VibeCoding: How We Deploy Experiments Faster Without Cutting Corners

Last month, we shipped three experiments in the time it used to take us to ship one. We didn’t work longer hours or hire more people. We just changed how we start new projects.

The approach is called VibeCoding, and it’s become a core part of how we build at ABXK.AI. But this is not just AI hype. Let me explain: VibeCoding isn’t about letting AI write your code while you sit back. It’s about using AI tools smartly to reduce the time between idea and results.

What VibeCoding Actually Means

The term “VibeCoding” gets used a lot, often in ways that worry experienced engineers. So let me explain what it means for us.

VibeCoding is AI-assisted development where you describe what you want to build, and AI helps create the first version. You then review, improve, and repeat until you have working code. The “vibe” part comes from the conversation style—you’re working with an AI assistant rather than writing every line yourself.

At ABXK.AI, we use VibeCoding for:

• Rapid prototyping — Getting a working version quickly to test core ideas
• Writing basic setup code — Skipping repetitive code that doesn’t need creative thinking
• Exploring unfamiliar APIs — Learning new libraries faster by seeing working examples
• Data transformation scripts — Writing one-time scripts for data processing tasks

What we don’t use it for:

• Production systems that need high reliability
• Security-critical code paths
• Complex logic where precision matters
• Code that will be maintained long-term without major rewrites

This difference is important. VibeCoding is a tool for specific situations, not a replacement for engineering skills.

Why This Matters for Experimentation

Most of our work at ABXK.AI involves testing ideas. Does this trading signal help predict prices? Can we improve model performance with different features? Will this data pipeline handle more data?

The traditional approach looks like this:

1. Spend days building a proper implementation
2. Run the experiment
3. Discover the hypothesis was wrong
4. Repeat

The problem isn’t the engineering quality—it’s the feedback loop. When you spend a week building something only to learn it doesn’t work, you’ve lost a week. Multiply that across dozens of experiments, and you’ve lost months.

VibeCoding compresses the first step. Instead of days, you’re looking at hours. The code might not be production-ready, but it’s good enough to test the hypothesis. If the experiment shows promise, you invest in proper implementation. If it doesn’t, you move on without significant time loss.

A Real Example: Testing a New Feature Pipeline

Last week, we wanted to test whether adding sentiment data from financial news would improve our trading model’s predictions.

Traditional approach estimate: 3-4 days to build a clean pipeline, connect it to our existing system, write tests, and run the experiment.

What we actually did: Used VibeCoding to create a rough pipeline in about 2 hours. The code wasn’t elegant—fixed paths, minimal error handling, no tests—but it worked well enough to feed sentiment features into our model and check if the signal had any value.

Result: The sentiment data showed no measurable improvement in our case. Time saved: about 2.5 days.

If the experiment had shown good results, we would have spent time building a proper version. But it didn’t, so we moved on to the next idea.

Comparing Approaches: Pros and Cons

Let’s be honest about both approaches. Neither is always better—it depends on the situation.

Traditional Development

Strengths:

• Produces code that is easy to maintain and well-tested
• Better for complex systems with many parts
• Easier to debug when things go wrong
• Knowledge stays in your head, not in AI prompts
• Works well for problems you understand deeply

Weaknesses:

• Slower feedback cycles
• Higher time investment at the start
• Can lead to building too much when needs are unclear
• Expensive way to discover an idea is wrong

VibeCoding

Strengths:

• Fast progress on early-stage ideas
• Lower cost for experiments that might fail
• Helpful when learning new tools or APIs
• Good for throwaway code and prototypes
• Reduces switching between tasks during exploration

Weaknesses:

• Generated code often needs a lot of cleanup
• Can introduce small bugs if not reviewed carefully
• Doesn’t build deep understanding of the code
• Poor fit for security-sensitive or production systems
• You need self-control to avoid creating messy code

The key lesson is that both approaches have their place. We use VibeCoding for exploration and testing, then switch to traditional development when we’re building for production.

When to Use Each Approach

Here’s a simple guide we follow:

Use VibeCoding when:

• You’re testing an idea that might be wrong
• The code is temporary or experimental
• Speed matters more than long-term quality
• You’re exploring an unfamiliar area
• The risk of failure is low

Use traditional development when:

• You’re building production systems
• The code will be maintained for months or years
• Security, reliability, or performance are critical
• You need deep understanding of how it works
• Others will depend on your code

Use a mix of both when:

• You start with VibeCoding to test the idea
• Then rewrite properly once you know it works
• This is often our default for research projects

Limitations and Responsible Use

VibeCoding has real limitations that are worth knowing.

Quality limit: AI-generated code tends to follow common patterns. For new problems or faster solutions, you often need to write code yourself.

Review work: You have to read and understand every line. If you don’t, you’re adding unknown behavior to your system. This takes time and attention.

Learning trade-off: When AI writes code for you, you learn less about the problem. For one-time experiments, this is fine. For skills you need to develop, it’s a real cost.

Debugging difficulty: When AI-generated code fails, debugging can be harder because you didn’t write it. You have to figure out what the code is trying to do.

Overconfidence risk: Working code isn’t the same as correct code. VibeCoding can create a feeling of progress that isn’t real if you’re not testing well.

We treat these as known limits, not reasons to avoid the approach. Every tool has limitations. The goal is to use tools in the right way.

What We’ve Learned

After several months of using VibeCoding in our work, here’s what we’ve learned:

Speed is real, but not free. We do ship experiments faster. But we also spend more time reviewing generated code than we expected. The overall benefit is positive, but it’s not magic.

Self-control matters. It’s tempting to ship VibeCoded prototypes directly to production. Don’t do this. The messy code problems grow quickly.

Know when to stop. If you’re on your fifth try of prompts trying to get the AI to do something complex, you’ve probably passed the point where writing it yourself would have been faster.

Use good testing. Since you didn’t write the code, testing is your safety net. We run experiments multiple times and check results before trusting them.

It doesn’t replace basics. Understanding systems, algorithms, and architecture still matters. VibeCoding just changes where you spend your time.

Interested in how we apply these ideas? Check out our AI Trading Platform journey or learn how we reduce AI development costs across projects.