University of Kwazulu-Natal

Vibe Coding Reviews 101 Why Human Developers Still Matter

Bonisiwe Shabane
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vibe coding reviews 101 why human developers still matter

Last updated on December 4th, 2025 at 11:09 am Just when we thought automation and low-code tools were the next frontier, a new term entered the conversation: Vibe Coding. Vibe coding is a fresh concept that’s quickly capturing attention in the tech community. It suggests that instead of writing every line of code by hand, developers can describe what they want an application to do in plain language, and an AI tool will handle the coding. For instance, a developer could say, ‘I want an app that can recognize faces and sort them by age,’ and the AI tool would generate the necessary code to achieve this. It’s an exciting vision, one where ideas flow directly into functioning software without needing deep programming expertise.

But this raises an important question: what happens to human developers when AI starts writing code? Is vibe coding the end of traditional programming, or simply the next tool that helps developers work smarter? Let’s explore what vibe coding actually is, how it compares to traditional development, what risks and opportunities it brings, and how outsourcing companies like The Remote Group (TRG) are helping businesses adapt through AI-powered... I’ve been a software developer for 15 years now, and what keeps me hooked is solving hard, complex problems. The trickiest issues I run into are the ones that LLMs (or even Google) can’t really help with, problems that are barely documented and rarely come up. The only reason I can tackle them is because I’ve spent years running into walls, breaking things, and digging deep to understand how different technologies and patterns actually work.

If I hadn’t hit those walls over and over again, I wouldn’t have the experience I need to solve the problems I face today. I’m a software developer, but I don’t exclusively write code. I spend time explaining technology and our team’s work to stakeholders, figuring out how to scale the system and make it more reliable, and planning architecture changes. All of this is still very much software development, but my main focus isn’t cranking out code. Instead, it’s reducing the amount of code we need to write in the future by designing the system smartly and making sure the team is aligned on that vision. That means a lot of my work happens in conversations, design docs, and whiteboard sessions rather than just in an IDE.

It’s about making decisions that will save us from unnecessary complexity down the road. Writing code is easy, but maintaining and evolving a system over time is the real challenge. While I think LLMs are great for brainstorming, debugging, writing boilerplate code or acting as a rubber duck, I don’t rely on them to solve the hardest problems. Instead, I rely on my own experience: years of dealing with edge cases, learning from past mistakes, and understanding how different technologies fit together. LLMs can suggest code, but they don’t understand why a system needs to be designed a certain way, and that’s where experience makes all the difference. Ah yes, vibe coding.

The newest star in the galaxy of tech buzzwords—right up there with “metaverse” and “blockchain-powered artisanal coffee loyalty apps.” The pitch is simple – instead of typing painstakingly correct syntax, you just tell an AI what you want, in plain English, and boom—working code appears. “Hey robot, make me an app that sorts my playlist by vibes.” Done. To some, it sounds like the holy grail of programming. No more Stack Overflow marathons. No more cryptic compiler errors.

Just vibes. Pure, uncut vibes. But before we all burn our CS degrees and declare keyboards obsolete, let’s ask– is vibe coding actually the future of development—or just the tech equivalent of drinking LaCroix and convincing yourself it tastes... Vibe coding is what happens when conversational AI tools like ChatGPT or Claude are asked to generate code based on natural language. Instead of typing out functions or wrangling syntax, you casually instruct: The last two years have radically changed the way software gets built.

Tools like GitHub Copilot, Cursor, Devin, and now full-stack “vibe coding” interfaces allow someone with minimal technical expertise to describe a feature and watch a working prototype emerge in minutes. Entire workflows—from scaffolding a database, to generating UI components, to writing test suites—can now be completed with a prompt. This shift has sparked a growing question inside organizations of every size:“Do we still need developers?” Some leaders are quietly wondering if AI can replace engineers altogether. Others are exploring whether “vibe coding”—the act of describing software instead of writing it—will make traditional development obsolete. And many are asking whether humans will remain central to software creation over the next decade.

At Earthling Interactive, we work with leaders across industries who are navigating these questions in real time. And the message is clear:AI is transforming development — but it is not replacing developers.In fact, it’s increasing the value of the right developers more than ever before. AI accelerates tasks. It boosts output. It removes friction.But it does not understand context, business logic, nuance, system trade-offs, or organizational constraints. A founder opens Claude and types: “Build me a waitlist app with email capture and a dashboard to view signups.”

Ten minutes later, it’s live on Vercel. No engineers hired. No tickets opened. Just vibes. The term was coined by Andrej Karpathy earlier this year, and it describes a new way of building software. One where the developer’s role switches from writing syntax to prompting intentions.

It’s not programming in the traditional sense. It’s a back-and-forth conversation with an AI tool that already knows how to code. You simply describe what you want and the AI generates the code. You test it. Then refine. The loop is fast, forgiving, and completely redefining how technical work gets done.

are transforming software development, especially for novice and non-software developers, by enabling them to write code and build applications faster and with little to no human intervention. Vibe coding is the practice where users rely on \AItools through intuition and trial-and-error without necessarily understanding the underlying code. Despite widespread adoption, no research has systematically investigated why users engage in vibe coding, what they experience while doing so, and how they approach quality assurance (QA) and perceive the quality of the \aigc. To this end, we conduct a systematic grey literature review of \NumberIncluded practitioner sources, extracting \NumBehavioralUnits firsthand behavioral accounts about vibe coding practices, challenges, and limitations. Our analysis reveals a speed–quality trade-off paradox, where vibe coders are motivated by speed and accessibility, often experiencing rapid “instant success and flow”, yet most perceive the resulting code as fast but flawed. QA practices are frequently overlooked, with many skipping testing, relying on the models’ or tools’ outputs without modification, or delegating checks back to the \AItools.

This creates a new class of vulnerable software developers, particularly those who build a product but are unable to debug it when issues arise. We argue that vibe coding lowers barriers and accelerates prototyping, but at the cost of reliability and maintainability. These insights carry implications for tool designers and software development teams. Understanding how vibe coding is practiced today is crucial for guiding its responsible use and preventing a broader QA crisis in AI-assisted development. Recent progress in large language models (LLMs), accessible through \AItools, such as GitHub Copilot and ChatGPT, is rapidly transforming software development. These tools enable developers to describe functionality in natural language and receive executable code, thereby speeding up routine work and lowering the barrier to entry for individuals with limited programming experience (Peng et al.,...

With the use of these tools, even people without any formal training are increasingly able to develop functional applications (Feldman and Anderson, 2024). This change represents a broader shift in developer roles, which now involve orchestrating, supervising, and integrating rather than writing every line of code (Smith, 2025; Naughton, 2025). However, while these tools are transforming how software is created, less is known about the new coding practices emerging from their everyday use. Within the wave of rapid adoption of \AItools, a new practice known as vibe coding has emerged. Coined by Karpathy in 2025 (Karpathy, 2025a), vibe coding is a new programming approach where users employ \AItools to write code by describing their desired outcome (in natural language) without fully understanding the \aigc. For example, a recent report noted that 25% of Y Combinator’s Winter 2025 startups had codebases written almost entirely by \AItools, illustrating how quickly this practice is spreading (Mehta, 2025).

In contrast to AI-assisted programming, vibe coding prioritizes speed and experimentation over understanding. 👋 Let's Connect! Follow me on GitHub for new projects. The way some developers write code has fundamentally shifted in 2025. With the rapid evolution of AI-powered tools like ChatGPT, GitHub Copilot, and domain-specific LLMs, developers are no longer just writing code—they’re guiding AI to generate it. This emerging paradigm, sometimes called vibe coding, is more than just AI-assisted development; it's a workflow where developers focus on intent and design while AI handles much of the syntax and boilerplate.

But does this make development more efficient or risk reducing our deeper understanding of programming fundamentals? Let’s take a balanced look at what vibe coding is, how it works, and the implications for the future of software engineering. Vibe coding is a term that encapsulates a natural language-driven approach to development. Instead of manually writing every line of code, developers:

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