Vibe Coding: What Could Go?Wrong?

Introduction

The past few years have seen a surge in AI-assisted coding tools that promise to turn plain-language ideas into working code. From GitHub Copilot to new agents like Cursor and Replit Agent, these tools appeal especially to non-technical or novice developers who want to “talk” an app into existence.?

The trend, often dubbed “vibe coding,” gained popularity in early 2025 as large language models became mainstream for development. Enthusiasts imagine building full-stack applications by simply describing features to an AI. Why painstakingly learn programming or software architecture when an assistant can generate code snippets and even entire functions on demand? The appeal is real?—?AI coding promises to dramatically lower the barrier to entry for software creation.

AI-assisted coding can feel magical?—?as if code appears at your fingertips without deep programming knowledge. However, this convenience comes with hidden risks if you rely on “vibes” alone.

To explore this, let’s consider a case study: the Aerial Finder application. Aerial Finder is an ambitious full-stack project built by a non-technical developer riding the vibe coding wave. It aims to integrate mapping, data, and machine learning in one package?—?precisely the kind of app many dream of creating with AI help.?

We’ll use Aerial Finder’s journey to illustrate how relying too much on AI code generation without complete understanding can lead to serious pitfalls.

?From misconceived Docker setups to misconfigured machine learning models, we’ll break down what could go wrong when “the vibes” take over the development process. The goal isn’t to discourage using AI assistants?—?it’s to highlight the areas where human developers must stay vigilant, even when an AI is writing the code, to avoid production nightmares like this:

Aerial Finder Application Overview

Aerial Finder is a hypothetical full-stack web application that combines interactive maps, cloud data, and machine learning, let's say it's a clone of Spaceknow (www.spaceknow.com).?

The feature set is broad: users can log in via OAuth, view a map with marked locations, upload or view aerial images (like drone photos or satellite snippets) for those locations, and get insights from an ML model about what’s in the image. The tech stack for this project is modern and extensive:

• Frontend: Built with TypeScript and Next.js (a React framework), providing server-side rendering and a snappy React UI. The interface uses Mapbox maps with a Leaflet library integration to display interactive maps.
• Backend/API: A GraphQL API (using something like Apollo Server or Next.js API routes) serves data to the frontend. The backend uses Mongoose to interact with a MongoDB database, storing user info and location/image metadata. GraphQL was chosen for its flexible querying, enabling the client to request exactly the data it needs.
• Authentication: User auth is handled via next-auth (an OAuth authentication library for Next.js). In development, a GitHub OAuth app provides login; in production any OAuth provider could be used. This means secure token exchange and session management across the app.
• Machine Learning Integration: The app uses TensorFlow (likely TensorFlow.js or Python TensorFlow via a separate service) to perform machine learning tasks. For example, it might run an image classification model on uploaded aerial photos to detect features (buildings, water, vegetation, etc.) and then store or display those results.
• Testing: Jest is set up for automated testing of both front-end components and back-end logic. Snapshot tests check that UI components render correctly, and some unit tests ensure the data logic works as expected.
• Deployment: Everything is containerized with Docker. There are separate Docker containers for the backend server (Node/Next.js environment with the API and ML runtime) and the frontend (though in Next.js these can be one and the same deployment). Docker ensures the development and production environments are consistent. Eventually, the containers are deployed to a cloud service, and a CI/CD pipeline builds and ships new images on updates.

The concept of Aerial Finder involves mapping points of interest on a world map?—?much like dropping pins on a physical map. The app’s frontend uses Mapbox and Leaflet to let users explore locations and their data (analogous to the pins and annotations on the map above) and finding interesting objects.

How Aerial Finder Works: When a user visits Aerial Finder, they can browse a map of locations (for example, marked spots of interesting aerial views). The Next.js frontend renders a list of locations and a map component. The map, powered by Mapbox, shows pins for each location. Clicking a pin brings up details?—?perhaps an image taken from a drone or satellite and some description. Here the GraphQL backend is in action: the frontend queries the GraphQL API for location data (coordinates, name, description, etc.) and for any analysis results. If the user is logged in (via next-auth OAuth), they might have additional options like saving favorite locations or uploading a new image.

Behind the scenes, when a new image is uploaded for a location, the backend could invoke the TensorFlow model to analyze it. For instance, the model might return “contains building: 80% confidence, contains water: 20%” which the app would store in MongoDB via Mongoose. Aerial Finder could then display these ML results on the frontend (e.g., showing icons on the image or listing detected features). All these operations?—?database writes, model inference?—?are exposed through GraphQL mutations and queries.

Finally, the entire app is packaged in Docker images for deployment. One container might run the Next.js server (serving both the API and the frontend) and another container could run a separate process if needed (for example, a worker for ML tasks, or maybe the Next.js container covers it all). Docker ensures that Node, Mongo (if included), and required libraries like TensorFlow are all correctly installed and configured. In development, Docker Compose might be used to spin up the app along with a MongoDB service, so the developer doesn’t have to install databases locally.

In summary, Aerial Finder touches every part of full-stack development: UI/UX, backend logic, database, third-party APIs (Mapbox, OAuth providers), ML processing, testing, and deployment.?

It’s exactly the kind of project a solo “vibe coder” might attempt with the aid of AI coding assistants. Now, let’s examine how relying on AI for each of these aspects?—?without proper understanding?—?can lead to trouble.

The Pitfalls of Vibe?Coding

“Vibe coding” refers to the practice of leaning heavily on AI assistants to generate code, going with the flow of AI suggestions without fully understanding or structuring the code oneself. A vibe coder might describe a feature in plain English to an AI and accept whatever code snippet is returned, stitching features together by iteratively prompting the AI. It’s an enticing shortcut: why spend hours debugging configuration or reading docs when the AI can spit out an answer in seconds?

However, this approach has significant pitfalls. One common issue is the illusion of correctness?—?the code “mostly works” but hides subtle problems. For example, an AI-generated function might work for a demo but fail for edge cases or larger scale. Since the developer didn’t write the code, they may not catch inefficiencies or errors lurking under the surface. As one observer quipped, “vibe coding is all fun and games until you have to vibe debug”. When something breaks, tracing a bug in code you don’t fully understand is challenging.

Quality and maintainability of AI-generated code can also be poor. AI might produce non-idiomatic or overly complicated code that passes initial tests but becomes a nightmare to extend. I personally think that while vibe coding is great for quick prototypes, using it for production code is “clearly risky”?—?you can end up with a codebase no one truly understands. If Aerial Finder was built by copy-pasting AI outputs, as it grows (more features, more contributors), the lack of clear structure could hamper development. Future developers might scratch their heads at unusual patterns that the AI introduced.

Another major concern is security and reliability. AI coding assistants do not inherently understand the security context of your application. They might generate code that, for example, doesn’t properly sanitize user input (leading to injection vulnerabilities) or that uses outdated cryptographic practices. In Aerial Finder, an insecure GraphQL endpoint generated by AI could expose user data or allow unauthorized actions if authentication checks are omitted. The AI won’t automatically enforce best practices?—?it might happily give you a working query resolver that accidentally allows querying any user’s data. If the vibe coder isn’t knowledgeable about security, these issues can slip by until a breach happens.

There are also misconceptions that non-technical users might have when vibe coding. One is assuming the AI can handle complex tasks end-to-end. For example, the developer of Aerial Finder might ask the AI, “create a TensorFlow model to detect buildings in images,” and expect a ready-to-use solution. The AI might output some model architecture code, but it’s not a trained model?—?without real training data and tuning, that code won’t magically solve the problem. Similarly, one might think AI can set up the entire CI/CD pipeline or Docker config flawlessly. In reality, AI might produce a generic Dockerfile or GitHub Actions script that doesn’t fit the app’s specifics, leading to deployment failures. Another misconception is thinking that if the AI-generated code runs once, it’s production-ready. Issues of scaling (will this approach still work with 10x users or data?), logging, error handling, and maintainability often require human foresight which an AI, focused only on the immediate prompt, might not provide.

Finally, over-reliance on AI leads to skill atrophy. If a new developer uses vibe coding to avoid the “hard stuff”?—?like learning how databases or authentication actually work?—?they become dependent on the AI for every fix or new feature. The moment the AI gives an incorrect suggestion or something odd occurs, they’re stuck. It’s crucial to remember that AI is a partner, not a replacement for understanding. As one TechCrunch piece humorously highlighted, when a user pushed Cursor to generate code for an hour, the assistant eventually refused, telling him “you should develop the logic yourself… to ensure you understand the system and can maintain it properly”. In other words, even the AI knew the human needed to learn coding fundamentals! (https://techcrunch.com/2025/03/14/ai-coding-assistant-cursor-reportedly-tells-a-vibe-coder-to-write-his-own-damn-code/)

In summary, vibe coding can accelerate development and empower non-coders, but blindly trusting AI output is dangerous. Next, we’ll dissect each stage of Aerial Finder’s development and see exactly what can go wrong with a vibe-driven approach?—?and how to avoid those issues.

Step-by-Step Analysis of Full-Stack Development and Vibe Coding?Risks

Let’s break down the development of Aerial Finder into key steps and examine potential pitfalls if one relies on AI generation (vibe coding) without adequate understanding. For each step, we’ll identify the risk, why it might happen when using AI assistance, and how to mitigate it:

The table above highlights that at each step of development, blindly trusting AI-generated code can introduce issues. A Dockerfile that “looked fine” could be based on the wrong Node image, causing mysterious crashes in production. A GraphQL resolver that works for one query might inadvertently expose data if you don’t think about authorization.?

The cure in each case is the same: augment AI’s speed with your own understanding. In practice, that means testing the AI’s output, reading documentation, and not skipping fundamental setups like security or error handling.

For instance, if Cursor or Replit Agent writes a chunk of code, take a moment to ask “Why does this work? What happens if X goes wrong?” If you can’t answer, have the AI explain the code to you, or do a quick manual tweak to handle that scenario. This way you remain in control of the architecture and stability of your app, using the AI as a helper rather than an all-knowing oracle.

The Myth of AI-Generated Machine Learning?Models

One particularly pervasive myth in vibe coding is that an AI coding assistant can single-handedly create a working machine learning model for you?—?as if by magic, it will conjure a sophisticated AI within your app. This misconception likely arises from seeing AI generate code for model architectures or reading about AutoML tools. Let’s set this straight: AI coding assistants cannot automatically train, optimize, and deploy a novel machine-learning model for your specific problem.

What they can do is generate code templates. For example, you could ask, “Create a TensorFlow model for image classification,” and the AI might output a snippet defining a neural network class in Python, or some layers in TensorFlow.js. But this is only a starting point. The real work of ML is training that model on actual data, evaluating its performance, tweaking hyperparameters, and iterating?—?none of which the AI can do for you in a single shot.?

As an experiment, try prompting ChatGPT to build a complex ML pipeline; it produced code, but when asked to refine it for errors and better logic, it struggled to improve and often repeated mistakes. The AI lacks the ability to reason over iterative experiments or learn from errors in the code it wrote.

Training a model requires a dataset. No AI assistant can pull a perfect, domain-specific dataset out of thin air (unless you provide it).?

In Aerial Finder’s case, to detect features in aerial images, you’d need hundreds or thousands of labeled examples (for instance, images labeled with “building” or “no building”). The AI won’t handle collecting or labeling this data for you. At best, it might suggest using a known public dataset?—?but integrating that and training still falls on the developer.

Another aspect is optimization. Suppose the AI gives you a neural network architecture. Is it the right size? Will it overfit or underfit your data? Those questions are answered through experimentation.?

A human ML engineer will adjust learning rates, try different network depths, or use cross-validation?—?tasks requiring judgment and iterative loops. AI code tools don’t truly understand the data or the problem context; they can’t intelligently choose one algorithm over another beyond surface patterns learned from textbooks or StackOverflow.?

Indeed, when tasked with a complex adjustment, ChatGPT often could not determine the correct specific steps without user feedback and kept producing the same error.

Finally, deploying an ML model has its own challenges. If you manage to train a model to a satisfactory accuracy, you need to serialize it (save weights), load it in your production environment, and ensure it runs efficiently (possibly needing GPU acceleration or converting to a lighter format like TensorFlow Lite). An AI assistant won’t automatically set up your model serving infrastructure or monitoring for model drift.?

These fall under MLOps, which currently require considerable manual setup.

In short, AI coding tools are fantastic for generating boilerplate ML code and even explaining ML concepts, but they do not eliminate the need for traditional ML development steps. If you ask an AI to “create a machine learning model for X,” be prepared that the answer will be some code that likely needs substantial work to become a real, trained model embedded in your application. The best approach is to use AI to accelerate writing known pieces (like a PyTorch training loop or a data preprocessing function), but treat the overall ML model creation as a serious project where you’ll be in the driver’s seat for data and training.?

Always validate the outputs from an AI-generated model code?—?does it compile? Does it run on a sample of real data without errors? If it runs, are the predictions reasonable or just random? Without this diligence, you might deploy an “AI feature” in your app that, in reality, does nothing useful or, worse, produces incorrect data that misleads users.

Best Practices for Non-Technical Developers

If you’re a non-technical developer enticed by AI coding tools (and who isn’t, given their promise?), the good news is you can build meaningful projects like Aerial Finder. But you’ll need to supplement the AI’s assistance with some learning of your own. Here are some best practices and skills to focus on to avoid turning your dream project into a maintenance nightmare:

• Learn the Fundamentals (even if just at a high level): Make a checklist of the major technologies in your stack and ensure you understand their roles. For Aerial Finder, that list would be: Docker, Next.js/React, MongoDB, GraphQL, OAuth, TensorFlow, Jest, CI/CD pipeline. You don’t need to become an expert in each but know what each is responsible for. For example, know that Docker is for containerizing your app (and roughly how it does so), know that OAuth involves token exchange and redirect URLs, and know that GraphQL isn’t automatically secure?—?you must implement auth checks. This high-level awareness will help you spot when AI output might be missing something important.
• Use AI as a Tutor, Not Just a Code Generator: When the AI provides code, ask follow-up questions. “Explain how this Dockerfile works.” “What does this error message mean?” This turns your coding session into a learning session. Over time, you will start anticipating what the code should look like. The AI can also quiz you or clarify docs. By staying curious about the why behind the code, you ensure that you’re leveling up your skills instead of just copy-pasting blindly.
• Consult Official Documentation: AI outputs can sometimes be outdated or slightly off. When implementing critical pieces (database connection, auth configuration, deployment), always cross-check with official docs or well-known tutorials. For instance, after getting an AI-generated next-auth setup, skim the next-auth documentation for a quick sanity check?—?you might spot a config or option the AI missed. Similarly, use framework examples: Next.js has example projects, Apollo GraphQL does, etc. These can serve as a reliable template to compare against AI code.
• Small Manual Tweaks Go a Long Way: You don’t have to hand-code everything, but be ready to write or modify code around the AI’s output. Maybe the AI gives you a GraphQL resolver that lacks an auth check?—?you can add a few lines to verify a JWT or session. Or the AI’s Dockerfile might not copy a needed file; you can fix that yourself once you recognize the issue. These small interventions require understanding the problem rather than brute-force coding skills.
• Testing, Testing, Testing: Embrace testing and debugging as your safety net. Write a few unit tests for each critical piece, or at least do manual testing for various scenarios. If you generated code with AI and it works for the simple case, try a harder case next. For example, create two user accounts and ensure one cannot access the other’s data in your app. If you find a bug, try to figure it out (with AI help if needed) rather than ignoring it. This not only improves your app but deepens your understanding. Remember, if something is broken and you have no tests, you might not know until a user complains.
• Security Mindset: Always assume your application will have malicious users (or at least curious ones who poke around). For any feature the AI builds, ask “what if someone tries to misuse this?” If AI built a file upload feature, consider: did it validate file type/size? If it built a form, does it sanitize inputs? Keep an eye on common vulnerabilities (SQL/NoSQL injection, XSS, etc.) and have the AI check your code for those specifically. You can literally prompt: “Scan this code for security vulnerabilities.” It might catch something you overlooked.
• Simplify when in Doubt: AI tools might sometimes use overly complex patterns or the latest fancy library which you find confusing. Don’t be afraid to simplify. It’s better to have code that you understand and is perhaps a bit more manual, than an abstract, magical snippet that you can’t decipher. For instance, if the AI uses a complex ORM feature that’s acting up, you might revert to a simpler query that you know how to troubleshoot. You can even instruct the AI: “rewrite this in a simpler way.”
• Learn from Mistakes (Post-Mortem): When something goes wrong (say a deployment fails or a feature crashes), do a quick post-mortem. Identify whether it was due to a lack of knowledge on your part or a blind spot of the AI. Then take that as a cue to learn. If your Docker container crashed because the AI used too much memory, read a bit about Docker memory limits. If your ML model was too slow because the AI picked a huge architecture, read about model optimization. Each bug is an opportunity to become a better developer. Over time, you’ll rely less on the AI for critical decisions and use it more for grunt work (where it truly shines).
• Community and Peers: Just because you’re using AI doesn’t mean you can’t ask humans for help. The Stack Overflow community or subreddits still can be useful to sanity-check an AI approach. (Be mindful though: some communities are wary if they suspect you just pasted AI code without understanding it?—?which circles back to the point of learning fundamentals.) Even better, if you know a developer friend, ask them to do a quick code review of key parts. A fresh pair of (human) eyes can catch issues an AI won’t.

Following these practices, a non-technical developer can gradually build the competence needed to supervise the AI’s work effectively. Think of it as gradually transitioning from “AI does, I watch” to “I design, AI assists”. By combining the AI’s speed with your growing understanding, you can indeed achieve feats that would have been daunting alone. It’s all about balance?—?leveraging AI for what it’s good at (generating boilerplate, suggesting solutions) while you handle what humans are currently better at (judgment, understanding context, and ensuring quality).

Conclusion

AI-assisted development is a powerful new paradigm, and vibe coding can feel like having a superpower?—?prototypes come to life in hours, and non-engineers can materialize their ideas. The Aerial Finder example illustrates both the allure and the dangers of this approach. On one hand, a single individual can attempt a full-stack application integrating maps, databases, and machine learning, which is truly remarkable. On the other hand, without a solid grasp of full-stack fundamentals, that individual is likely to hit walls: cryptic errors, security holes, fragile deployments, or an ML feature that’s more smoke-and-mirrors than substance.

The key takeaway is that AI tools should be treated as assistants, not replacements. They can draft code, but you are the one who must architect the solution. They can save you from writing tedious boilerplate, but you must configure and polish the final product. Blindly relying on AI is like driving blindfolded with a GPS?—?it might get you moving, but you’re bound to crash if you don’t eventually look at the road. As one expert succinctly put it, don’t trust AI outputs for mission-critical code without verification.

For developers new and old, the emergence of AI coding assistants is an opportunity. It allows you to focus more on design and logic while delegating some typing to a machine. But it’s also a responsibility to maintain code quality and security. By staying aware of the pitfalls discussed?—?from Docker configs to deployed ML models?—?and actively learning alongside the AI, you can avoid the “vibe coding” traps. In doing so, you’ll not only build cooler applications faster, but you’ll also evolve into a better developer. The future of coding is likely a collaboration between humans and AI; to make the most of it, keep your vibe positive but your eyes open.

Happy coding, and may your AI partner help you write great code that you truly understand!