I Found 15 Different Email Validation Functions in My AI-Generated Code

Last week, I found something that made me physically ill. Fifteen different email validation functions. In the same project. All written by AI. Each one slightly different, all essentially identical.

const validateEmail = (email) => {
  return /^[^\s@]+@[^\s@]+\.[^\s@]+$/.test(email);
};

function isValidEmail(emailAddress) {
  const regex = /^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$/;
  return regex.test(emailAddress);
}

export const checkEmail = (str) => {
  const emailRegex = /^[\w-\.]+@([\w-]+\.)+[\w-]{2,4}$/;
  return emailRegex.test(str);
}

Welcome to the AI Function Graveyard, where perfectly good utilities go to die… repeatedly.

The Token Holocaust

"One mid-sized project burned through $847 just regenerating utility functions"

— Not including actual business logic

Let’s do the math that nobody wants to do. Each email validation function costs approximately 150 tokens to generate (prompt + response). Fifteen versions? That’s 2,250 tokens. At current GPT-4 rates, you just spent $0.07 regenerating the same damn function.

“Seven cents?” you scoff. “That’s nothing.”

Now multiply that by every utility function in your codebase. Date formatting? I counted 23 versions. String truncation? 18. Deep object cloning? Don’t get me started – 31 variations, each more convoluted than the last.

One mid-sized project I audited had burned through $847 in tokens just regenerating utility functions. That’s not counting the actual business logic. That’s just AI rewriting leftPad for the thousandth time.

The Maintenance Nightmare

But tokens are just the appetizer. The real feast of suffering comes during debugging.

Picture this: Production bug. Customer data isn’t validating correctly. You grep for email validation. Fifteen results. Which one is actually being used? Time to play everyone’s favorite game: “Trace the Import Chain.”

Three hours later, you discover that validateEmail, isValidEmail, and checkEmail are all being used in different parts of the app. Two of them have subtle regex differences that cause edge case failures. One allows unicode, two don’t. Your QA team is having a meltdown because test coverage differs for each function.

"This isn't hypothetical. This is Tuesday."

Why AI Can’t Remember

Here’s the dirty secret: AI has goldfish memory. Every new session, it’s like meeting your utility functions for the first time. “Email validation? Never heard of her. Let me write you a fresh one!”

Current AI coding assistants have no concept of your existing codebase’s utility ecosystem. They can’t search for existing implementations. They definitely can’t remember what they wrote last week. So they do what they do best: generate new code from scratch.

It’s like having a brilliant developer with severe amnesia. Every morning they wake up and reimplement quicksort because they forgot it exists.

Enter Multi-Pass Planning

This is where xSwarm gets interesting. Instead of letting AI freestyle your utilities into oblivion, we enforce a three-pass epoch planning system:

Pass 1: Pure Function Extraction Before writing a single line of feature code, xSwarm scans for and extracts every pure function opportunity. Email validation? That’s a pure function. Date formatting? Pure. String manipulation? Pure as driven snow.

Pass 2: Function Registry Building Every extracted function gets cataloged with:

• Semantic fingerprint (what it actually does)
• Complexity score (how gnarly is this beast?)
• Reusability index (will we need this again?)
• Dependency graph (what else does it need?)

Pass 3: Implementation with Reuse Only now does xSwarm start building features. But here’s the kicker – before generating any utility, it searches the registry. Found a match? Reuse it. No match? Generate it once, catalog it forever.

The 60% Solution

We’re seeing 60%+ code reuse rates in projects using multi-pass planning. Let that sink in. More than half your codebase doesn’t need to be written because it already exists.

One team reduced their utility generation from 1,847 functions to 312 unique implementations. That’s 83% fewer functions to maintain, test, and debug. Their token costs dropped by 71%. Their developers stopped crying during code reviews.

Building Digital Memory

The magic isn’t just in the extraction – it’s in the memory. xSwarm’s function registry becomes your team’s collective unconscious. Every pure function ever written, searchable, reusable, battle-tested.

No more “I swear we wrote this last sprint.” No more utility sprawl. No more token bleeding. Just clean, reusable code that accumulates value over time instead of technical debt.

Your codebase transforms from a graveyard into a library. A living, breathing repository of solved problems.