From Chat Tools to Platforms: How Vibe Coding Lets Non-Engineers Build Apps with AI

Introduction

Vibe coding is finally reaching non-engineers. Until recently, most people who heard "you can build apps with AI" and gave it a try ended up stuck somewhere along the way.

Chat interfaces would generate code that looked promising. But actually running it required environment setup and configuration. When errors appeared, there was no way to understand what went wrong.

Even when something worked locally, the process of deploying and sharing it created another wall. As a result, chat-based vibe coding felt like developer territory—too technical for non-engineers to navigate.

So why has this changed now? The answer isn't just improved model performance.

Vibe coding tools themselves have evolved through three stages. First came chat-based code generation. Then in-IDE interactive development. Now platform-based development.

Each stage absorbed barriers that blocked non-engineers—environment setup, execution and debugging, deployment and sharing.

This article traces these three approaches as an evolution and explains which obstacles each phase removed. Read it as a guide to understanding why AI coding is becoming a realistic option for non-engineers today.

What Is Vibe Coding?

This article defines vibe coding as a development approach where AI acts as a partner. You describe what you want in natural language, and AI generates code based on your requirements.

The key isn't simply that code appears automatically. The real value lies in the process itself.

You communicate your intent via a natural language prompt. AI then generates, revises, and improves the code through ongoing dialogue. The experience feels like "describing what you want to build with AI, and watching the output evolve."

This approach has become a global movement. Model performance has improved, but that's not the only reason. The surrounding experience—IDE integration, execution, deployment, and sharing—has matured. This has dramatically expanded who can actually try it.

A symbolic moment that sped up this movement was the birth of the term itself. Andrej Karpathy, a founding member of OpenAI, coined "vibe coding" in his February 2025 post on X. It has since become a worldwide buzzword.

This trend has proven to be more than a passing hobby. Collins Dictionary selected "vibe coding" as its Word of the Year 2025. This shows how broadly the concept has entered public awareness.

On the market side, Lovable—a leading AI app builder—raised a $200 million Series A in July 2025, valuing the company at $1.8 billion (unicorn status). Just five months later, Lovable raised a $330 million Series B in December 2025, pushing its valuation to $6.6 billion. Companies clearly recognize the value, and significant capital is flowing into this new market.

Phase 1: Code Generation in Chat Tools

The starting point for vibe coding was the experience of using code tools within chat interfaces. Prime examples include general-purpose AI coding assistants like ChatGPT and Gemini. The essence of this phase was that conversation—not development environments—became the entry point.

What became possible at this stage was simple but powerful. You describe the feature you want to build in plain language. The AI then generates code along with design scaffolding for implementation.

Through a simple chat, you could quickly get web app boilerplate, API designs, database schemas, SQL queries, and small scripts for daily tasks. All of these came out in "good enough" form. For non-engineers, this meant leaping past the "I don't know where to start" or "the first step feels too heavy" barrier.

However, one major hurdle remained for non-engineers: the execution environment.

Even when chat generated code, running it required extra work. You had to install dependencies, configure environment variables and credentials, and set up data storage. These prerequisites went beyond the code itself, and users had to prepare them on their own.

Many users got their code generated but couldn't figure out how to actually run it.

Still, the value this phase created was enormous. The biggest impact was a dramatic reduction in learning costs. Without specialized knowledge, you could articulate your intent, get a starting point, and refine through conversation.

This phase sped up "getting something to exist" rather than writing code from scratch. It widened the gateway to software creation. The next phase would push this opening toward practical use.

Phase 2: Interactive Development Within the IDE

In the next phase, vibe coding evolved to work entirely within development environments. Prime examples are Cursor and Claude Code—AI assisted tools that integrate directly into the editor.

Think about the old workflow. You generate code in chat. You copy it. You paste it into an editor. You run it in the terminal. You hit an error. You go back to chat for help.

Now developers could skip all that. They could talk to AI right inside their editor. They could build, fix, and test without switching tools.

This phase first solved the back-and-forth friction. The "chat → copy-paste → execute" loop could now happen within a single environment.

More importantly, AI could now see the entire codebase. It could view multiple file structures and dependencies, then propose and apply changes accordingly. This reduced problems like "the code looks right but doesn't fit the existing structure" or "it conflicts with other files." AI could now make context-aware changes.

Error resolution also became faster. With access to logs and files as a given, tracking "where and what went wrong" became easier.

Phase 1 often left causes invisible. This phase provided observable information—logs, configurations, file structure. This enabled the fix-verify loop to actually turn.

As a result, vibe coding advanced from "producing code through conversation" to "progressing development through conversation."

Yet the biggest barrier for non-engineers—local environment setup—remained. Even if something worked locally, moving to execution, deployment, and sharing raised the difficulty again. This included URL generation, permissions, team usage, and long term operation. This phase was revolutionary for developers, but for non-engineers, it still tended to require development knowledge.

Phase 3: Platform-Based Development

What proved decisive for vibe coding reaching non-engineers wasn't AI capability itself. It was the shift in who provides the execution and sharing environment.

In previous phases, users had to handle environment preparation and publishing on their own (locally). Platform-based approaches—often called no-code AI app builders—have the service provider prepare this infrastructure in advance. This way, vibe coding becomes not just code generation but a complete flow through execution, publishing, and sharing. Prime examples include Squadbase, Lovable, and Replit.

The decisive change in this phase was that platforms absorbed the barriers that persisted through Phases 1 and 2.

First, environment setup becomes unnecessary. From the moment you sign up, the execution environment is ready. This dramatically reduces friction around dependency installation and runtime configuration.

Many platforms offer a free tier, making it easy to start experimenting without commitment. While environment variables and credentials may still need configuration, platforms provide settings interfaces and guided procedures. This makes progress easier than fumbling through local setup.

You can run what you build directly on the platform and share it via URL. Whether it's a dashboard, an AI app, or even a landing page, sharing becomes instant.

These AI features extend beyond simple code generation. Modern app builders now support real time preview, instant deployment, and connections to external services like Google Sheets. The product also includes permissions management and team collaboration features built in. This saves time compared to setting these up manually.

Platform-based tools have also moved beyond "build from scratch." They offer templates and connectors that provide "ready to run" starting points for specific purposes.

Some platforms even integrate AI agents that can handle multi-step tasks on their own. For non-engineers, the value may lie less in "freedom to write code" than in starting from a state already close to their goal.

As a result, non-engineers can reach something close to their ideal faster.

As a next step, if you're unsure where to start, we recommend beginning with a platform-based approach.

First experience building and sharing something that actually runs. Non-engineers can complete many use cases entirely within a platform.

Start by actually trying it. Ask yourself what you want to build and how you want people to use it.

If you want to quickly build BI that drives decisions through dialogue and deliver it to your team, Squadbase is an excellent starting point. Squadbase is a platform focused specifically on BI (dashboard) development among vibe coding platforms. It offers the shortest path from data preparation to charts and sharing.

Squadbase is particularly effective if you:

• Have BI tools that have become rigid and can't keep up with changing metrics or perspectives
• Experience slowed decision-making due to waiting on engineers or analysts
• Want to build "something that works" yourself or with your team, then iterate while sharing
• Want to spend time on analysis and improvement, not dashboard construction

Sign up for Squadbase and experience "building BI through dialogue" for yourself.

We've also prepared a detailed guide comparing major vibe coding platforms—including Squadbase, Lovable, and Replit—covering their strengths, ideal use cases, and extensibility from a business perspective. If you're unsure which one to choose, check it out as well.