses it.
• Centralize the boring security work. Handle auth and permissions in one shared layer, secrets included, instead of letting the AI reinvent them per app, since secure-by-default tooling beats bolting checks on later. That's where generated code tends to cut corners.
• Vet every dependency the AI suggests. A 2024 USENIX study found models suggested non-existent packages 5.2% of the time for commercial models and 21.7% for open-source ones. Attackers register those hallucinated names, so confirm a package exists and is maintained before you install it.
• Keep a human accountable per app. The whiteboard has an owner. The shipped app should too. AI speeds the work; it doesn't hold the pager.

How Superblocks fits into white coding

White coding needs a tool that turns an agreed design into a governed app with no black box in between. Superblocks works this way.

Its AI feature, Clark, runs the whiteboard-to-agents loop. Here's how it maps to the practice:

• Governed generation. Clark generates a full internal app with your RBAC and SSO applied and audit rules attached automatically, so review has less to catch. Superblocks says Clark increases development velocity 10x while enforcing full governance.
• Full visibility, no black box. You see exactly what Clark changes, refine it in a visual editor, or drop into enterprise React mode. The plan and the code stay legible.
• Data stays yours. A stateless agent handles traffic between your API and database without routing it back through Superblocks' cloud, so regulated data stays inside your own network.
• No lock-in. Export the app as React and keep editing in your own IDE.

That mix of speed and control is what customers notice. Superblocks customers describe the contrast plainly: consumer builders like Lovable are great for messing around and quick demos, but Superblocks lets them actually deploy without the security headaches.

Cursor and Windsurf are built for engineers writing code fast; Superblocks sits at a different layer, turning an agreed design into a governed app the whole team can run.

The tools that win here won't be the fastest to a demo. They'll be the ones that let you move fast and still answer for what shipped. To try the whiteboard-to-governed-app loop yourself, book a demo. You can also see how it works in the Superblocks docs.

Frequently asked questions

What is white coding?

White coding means agreeing on the design on a whiteboard, turning it into a short spec, and letting AI agents build against that spec. Capgemini architect Iwan van der Kleijn coined the term to pair whiteboard thinking with vibe coding, so speed doesn't cost you control.

What is the difference between white coding and vibe coding?

The main difference between white coding and vibe coding is when you think. Vibe coding prompts an AI and edits as it goes, often skipping review. White coding settles the design and spec first, so every diff gets checked against what the team agreed.

Is white coding safer than vibe coding?

Yes, white coding is safer because the code gets reviewed against a spec instead of shipped on trust. A 2025 CodeRabbit analysis found security issues ran up to 2.74 times higher in AI co-authored code than in human-written code.

When should I use vibe coding instead of white coding?

Use vibe coding for throwaway prototypes and small apps that store no sensitive data. White coding pays for the extra step once the app touches a database, actual users, or code someone maintains later.

What tool is best for white coding in an enterprise?

Superblocks is the strongest fit for enterprise white coding because its Clark feature automatically generates internal apps with RBAC, SSO, and audit logs. A stateless agent keeps your data inside your own infrastructure, so governed builds don't turn into security headaches.