Why We Built an AI Experimentation Experience That’s Simpler Than a Visual Editor

At Mida, our mission has always been to make experimentation simple. For a while, we thought we had. Our visual editor was a huge step forward, making it easy for anyone to change text, swap colors, or test design tweaks, all without writing a single line of code. It successfully lowered the barrier, empowered more teams, and sped up testing cycles.

But we learned that simplicity is a moving target.

The Real Challenge With “Simple”

The hard reality is that every website is different. Some are clean and structured; others are complex, dynamic, and layered with legacy code. We realized that even the most intuitive visual editor can't abstract away the inherent complexity of the modern web. To run a meaningful test, you still need to understand how websites work: HTML structure, CSS selectors, targeting logic, event triggers, and audience segmentation.

For non-technical users, this is still a massive blocker. And that’s not what “simple” should ever feel like.

The Problem With Visual Editors

Visual editors were supposed to remove friction. They were designed to give non-developers a way to test their own ideas, faster. And they do, but only up to a point.

The moment you want to move beyond small visual tweaks, the cracks begin to show. How do you test a change inside a complex, single-page application? What about targeting dynamic components that load based on user interaction? How do you inject a new promotional banner above a hero that’s also part of a carousel?

Suddenly, the "no-code" user is deep in the weeds. You either have to file a ticket and wait for a developer, or you start bending the tool in ways it was never meant to work, leading to brittle, broken experiments.

Lately, we’ve seen a creative but telling workaround emerging. Some users are turning to ChatGPT to generate small, customized snippets of JavaScript or CSS to paste into our editor. It’s an ingenious solution, and it can work. But it also introduces a new, time-consuming loop of debugging, testing, and cross-browser validation. This is especially risky when you don’t fully understand what the generated code is doing under the hood.

That approach works for a specific type of person that is curious, semi-technical, and patient enough to troubleshoot. But for most product and marketing teams, it’s still too much friction. This trend was a flashing red light for us. It proved that users were desperate for more power, but the visual editor wasn't the right interface to deliver it.

It made us ask: What if we could make that entire process happen seamlessly, right inside Mida?

Simplicity, Reimagined

So, we decided to start fresh. We threw out the old assumptions about what an experimentation tool should look like.

What if, instead of clicking and dragging boxes in a visual editor, you could just tell the platform what you wanted to test?

That’s what we built: an AI-powered experimentation experience. You don't interact with a complex UI; you simply describe the change you want to make in plain English.

“Test a new layout for the product cards on the category page, making the image 50% larger.”

“Change the headline on the pricing page and add a 'Most Popular' badge to the Pro plan.”

“Add a promotional banner with a black background and a 'Shop Now' button above the main hero section.”

In minutes, Mida’s AI acts as a translator. It understands your intent, analyzes the page structure, and generates everything you need, the production-ready variant code that ready to launch.

This means complex changes, new designs, and interactive elements are no longer multi-day development tasks. They are literally built and ready to test in minutes.

Built for Speed and Learning

As a developer, I was genuinely surprised by what it could do. Experiments that used to take me or my team a full day of careful coding and testing now happen in a single prompt. This shift changes everything about how teams operate.

Growth, marketing, and product teams can now turn their ideas into live experiments instantly. They no longer have to wait for developer bandwidth or fight for a slot in the next sprint. They can act on their curiosity right now.

And for developers, this isn't about replacement. It's about relief.

Instead of spending hours wiring up A/B test variants or debugging finicky selector targeting, they can focus on higher-impact work: building the core product, optimizing platform performance, or tackling complex architectural challenges. The AI handles the repetitive, low-leverage experimentation work, freeing everyone up to solve bigger problems.

When ideas move faster, learning moves faster. The feedback loop between an idea and its real-world impact shrinks from weeks to minutes.

Why This Matters

Experimentation has never just been about testing button colors. It’s about building a culture of learning, where every team can explore ideas, validate hypotheses, and make decisions based on insight, not intuition.

But in most organizations, that culture gets stuck in a queue. Teams are constantly waiting: waiting for developer resources, waiting for back-end changes, waiting for their idea to "fit in the sprint." This friction is toxic to a culture of learning.

That waiting doesn't just delay a single test; it kills the entire team's learning velocity. Curiosity fades, and "good enough" becomes the default.

We are building tools to remove those walls. Our goal is to make high-quality, high-velocity experimentation accessible, fast, and scalable for everyone on the team, not just the ones who can code.

What’s Next

This launch is a significant step toward that mission. We’ve built an AI-powered experimentation experience that keeps things truly simple, not by limiting what’s possible, but by removing the technical friction that makes testing hard in the first place.

Because simplicity shouldn’t mean restriction.

It should mean clarity, speed, and freedom, for anyone to experiment, learn, and grow. This is the new baseline for experimentation, and we're just getting started.