I set out to “vibe code” an app from scratch using plain English, curious whether Cursor and Replit could turn a beginner’s intent into a working product. The result was illuminating: both platforms made setup feel magical, but the path from prototype to meaningful functionality still demanded skills, structure, and patience.
What Vibe Coding Promises for Beginners and Pros
“English is the new programming language,” Nvidia’s Jensen Huang likes to say, and modern agentic IDEs are built around that idea. You describe the app; the agent plans, scaffolds files, installs dependencies, spins up a local or cloud server, and shows a live preview. It’s a powerful abstraction of the build loop.
Industry data supports the hype but also the caveats. GitHub has reported task-time reductions of up to 55% in controlled studies with AI-assisted coding, while Stack Overflow’s latest Developer Survey indicates strong interest alongside persistent concerns about correctness and security. The tools are accelerators—not autopilots.
Cursor Taught Me Setup Speed And Fragility
Cursor impressed immediately. It unpacked my vague brief for a document-analysis app, outlined requirements I hadn’t considered, and proposed a full file structure with Python libraries and a local UI. Watching it pull in parsers and bind services felt like having a senior engineer handle boilerplate.
Then reality intruded. I was bounced into a terminal to approve commands and resolve environment issues—a mild speed bump for a power user, a brick wall for a newcomer. Worse, a restart wiped my chat history, erasing the agent’s design rationale and prior context. For vibe coding, history is the memory of the project; when it disappears, so does momentum.
The big lesson from Cursor: agents are phenomenal at scaffolding, but beginners need reliability features—durable chat logs, reproducible environments, auto-recovery—just as much as code generation. Without them, you spend more time re-explaining than building.
Replit Taught Me Convenience With A Meter
Replit delivered the opposite trade-off: near-zero setup friction in the browser, fast bootstrapping, and a ready preview. For a first-timer, that smooth on-ramp is energizing, and the agent did a credible job mapping my goals to a minimal web app.
But cloud convenience comes with a meter. As the agent refined code and retried file ingestion, I burned through free credits quickly, hit quotas, and faced a pause or a paywall. I also hesitated to upload private documents, a common concern flagged by enterprise security teams and reflected in guidance from organizations like OWASP.
The big lesson from Replit: it’s great for rapid, low-friction experiments, but you need to budget for iteration costs and design around data privacy from day one. For beginners, compute and token usage are invisible until they aren’t.
What AI Agents Help With And What They Miss
Both tools excelled at the “unfun” parts: creating directories, wiring dependencies, launching servers, and drafting UI scaffolds. They turned an hour of setup into minutes, and that’s real value.
Where they struggled was product definition and domain nuance. My goal wasn’t keyword matching; it was thematic analysis across many articles, including proprietary file formats. The agent could guess at a solution, but robust text analytics required decisions about ingestion formats, chunking, embeddings, evaluation metrics, and latency trade-offs—decisions I had to own.
This gap mirrors what research groups like McKinsey have noted: genAI speeds execution of well-specified tasks, yet outcomes still hinge on problem framing and system design. The agent can write code; it won’t write your product requirements.
Beginner Playbook for Cursor or Replit Users
- Start with clean data formats. Proprietary files create brittle parsing steps; stick to plaintext, CSV, DOCX, or PDF unless you have a strong reason not to. Ask the agent to generate ingestion tests so you can validate parsing before building features on top.
- Lock the environment. In Cursor, request a manifest of dependencies and a one-command bootstrap script. In Replit, track agent actions in a build log and snapshot states before big changes. Reproducibility saves beginners from “works on my machine” loops.
- Budget iteration. Assume multiple cycles of retries, model calls, and container restarts; monitor token usage and compute time. A simple burn-rate rule of thumb helps: prototype on small datasets, test in batches, then scale.
- Instrument early. Ask the agent to add logging around data ingestion, prompt I/O, and errors. When things break—and they will—you need observability more than another guess.
Bottom Line: AI Agents Speed Setup, Not Product Vision
Cursor and Replit made me faster, but they also made clear where human judgment remains non-negotiable. The tools are terrific at clearing brush; they won’t decide what to plant, or how to measure a good harvest.
For beginners, vibe coding can absolutely get you to “something that runs.” Turning that into “something that matters” still requires choosing the right data formats, enforcing reproducibility, budgeting the cloud meter, and articulating product goals with precision. The agent is your power tool; you’re still the builder.