Vibe Code Reality Check: What You Can Actually Build with Only AI

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# Introduction

 
Coding has traditionally been a major pillar of most software engineers' and developers’ work, be it by implementing algorithms, building business logic, or maintaining complex systems. But due to the progress made by large language model (LLM)-powered applications like chatbots, this is rapidly changing. vibe coding entails using modern chatbot apps to specify software requirements and intent in natural language, and delegating to artificial intelligence (AI) the generation and modification of code, sometimes with little direct understanding of its inner logic.

This article adopts an "expectations vs reality" approach to demystify, based on research of real success and failure stories, what the capabilities and limits of vibe coding are.

# Defining Vibe Coding

 
The term "vibe coding" was coined in early 2025, and it can be defined as a chatbot-driven software development approach, such that developers describe a project or task to an LLM. As a result, the model generates code that meets the specifications stated by the user's prompt.

Ideally, if we literally abide by the original idea behind it, vibe coding would entail the fact that the developer may not need to examine the generated code, but instead they accept the AI-generated code as it is. Yet, in practice, this approach is not exempt from risks — from hidden bugs and subtle security issues to difficulties for maintainability — so at the end of the day, some degree of human oversight and refinement are still needed in most generated code outcomes to become production-ready.

Interested in acquiring a deeper, more solid understanding of vibe coding first? Here are some key KDnuggets articles you might want to have a look at:

• 7 Steps to Mastering Vibe Coding
• 10 GitHub Repositories to Master Vibe Coding
• How to Vibe Code on a Budget

# Reviewing Success and Failure Stories

 
Now that we have a clear understanding of what vibe coding is, it's time to look at examples of projects or real-world initiatives where it yielded successful results, as well as failure cases.

Success stories include:

• This Minecraft-styled flight simulation game has been developed using vibe coding, namely by putting together several thousand prompts that together create full gaming applications from beginning to end: no coding burden involved.
• Another popular example of a vibe-coded application is Creator Hunter: in its creator's words, conceived while prompting on a train commute. The app aims to connect content creators with startup founders. While raising high expectations at first, later traction dynamics results suggested that the growth of the resulting product may have reached a plateau far too early; hence, while we can definitely consider the establishment of Creator Hunter as a success story in its own right, its long-term status as such is nuanced.
• On a third example, we have a New York Times (NYT) journalist's successful attempts to experiment with vibe coding for creating several small apps for enhancing personalization in daily life tasks. One example is LunchBox Buddy, an assistant that suggests meals based on what ingredients are in your fridge. While receiving criticism due to the idea behind the app not being original or pioneering, from the viewpoint of using vibe coding, it is a sort of achievement experimentally speaking. Sure, there may be a lot of things to improve, but let's point out that vibe coding is a very new paradigm that may still need a lot of maturing.

Meanwhile, a couple of failure stories to point out include:

• This Replit story sounds like crossing the boundaries between reality and science fiction. A company used the popular vibe coding tool to build an AI agent that managed their SaaS product's professional network. What started as sheer, addictive joy using the vibe coding tool ended up in a disastrous incident, with valuable database entries containing data from executives and companies being destroyed. The most shocking part: the AI agent admitted having done it, arguing that it saw empty database queries, and it panicked rather than systematically pondering how to build a course of action. The rest is history: months of data collection, processing, and storage work were destroyed in mere seconds.
• The startup Enrichlead turned out to be another well-known case of failure when attempting to make use of vibe coding, specifically by building its app entirely with Cursor AI. While appearing functional and safe at launch time, shortly after being deployed to the real world, it ended up in collapse due to serious security breaches being exploited by attackers, e.g. bypassing subscriptions requiring authentication and even polluting the database due to the lack of appropriate input validation mechanisms. Part of the reasons behind the incident is reported to be a lack of technical expertise to diagnose or fix cascade issues that might at first glance seem harmless. The entire project had to be ultimately shut down.

# Final Thoughts

 
By looking at the success and failure stories above, we can conclude that if we adopt a critical, ambitious perspective, it might be difficult to find major success stories of vibe coding as of today. Most of these cases have their own nuances, which proves that vibe coding is still a paradigm in its infancy and might take much longer to make it truly reliable in real-world settings, especially — if we look at failure stories — in terms of security and robustness against unexpected or less likely situations. 

// Key Takeaways

1. Vibe coding can enable rapid code generation, but human understanding and verification are still crucial. AI tools used in vibe coding lack the cognitive understanding required to secure, debug, or make the code maintainable in the long run.
2. As with nearly every technology, patience is key to seeing real success stories. As the founder of the SaaStr community stated, "it will be a long and nuanced journey getting vibe-coded apps to where we all want them to be for many true commercial use cases."

Iván Palomares Carrascosa is a leader, writer, speaker, and adviser in AI, machine learning, deep learning & LLMs. He trains and guides others in harnessing AI in the real world.