In the rapidly evolving landscape of software development, a revolutionary approach has emerged that is transforming how code is written. Vibe coding - a term popularised by AI expert Andrej Karpathy in 2025 - represents a fundamental shift in development methodology where artificial intelligence generates code based on natural language instructions rather than developers manually crafting each line. The concept involves developers "fully giving in to the vibes... and forgetting that the code even exists," allowing AI to handle the technical implementation while humans focus on intent and outcomes.
This article explores my personal journey into AI-assisted development, a transformation that began with the launch of ChatGPT and expanded as I experimented with various AI platforms. What I discovered was nothing short of incredible - the ability to translate ideas into functional applications at unprecedented speed. Instead of spending months explaining my vision to development teams and waiting for implementation, I now describe my requirements to an AI assistant and receive complete code solutions within minutes. While human developers remain essential for technical infrastructure and deployment, AI writing code has dramatically accelerated my product development cycle and reduced costs, revealing the extraordinary potential of this new development paradigm.
Understanding Vibe Coding and AI-Assisted Development
Vibe coding represents a paradigm shift in software development where programmers communicate their intent rather than writing individual lines of code. Instead of meticulously crafting each function or method, developers express what they want to achieve in natural language, and artificial intelligence generates the corresponding code. For example, a developer might simply state, "add a navigation bar with links to home, about, and contact pages," and the AI produces the necessary HTML, CSS, and JavaScript.
This approach fundamentally changes the relationship between developers and their code. Traditional programming requires detailed technical knowledge and precise syntax, but vibe coding focuses on communicating the desired outcome rather than the implementation details. The developer becomes more of a director, guiding the AI toward the intended result while the AI handles the technical implementation. This shift allows professionals to focus on higher-level design decisions and user experience rather than getting lost in syntactical details.
The evolution of natural language programming
The concept of programming in natural language has existed for decades, but practical implementations remained limited until recent advancements in language models. Early attempts like COBOL aimed to make programming more accessible by using English-like syntax, but they still required strict adherence to programming conventions. Modern AI-assisted development tools represent a quantum leap forward, as they can understand ambiguous human instructions and translate them into functional code.
This evolution has accelerated dramatically since 2022, with large language models demonstrating impressive capabilities in code generation. These systems have been trained on vast repositories of code and can understand programming concepts across multiple languages and frameworks. The progression from rigid syntax-based programming to fluid, intent-based development marks a significant transformation in how software is created, potentially making development more accessible to those without formal programming education.
How AI writes code from human intent
When a developer provides a natural language instruction to an AI coding assistant, several sophisticated processes occur behind the scenes. First, the AI parses the human intent, identifying key requirements, constraints, and objectives. It then matches these requirements against its knowledge of programming patterns, best practices, and syntax rules. The system may consider multiple implementation approaches before selecting the most appropriate solution based on context and common usage patterns.
The quality of AI written code depends largely on the clarity of human instructions and the AI model's training. More specific prompts typically yield better results, though modern systems can often infer missing details from context. For instance, when asked to "create a responsive image gallery," the AI might generate not only the basic structure but also include responsive breakpoints, loading states, and accessibility features based on current development standards. This capability to extrapolate from vague instructions to complete, functional code is what makes vibe coding such a powerful development methodology.
My Journey to Becoming a Vibe Coder
I am not a developer, my background has been in business operations, systems and efficiencies. I have always been obsessed with computers, coding, data and leveraging the power of technology for everyday business activities. Always searching for ways to automate or batch tasks to make them more efficient. I have helped build and develop an number of tools and platforms but not normally directly coding them myself. This has been done via collaboration with developers and coding teams.
When ChatGPT launched, I signed up for the waitlist immediately. I had dabbled with some early AI tools that were leveraging LLMs, like Writesonic, Copy.ai and Jasper, and was already hooked.
My initial requests were simple, pasting in current code from my websites and asking ChatGPT to make changes, or fix CSS bugs. Plus generating basic JavaScript functions. I was genuinely surprised when the AI not only resolved my issues but explained the solutions in a way that enhanced my understanding. What began as occasional assistance soon became a daily collaboration as I tested the boundaries of what these tools could accomplish.
I now use AI tools for every task I do, from coding to data analysis, data processing, article writing, SEO, social media campaigns and even handling some of my emails. I have spent many 100s, if not thousands, of hours on many of the mainstream tools. I have started to learn their strengths and weaknesses and know which to use for which tasks.
- ChatGPT - Good all-rounder not great for complex coding or large projects.
- Claude - best by a mile for all round coding especially large project planning, visuals and full project understanding.
- Gemini - next best for coding but goes a bit rouge at times. Best large context window.
- DeepSeek - Ok better than ChatGPT (for somethings) but cheats a bit sometimes and short changes on the answers - can't complain as it is free.
- Mistral - super fast. Ok for simple quick tasks.
- Grok3 - Good allrounder, good for social media, as it is basically trained on X (Twitter).
- MS - Co-Pilot - the laziest LLM. Does seem to really want to help. Short answers no substance.
I have also used some of the popular coding platforms like Cursor, Lovable, Replit and others - all ok but not not great yet, there are getting better by the day. Plus some open source LLMs run locally and some agent type tools like Raid - ai and Trae (among others).
Plus, tons of image and video generators and other more task specific AI tools.
I will need to write some more articles to discuss the nuances and my experiences with each.
Code by Intent, Not Syntax
AI is transforming development from syntax memorization to intent expression. "Vibe coding" lets developers communicate ideas naturally while AI handles implementation details, reducing cognitive load and accelerating development cycles. The future isn't about replacing developers—it's about amplifying their creativity.
Within weeks, I had used AI assistants to build functional prototypes from HR management systems to automated article writing agents. These would have taken months to code manually. The speed was remarkable - I could describe a feature in natural language and receive working code almost instantly. Though early attempts required significant refinement, the foundation was solid enough to build upon, and the learning process was far more intuitive than traditional development approaches.
My workflow transformed dramatically. The AI would generate complete components or modules that I could then review, test, and refine. This approach allowed me to focus on architecture and user experience while the AI handled implementation details. The most significant benefit was in rapid prototyping - I could test multiple approaches in the time it previously took to implement just one.
Learning to communicate effectively with AI
Effective vibe coding requires a different communication skill set. I discovered that providing context was essential - explaining not just what I wanted but why I needed it helped the AI understand my intentions. Describing the desired outcome rather than implementation details yielded better results, allowing the AI to suggest approaches I might not have considered.
I developed a pattern of iterative refinement - starting with broad requests and gradually adding specificity based on the results. This conversational approach to development felt strange initially but became second nature. The most valuable lesson was learning to be precise without being technical - using clear, descriptive language rather than jargon. This skill of intention-focused communication has improved not only my AI interactions but also how I collaborate with human developers on more complex projects.
I often use one tool, like ChatGPT to explore ideas and help me map out the process asking it to provide a brief for an AI coder. I then, simply, copy and paste this to Claude using the project function and adding some reference files, and away it goes. On a couple of occasions, using Claude Sonnet 3.7, it has written every line of code for every file for a whole application in a one 1/2-1hr session. Yes, it had bugs, lots of them, BUT the structure, the files, the code, the logic was all there to build on.
This meant I could either pass this to an expert human to fix or de-bug with AI. Either was I had just saved over a month of work in an hour - that is not an exaggeration.
Rapid Prototyping with AI: Speed and Cost Benefits
The transformation in development timelines when switching to AI-assisted coding is nothing short of mind blowing. Where traditional development cycles might require weeks of planning, coding, testing, and refining, vibe coding collapses this timeline dramatically. A feature that would typically take days to implement can now be completed in hours or even minutes.
The efficiency comes from eliminating the tedious manual coding process. Instead of painstakingly writing each line of code, developers (or even the non-professional enthusiasts, like me) can focus on communicating their intent to AI tools. This shift fundamentally changes the development workflow - rather than spending time on syntax and implementation details, developers can concentrate on higher-level design and functionality considerations.
Cost Efficiency of AI-Assisted Coding
The economic advantages of AI-assisted development extend beyond just faster delivery times. By reducing the hours required to produce functional code, organisations can significantly lower their development costs. This is particularly valuable for startups and small businesses with limited resources who can now build sophisticated applications without large development teams.
Additionally, the cost structure shifts from high upfront development investments to more manageable ongoing AI tool subscriptions. This allows for better cash flow management and reduces the financial risk associated with software development projects. The democratisation of development also means businesses are no longer wholly dependent on expensive specialist developers for every task.
Balancing AI and Human Input in Software Development
Vibe coding excels in specific scenarios where rapid prototyping and iterative development are prioritised. I have found it particularly effective for frontend development, UI components, and creative projects where visual feedback is immediate. When building landing pages or implementing design changes, natural language prompts like "make the header sticky" or "add a hover effect to those buttons" yield remarkable results in seconds rather than minutes or hours.
Traditional development remains essential for complex backend systems, performance-critical components, and infrastructure work. Any project requiring deep technical understanding of security protocols, database optimisation, or system architecture demands human expertise. The distinction is not about replacing developers but augmenting their capabilities—allowing them to focus on high-value tasks while AI handles more repetitive coding tasks.
Technical limitations and human oversight
Despite impressive capabilities, AI-generated code is not infallible. I have encountered instances where vibe coding produced solutions that worked initially but created technical debt later. Server configurations, session management, and authentication systems typically require human oversight to ensure security and reliability. The code may function, but without understanding the underlying principles, developers might miss critical vulnerabilities.
Maintainability presents another challenge. Code written through natural language programming can lack documentation or follow inconsistent patterns if not properly reviewed. Human developers play a vital role in establishing coding standards, reviewing AI output, and ensuring the codebase remains comprehensible and maintainable for future development.
The future of AI-human collaboration in coding
The relationship between developers and AI assistants is evolving toward a symbiotic partnership rather than replacement. In my experience, the most effective approach involves using AI as an intelligent amplifier for human creativity and problem-solving. Developers who embrace this paradigm shift focus more on system architecture, business logic, and user experience while delegating implementation details to AI.
Looking forward, we may see the emergence of specialised roles—some developers becoming AI prompters who excel at directing AI systems through natural language, while others focus on the technical foundations that AI cannot yet master. This collaboration represents not the end of programming as we know it, but rather a transformation that may ultimately make software development more accessible while elevating the human elements of creativity and innovation.
Human developers and coders have legitimate concerns about AI-powered coding tools. After all, these large language models learned from their work. Yet this technology remains an inevitable part of development's future. Those who adopt these tools will help shape how code evolves, while professionals who resist or dismiss them risk falling behind. My perspective remains consistent - regardless of your programming expertise, why not leverage these resources to increase efficiency, accelerate development, reduce manual typing of syntax, and gain advantages? Explore all available tools. Many integrate directly into your code editor, providing assistance whenever you encounter challenges.
Summary of Embracing the Vibe Coding Revolution
Vibe coding represents a fundamental shift in how we approach software development. By embracing AI as a collaborative partner rather than merely a tool, developers are experiencing unprecedented productivity gains and creative freedom. The ability to communicate intent through natural language and have AI generate functional code in seconds transforms the development process from technical implementation to creative direction.
As we have seen throughout this article, this approach democratises development, allowing individuals with minimal coding experience to build sophisticated applications and tools. While human oversight remains essential for complex systems and technical infrastructure, the potential of AI-assisted development continues to expand. Whether you are a seasoned developer or a creative professional with coding aspirations, experimenting with vibe coding may open new possibilities and efficiencies in your work.
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This article was created by a human, written with some help from AI and edited by a human.
