by ABXK.AI AI Development

VibeCoding: How We Deploy Experiments Faster Without Cutting Corners

vibecodingai-developmentprototypingengineeringproductivity

Last month, we shipped three experiments in the time it used to take us to ship one. Not because we worked longer hours or hired more people, but because we changed how we approach early-stage development.

The approach is called VibeCoding, and it’s become a core part of how we build at ABXK.AI. But before you dismiss this as another AI hype story, let me be clear: VibeCoding isn’t about letting AI write your code while you sit back. It’s about using AI tools strategically to reduce iteration time and reach measurable results sooner.

What VibeCoding Actually Means

The term “VibeCoding” gets thrown around a lot, often in ways that make experienced engineers cringe. So let me explain what it means in our context.

VibeCoding is AI-assisted development where you describe what you want to build, and AI helps generate the initial implementation. You then review, refine, and iterate until you have working code. The “vibe” part comes from the conversational flow—you’re collaborating with an AI assistant rather than writing every line from scratch.

At ABXK.AI, we use VibeCoding specifically for:

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

What we don’t use it for:

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

This distinction is important. VibeCoding is a tool for specific situations, not a replacement for engineering fundamentals.

Why This Matters for Experimentation

Most of our work at ABXK.AI involves testing hypotheses. Does this trading signal have predictive value? Can we improve model performance with a different feature set? Will this data pipeline scale?

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, integrate with our existing system, write tests, and run the experiment.

What we actually did: Used VibeCoding to generate a rough pipeline in about 2 hours. The code wasn’t elegant—hardcoded paths, minimal error handling, no tests—but it was functional 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 specific use case. Time saved: approximately 2.5 days.

If the experiment had shown positive results, we would have invested time in building a proper implementation. But it didn’t, so we moved on to the next hypothesis.

Comparing Approaches: Pros and Cons

Let’s be honest about both approaches. Neither is universally better—it depends on context.

Traditional Development

Strengths:

  • Produces maintainable, well-tested code
  • Better for complex systems with many dependencies
  • 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 iteration cycles
  • Higher upfront time investment
  • Can lead to over-engineering when requirements are unclear
  • Expensive way to discover a hypothesis is wrong

VibeCoding

Strengths:

  • Fast iteration 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 context-switching during exploration

Weaknesses:

  • Generated code often needs significant cleanup
  • Can introduce subtle bugs if not reviewed carefully
  • Doesn’t build deep understanding of the code
  • Poor fit for security-sensitive or production-critical paths
  • Requires discipline to avoid technical debt

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

When to Use Each Approach

Here’s a simple framework we follow:

Use VibeCoding when:

  • You’re testing a hypothesis that might be wrong
  • The code is temporary or experimental
  • Speed matters more than long-term maintainability
  • You’re exploring an unfamiliar domain
  • 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 the implementation
  • Others will depend on your code

Use a hybrid approach when:

  • You start with VibeCoding to validate 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 acknowledging.

Quality ceiling: AI-generated code tends to follow common patterns. For novel problems or optimized solutions, you often need to write code yourself.

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

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

Debugging complexity: When AI-generated code fails, debugging can be harder because you didn’t write it. You have to reverse-engineer the logic.

Overconfidence risk: Working code isn’t the same as correct code. VibeCoding can create a false sense of progress if you’re not testing thoroughly.

We treat these as known constraints, not reasons to avoid the approach. Every tool has limitations. The goal is to use tools appropriately.

What We’ve Learned

After several months of using VibeCoding in our workflow, 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 net benefit is positive, but it’s not magic.

Discipline matters. It’s tempting to ship VibeCoded prototypes directly to production. Resist this. The technical debt compounds quickly.

Know when to stop. If you’re on your fifth iteration 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.

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

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