OnSpace.ai Hands-On Review: Building and Publishing an App to App Stores with Zero Coding

Introduction: Can You Build an App Without Knowing How to Code?

Building a mobile app and publishing it to the App Store or Google Play without any programming knowledge — this was hard to imagine just two years ago, but with the explosive growth of AI development tools, it's now a reality.

The OnSpace.ai platform I tested today allows anyone to complete the entire app development process from concept to store listing using nothing but natural language descriptions. What's more, the platform is completely free to get started.

OnSpace.ai Platform Overview

OnSpace.ai is an AI-powered no-code app development platform that supports creating iOS and Android apps, websites, and general-purpose AI agents.

Technical Background of No-Code Platforms: The rise of No-Code and Low-Code development platforms is essentially a response to the democratization of software development. Traditional mobile app development requires mastering Swift/Objective-C (iOS) or Kotlin/Java (Android), plus UI frameworks, backend APIs, database design, and an entire technology stack — an extremely steep learning curve. AI-driven no-code platforms like OnSpace typically use Large Language Models (LLMs) under the hood to convert natural language requirements into executable application logic, then generate code compatible with both iOS and Android through cross-platform frameworks (such as React Native or Flutter). This technical approach allows non-technical entrepreneurs to bypass the engineering implementation layer and focus directly on product logic itself.

Its core advantages include:

• Zero-code development: Generate complete applications through natural language descriptions
• One-stop deployment: Built-in workflow for publishing to App Store and Google Play
• Rich integration capabilities: Supports user authentication, databases (Supabase), AI integration, Stripe payments, and more
• Free to use: Basic features require no payment

About Supabase Integration: Supabase is an open-source Backend-as-a-Service (BaaS) platform built on PostgreSQL, often called the "open-source alternative to Firebase." It provides real-time databases, user authentication, file storage, and edge functions — developers can access complete backend capabilities without building and maintaining their own servers. OnSpace's integration with Supabase means user data, app content, and business logic can all be persistently stored, rather than remaining at the frontend demo level. For no-code platforms, the advantage of choosing Supabase over a self-built database lies in its Row Level Security (RLS) policies, which control user data access at the database level — particularly crucial for multi-user SaaS applications.

According to official showcases, users have built and successfully published an app within 24 hours using this platform. This is quite rare among similar AI development tools — most platforms stop at the code generation stage, while OnSpace bridges the last mile from development to deployment.

Hands-On: Building an AI Ad Creative Generator App

Optimizing Prompts with ChatGPT

Before using OnSpace, here's a practical tip: use ChatGPT first to refine your requirement descriptions. The goal of this test was to build an "AI Ad Creative Generator" with the following core features:

1. Upload a product photo (phone, water bottle, T-shirt, etc.)
2. Automatically remove the background
3. Generate product showcase images in multiple styles
4. Generate short video ad materials

The specific approach is to describe your requirements in detail to ChatGPT, have it output a structured prompt, and then paste it directly into OnSpace. This "prompt engineering" method significantly improves the quality of AI-generated results.

The Principles of Prompt Engineering: Prompt Engineering refers to the methodology of carefully designing input text to guide large language models toward more accurate and structured outputs. The core principle is that LLM output quality is highly dependent on the completeness of input context and clarity of instructions. A vague requirement description (like "make an ad app") forces the model to fill in numerous assumptions, causing results to deviate from expectations. In contrast, a structured prompt that includes feature boundaries, user flows, and technical constraints can significantly narrow the model's interpretation space and improve generation quality. The technique of using ChatGPT to preprocess requirements is essentially leveraging one LLM to optimize the input for another LLM — this kind of "model collaboration"