Vibe coding looks easy… until your app breaks. 😅

“Just tell AI what you want and it will build it.”

Sounds great.

Until you spend credits asking it to fix the same bug 10 different ways.

Then another feature breaks.

You fix that one.

Something else stops working.

Then you're staring at the same error for hours thinking:

“Why is this still happening?”

That's the part people don't show you about vibe coding.

AI can write code incredibly fast, but fast code doesn't automatically mean good code.

You still need to understand:

→ How the application is structured → Where the bug is actually coming from → How different features affect each other → When an AI-generated solution is making things worse → How to debug instead of blindly regenerating code

And sometimes the biggest time killer isn't building a feature.

It's getting stuck on one tiny bug that refuses to die.

That's why I don't see vibe coding as:

“Tell AI what to build.”

I see it as:

Human problem-solving + AI development.

The AI can help you move faster.

But you still need someone who knows where to go when things stop working.

That's where the real skill comes in.

What has been your biggest headache with vibe coding? 👇

— Optimum Studio

the same bug fixed 10 different ways is painfully accurate lol

Most inbound bottlenecks aren't caused by low lead volume—they happen during triage.

When inquiries roll into a Google Sheet or CRM, operations teams often waste 10–15 minutes per submission reading through messy paragraphs, deciphering budget mismatch, and manually deciding who gets an immediate call vs. a standard rate sheet.

Instead of adding a bulky CRM add-on, I documented a lightweight 3-step automation pipeline built inside Make:

Intake & Extraction: An active listener triggers immediately upon a new submission, extracting core variables: client scope, target deliverables, timeline, and stated budget.

Context-Aware Evaluation (Gemini 2.5 Flash): The payload passes through an AI evaluation node with strict guardrails:

Data Return & Clean Pipeline: Appends the structured classification directly back to the master operational tracker, keeping the sheet filterable and clean.

The outcome: The sales and operations team can filter the entire board by priority status in two clicks, cutting first-touch response time to minutes without manually reading every single submission.

I’ve documented the full scenario map, execution logic, and data schemas below.

Curious how other agency operators and automation leads here handle their lead enrichment layer—are you doing AI scoring at the form level, or later in the CRM pipeline?:

For the budget gate, I'd keep an unstated budget separate from one explicitly below your minimum. Does the Make route ask for clarification when the amount or currency is missing, so a potentially suitable lead doesn't fall into Cold by default?