Built on Vibes: A First-Hand Account of Software Development with Vibe Coding

Vibe coding, a concept introduced by Andrej Karpathy in early 2025, reimagines software development by shifting the coder’s main task from manual programming to describing project needs in conversational language.

With vibe coding, you express what you want your software to do - often in plain English - and AI converts those requests into functioning code using tools like ChatGPT, GitHub Copilot, and Replit.

As discussed in this overview of vibe coding, the approach centers on prompt-driven collaboration with large language models: you describe a feature or solution, review the AI’s output, and refine as needed, even if you don’t fully understand every piece of underlying code.

This model makes app creation accessible, especially for beginners or non-engineers, as highlighted in this software engineering guide to vibe coding, where rapid prototyping and iterative experimentation become the norm.

However, as vibe coding gains popularity - 25% of recent Y Combinator startups reportedly use predominantly AI-generated code - it’s important to be mindful of potential risks like undetected bugs or security gaps.

MIT Technology Review’s exploration points out that while this trend lowers technical barriers for many, it’s still essential to maintain oversight and testing, especially on critical projects.

The rest of this article dives deeper into vibe coding’s practical workflow, benefits, and considerations for new and seasoned developers alike.

Nancy Gonzalez shares: "Vibe coding allowed me to quickly move from ideas to prototypes without anxiety about my coding background. The AI handled the heavy lifting, leaving me to focus on my app's vision."

• Practical Workflow: The central theme here is that vibe coding streamlines the software development process by using conversational prompts and collaborative iteration with AI models.
• Key Benefits: This approach lowers barriers so more individuals, such as William Williams, can participate in software creation and experiment rapidly with new ideas.
• Essential Considerations: While the efficiency gains are significant, it's critical to perform manual review, security checks, and thorough testing, especially when building production software.

Aspect	Main Takeaway	Contributor
Workflow	Uses conversational language for iterative code generation and refinement.	Nancy Gonzalez
Benefit	Opens up coding opportunities to people with less technical expertise.	William Williams
Consideration	Requires extra care with security and bug detection for critical projects.	Nancy Taylor

Vibe coding in 2025 has changed the developer workflow by letting you describe what you want your software to do - in plain English - and having AI tools transform those ideas into working code.

Instead of worrying about every syntax rule, you start with your concept, and AI coding assistants like Cursor, Replit, and GitHub Copilot jump in to generate code, frameworks, or even full project setups.

This approach, sometimes called "chat-oriented programming," means the developer's main job is guiding the AI with clear prompts and giving feedback on the output.

For example, a typical workflow starts when you Describe a task, like “Create a web app for photo sharing with user login.” The AI tool then Generates the initial files and code.

You Iterate through feedback cycles - asking for new features, changes, or fixing issues - which is a flexible process similar to having a conversation.

Research shows most users go through several rounds of prompting, continually refining their project with the AI's help (Getting Started with Vibe Coding).

Finally, in the Refine & Deploy phase, you review, test, and make last tweaks, often letting the AI suggest bug fixes or optimizations as well.

The choice of tool makes a big difference: platforms like Cursor support both beginner and advanced users with chat-integrated editing, while GitHub Copilot and Replit excel in IDE and cloud-based workflows, respectively (Best AI for Coding in 2025).

Tool	Unique Features	Best For
Cursor	Graphical and terminal user support, multi-file chat, strong team collaboration	Lisa Williams: Versatility for both beginners and professionals
GitHub Copilot	Block code suggestions, deep IDE integration, real-time chat	Matthew Garcia: Seamless IDE workflow integration
Replit	Web-based coding, instant AI pair programming, live collaboration	Richard Thomas: Cloud-based development and live teamwork

• Accessibility: Vibe coding lowers barriers for beginners, as anyone can describe software ideas without coding expertise.
• Oversight: Experts, like Lisa Williams, recommend thorough reviews of AI-generated code for hidden security or quality concerns.
• Iteration: This approach thrives on rapid feedback, creating better software through frequent prompt-and-response cycles.
• Shifted Roles: The primary job of developers has transformed, emphasizing the crafting of conversations with AI instead of rote instruction writing.

As Andrej Karpathy puts it, “Success isn’t about writing code by hand anymore - it’s about learning how to guide, iterate, and review side-by-side with AI.”

Vibe coding’s power comes from a blend of rapid iteration and human oversight - embracing the back-and-forth between people and AI (What is Vibe Coding?).

The paradigm has shifted from writing instructions to crafting conversations, allowing more people to turn ideas into software with fewer barriers.

Embarking on a vibe coding journey in 2025 as a beginner shows just how accessible software development has become with the help of AI tools.

Setting up a project using Nuxt 3 and Tailwind CSS, for instance, no longer requires extensive command-line knowledge or manual config. AI coding assistants like GitHub Copilot, ChatGPT, and others are now fully woven into the development process, shifting the focus from following strict tutorials to actually creating and experimenting.

You can start a session by describing your goal in everyday language - such as, “Set up a Nuxt 3 app with Tailwind CSS and a responsive hero section” - and the AI responds by generating the right codebase, configuration files, and even offering step-by-step debugging help.

Tools like Cursor, Windsurf, and Copilot have become go-to options, with developers praising their ability to handle project scaffolding, inline suggestions, and context-aware code reviews as documented by software creators.

Getting Tailwind CSS set up with Nuxt takes just a few guided steps, such as installing dependencies, updating your Nuxt config, and letting the AI streamline the asset imports and build process - all of which you can follow and tweak as you learn (see the official guide).

A practical AI-driven workflow typically looks like this:

• Project Scaffolding: You describe your project to ChatGPT, Copilot, or Cursor and let it scaffold a basic Nuxt 3 and Tailwind setup, making the initial build phase seamless.
• Automated Asset Generation: Dependencies, config files, and sample components are generated and well documented automatically.
• Contextual Learning: Use AI comments and in-editor chat for instant explanations about code snippets and patterns.
• Instant Prototyping: You prompt for updates - like layout changes or features - and see results immediately in your project.

With AI-driven editors allowing multi-model support and features like real-time chat and codebase-wide context, even beginners can quickly build, troubleshoot, and refine their projects as shared by developers testing top coding tools in 2025.

To get the most out of this approach:

1. Stay Updated and Secure: Double-check all dependencies for up-to-date versions and ensure your project’s safety.
2. Seek Explanation: Ask the AI to explain any unfamiliar patterns or generated files as you go.
3. Test and Iterate: Test each change locally and use hot reload tools for immediate feedback to refine your project efficiently.

This process gives you both guidance and the space to experiment, making coding a more relaxed and creative experience.

Step	AI Assistance	Beginner Tip
Project Initialization	Generates setup scripts and project structure	Ask why certain folders are needed
Component Building	Scaffolds Vue components with example data	Edit small parts to see results in real time
Styling	Suggests Tailwind utility classes and layouts	Try out AI’s alternative suggestions for different looks

Building with vibe coding is as much about learning and collaborating with AI as it is about coding itself.

Approaching each session as a conversation and not hesitating to ask for clarity helps turn what could be a daunting process into one that is both approachable and empowering.

Sarah Lopez reflects, "My first project felt effortless thanks to the AI's detailed guidance and encouragement."

Read more about beginner empowerment here.

Vibe coding is changing how beginners step into software development, making it more approachable and opening new opportunities for those without traditional technical backgrounds.

Instead of wrestling with complex syntax or frameworks from day one, users describe what they want in plain language and let AI tools - like GitHub Copilot, Cursor, and Replit - translate those ideas into working code.

This method, named by Andrej Karpathy, has made it possible for people to build programs simply by guiding the AI through natural conversation and feedback loops.

Recent insights highlight that, with vibe coding, product iteration cycles are much shorter, and working prototypes are often ready in hours - sometimes even in minutes - allowing teams to launch with fewer resources and less friction (hidden benefits of vibe coding).

Documenting code - a chore for many - also becomes easier, as AI tools can keep documentation synced with code changes automatically. For complete beginners, much of the anxiety around “not knowing enough” disappears, replaced by the ability to experiment, learn by doing, and refine ideas with less risk of running into technical roadblocks (Vibe Coding: AI-Assisted Coding for Non-Developers).

Vibe coding tools also encourage creative freedom, letting learners try new languages or design patterns with less commitment and lower stakes. Because the process centers on human guidance and iterative review, users can develop a real sense of ownership over their projects, regardless of their previous coding experience.

This accessibility is helping more non-traditional learners and diverse newcomers become active creators in tech. Even startups are noticing the change, with 25% of Y Combinator’s 2025 batch reporting up to 95% of their code generated through AI, a sign of just how much faster and more inclusive development can become (balancing speed and safety with vibe coding).

Vibe coding empowers beginners to move from idea to application with unprecedented speed, but it’s equally important to recognize the unique pitfalls and risks that arise when relying on AI-generated code.

AI-powered tools can analyze and suggest code quickly, but research shows they frequently create functional code that contains errors, security flaws, or may not follow best practices - sometimes missing project-specific context entirely.

For instance, recent findings have highlighted that risks include not just insecure code, but also downstream impacts like technical debt and reduced developer understanding when auto-generated suggestions aren't carefully reviewed or adapted.

To stay safe, beginners should focus on these strategies:

• Code Review is Critical: Manual oversight is irreplaceable in the review process. Automated solutions may miss bugs, so human review remains a top defense against issues such as outdated dependencies and insecure logic, especially as AI does not always grasp project-specific nuances. Learn more about these review challenges and mitigation strategies here.
• Quality Assurance Matters: Comprehensive QA is vital because AI outputs can miss edge cases or essential safeguards. Both automated and manual testing support reliability and maintainability. Tools like Codacy and SonarQube catch what reviews might miss - see AI-driven QA in practice here.
• Technical Debt Management: Refactor often and maintain clear documentation. Sole reliance on AI can produce poorly documented legacy code, complicating maintenance. Regularly verify and refactor to avoid long-term pitfalls and maximize AI’s benefits - get an overview here.

"Staying vigilant - by reviewing all AI-generated code, incorporating strong QA processes, and addressing technical debt early - will help you build secure and sustainable software from the ground up, no matter where you are in your vibe coding journey." - Matthew Jones

As vibe coding continues to gain traction in software development, applying best practices for safety and effectiveness is vital - especially for those new to using AI-assisted workflows.

Research highlights that while many developers rely on AI coding assistants, widespread challenges remain around consistent code review and understanding the unique risks AI can introduce.

For example, studies show that AI-generated code, even when produced by top models, may contain logical inconsistencies, miss business logic, or introduce security flaws if not thoroughly reviewed by a human.

• The importance of human review is central, as Sarah Wilson notes that every AI-generated snippet should be regarded as a draft to maintain quality and reduce risk.
• Using automated tools such as automated tests, linting, and static analysis reinforces code safety, explained by John White, helping to catch errors introduced by AI.
• Clear team communication about the origins and limitations of AI-generated code maintains project transparency and reduces long-term maintenance risks, as Jessica Williams emphasizes.

Human expertise should always be part of the cycle, both to review AI suggestions critically and to handle scenarios where AI tools do not fully grasp project-specific context.

Automated code review platforms and integrated IDE tools can assist with instant feedback and adherence to best practices, but these are most effective when combined with iterative testing and feedback from developers.

Strategy	Purpose	Best Used With
Automated Testing	Catch bugs early	Manual code review
Code Linting	Ensure style consistency	Continuous Integration
Static Analysis	Identify security issues	Automated review tools

By aligning your workflow with these strategies - comprehensive review, thorough testing, transparency on code origins, and ongoing learning - teams can boost both productivity and software quality without compromising on trust or security.

For practical implementation tips and a deeper look at reviewing AI-generated code, see this guide on reviewing AI-written code, explore best practices for coding with AI, and learn strategies for safe automated code reviews.

The future of software development is rapidly evolving as vibe coding gains traction, reshaping how we think about building technology. Predictions for 2025 and beyond suggest that AI-powered coding assistants will not only speed up workflows but also fundamentally shift developer roles from hands-on coding to guiding, reviewing, and orchestrating AI-generated solutions.

As highlighted by Andrej Karpathy, the originator of the term vibe coding, success will hinge on being able to communicate needs clearly and collaborate with AI effectively.

Recent studies show that nearly 80% of professional developers now use AI tools daily, and the new development landscape emphasizes prompt engineering, iterative feedback, and human-AI teamwork see more on AI's impact on developer roles.

• AI-powered coding assistants are transforming workflows, making developers more efficient and shifting focus to higher-level guidance.
• Prompt engineering and feedback loops have become core to development, emphasizing clear communication and collaboration with AI.
• Human-AI teamwork enhances productivity, requiring developers to actively review and steer AI-generated code.

Looking ahead, vibe coding is making software creation more accessible, empowering non-developers, youth, and people from non-traditional backgrounds to participate in technology.

Estimates reveal that up to 75% of users on platforms like Replit never write a single line of code, relying completely on AI to turn their ideas into working applications discover how vibe coding lowers barriers.

This democratization is sparking a new era where creativity and problem articulation matter as much as traditional programming skills. However, experts caution that while AI speeds things up, it also introduces new challenges around code quality, security, and debugging, making roles like “AI Prompt Engineer” and “AI Code Auditor” increasingly vital.

Role	Key Responsibility	Central Theme
AI Prompt Engineer	Crafts clear instructions for AI	Effective communication ensures quality results
AI Code Auditor	Reviews and validates AI-generated code	Security and quality are top priorities
Human Developer	Collaborates, innovates, and guides AI output	Blending creativity with technology drives success

"Democratization of coding is the real game-changer - people like Robert White and Jennifer Thomas, previously outside tech, are now using AI tools to transform their ideas into reality, thanks to vibe coding."

Community-driven ecosystems are evolving as well, with open source contributions rapidly expanding - mirroring how collaborations and best practices propagate across the vibe coding community.

As the industry adapts, blending human creativity with AI’s efficiency will redefine job satisfaction, prompt new learning models, and change what it means to “develop software” as explored in recent predictions for the future of vibe coding.