Can a Beginner Code an App? I Tried Cursor & Replit
▼ Summary
– The author, a non-programmer, attempted to use AI coding tools like Cursor, Replit, and Microsoft Visual Studio to build a data analysis app by describing goals in natural language.
– While these tools can automate complex setup tasks like installing libraries and creating file structures, they often require tedious manual command-line work and have reliability issues, such as Cursor losing chat history.
– Cloud-based platforms like Replit and Lovable quickly hit free-tier credit limits, forcing upgrades, and raise data privacy concerns by requiring users to upload private data to remote servers.
– The experiment succeeded in creating a basic app with Lovable, but revealed that generating meaningful analysis requires deep conceptual understanding of the desired function, not just automated coding.
– The experience highlighted that AI tools assist with implementation but cannot replace the need for human programming intuition, product design decisions, and the management of technical environments.
The journey from a simple idea to a functional application is now being reimagined by a wave of AI-powered coding assistants. These tools promise to translate natural language into working software, potentially opening the door for non-programmers to build their own apps. To test this promise, I embarked on a personal experiment using several leading platforms, including Cursor and Replit, to see if a beginner could truly code an app through conversation alone.
My goal was to create a custom data analysis tool for my newsletter’s archive. I envisioned a web interface where readers could go beyond simple keyword searches to ask complex, free-form questions about the themes and narratives within over 1,700 articles. This would provide insights that general AI chatbots couldn’t, as they lack access to my proprietary content.
I began with Cursor, drawn by its reputation. The initial experience was nothing short of exhilarating. After describing my project in plain English, the agent panel sprang into action, outlining requirements, proposing a file structure, and even offering to install the necessary Python libraries to parse Apple’s .Pages document format. For a moment, it felt like magic. However, the excitement was short-lived. After restarting to complete an installation, my entire chat history, the blueprint of our plans, vanished without a trace. This critical flaw, where the chat history is essential for project continuity, proved to be a deal-breaker and forced me to abandon Cursor entirely.
Shifting to the cloud, I tried Replit. The setup was impressively fast, generating a basic app preview within minutes. Yet, I quickly encountered the limitations of free tiers. After my first substantial query, I hit a usage quota and was locked out for 24 hours unless I upgraded to a paid plan. The paywall in cloud-based platforms can arrive before you have a working prototype, making iterative development frustrating for a beginner on a budget.
Seeking a local solution again, I turned to Microsoft’s Visual Studio with GitHub Copilot. While it eventually got a prototype running on my local server, the process involved tedious back-and-forth with the command line. More importantly, the result was a basic text-matching tool, not the intelligent analysis engine I wanted. Refining it proved impossible without a deeper understanding of what constitutes meaningful textual analysis. This highlighted a core challenge: you must understand what you want to build before you can effectively guide the AI to build it.
Finally, I experimented with Lovable, which offered the most streamlined, ChatGPT-like experience. It quickly built a web interface and even integrated Google Gemini for higher-level analysis. However, to make meaningful progress, I had to upgrade to a paid plan and switch from problematic .Pages files to an XML archive hosted on Algolia. After this investment of time and money, I achieved a rudimentary semantic analysis app, a minimum viable product, but one that still felt far from my original vision.
This experiment revealed several truths. AI coding tools are powerful for automating setup and generating boilerplate code, handling tasks that would stump a novice. Yet, they exist in a middle ground between cumbersome local IDEs and costly cloud platforms. Desktop tools like Cursor and Visual Studio introduce complexity with terminals and environment management, while cloud services like Replit meter your progress and raise data privacy concerns.
Ultimately, the experience gave me a profound appreciation for the work of programmers and product teams. While AI can automate lines of code, the intuition, problem-solving, and architectural decisions required for serious development remain firmly human domains. These tools are incredible assistants, but they are not replacements. For a beginner, they can help you start the journey, but be prepared for a steep learning curve, unexpected costs, and the realization that bringing a sophisticated app idea to life still requires a significant investment of thought and effort.
(Source: ZDNET)
