How does Vibe Coding differ from traditional coding?

Vibe coding means describing an app in everyday language and trusting the AI to generate the code - no programming experience required. In vibe coding, your natural language becomes the programming language[6]. You simply explain what you want the software to do (the "vibe"), and the AI model writes and modifies the code for you. This concept flips traditional coding on its head: people who don't "speak" programming can now tap into software creation by conversing with AI[7]. It differs from traditional coding in that you don't need to know syntax, libraries, or frameworks - you just need to clearly express your intent.

Crucially, vibe coding is not the same as generic AI-assisted programming or coding. It refers to a specific, carefree style where you generate code with AI without closely reviewing it[8]. You provide broad instructions, hit "accept" on the AI's changes (often without scrutinizing every line), and iterate by testing and re-prompting rather than manually debugging each snippet[3][9]. This approach shines for rapid prototyping and hobby projects. As Karpathy noted, it's "not too bad for throwaway weekend projects" where you can quickly get a working app by iteratively chatting and copying error messages back to the AI until things run[10]. The focus is on the end result and quick experimentation - the AI handles the heavy lifts of syntax, boilerplate, and setup[1].

That said, vibe coding doesn't imply that the code quality is poor or that you shouldn't care at all. It simply means you start by prioritizing speed and creativity over meticulous code craftsmanship. In fact, many developers see vibe coding as the early stage of a spectrum: it gets you 70% of the way to a functional app very quickly, and then you can apply more traditional engineering rigor for the last 30% to polish and harden the result[11][12]. We'll discuss those challenges later, but it's important to know vibe coding ≠ "low-quality" coding by default - it's a deliberate trade-off of upfront speed vs. later refinement[13]. In practice, vibe coding opens the door for non-engineers to build software, and it can augment developers' productivity for certain tasks. The key is understanding how to use it effectively and when to transition to deeper engineering.

Popular Vibe Coding Tools and Platforms

English is your new programming language - today's vibe coding platforms turn plain prompts into real, running apps. A wave of new AI-powered tools has emerged to make vibe coding accessible to everyone. These platforms all share a common goal: let you build, edit, and deploy apps by conversing with an AI, so you rarely (or never) have to write code directly. They differ in specific features and strengths, but the overlap is significant. If you have an idea, these tools will orchestrate everything from the database to the UI for you, often end-to-end from prompt to published app[23][24]. Let's look at some of the most popular vibe coding tools available:

Lovable - AI App Builder for Effortless Prototypes

Lovable (lovable.dev) has quickly become one of the most popular vibe coding platforms, known for its ease of use and well-rounded capabilities[25][26]. It's often recommended if you're vibe coding an app for the very first time[27]. With Lovable, you simply describe your app idea in detail and it does three things exceptionally well: planning, design, and explanation. After you enter your initial prompt, Lovable sketches out what it's going to do before writing any code - it might say, "I will create a homepage with X, a login screen with Y, and integrate Z for the database." This gives you a sense of control and understanding of the direction[28]. Then it generates the app's frontend and backend, often producing a surprisingly polished UI without you having to specify any design details[29]. Many users report that Lovable's first pass on the UI looks good out-of-the-box (it even chooses reasonable color schemes and layouts on its own).

Under the hood, Lovable uses a React framework for the front-end and can integrate with backend services. Notably, it has built-in integration with Supabase (a popular backend-as-a-service) for features like user authentication and data storage[30]. This means your AI-generated app can immediately have a real database and login system without you configuring servers - Lovable handles it. It also connects to GitHub for version control, so you can export your code to a repo with one click[31]. This is useful if you want to later review or tweak the code manually, or collaborate with others. Throughout the process, Lovable provides explanations in plain English: as it builds or modifies your app, it tells you what it implemented and why. This running commentary helps you trust what's happening and learn some coding concepts along the way. The main caution with Lovable noted by users is that it can consume a lot of credits (API usage) for complex apps[32] - in other words, it's powerful, but you may hit the free tier limits quickly if you keep iterating on a big project. Still, with a generous free plan (30 prompts per month) and a very balanced feature set, Lovable is often the first stop for beginners who want to "build something lovable" without coding.

Bolt - Flexible AI Development with Developer Tools

Bolt (bolt.new) is another leading vibe coding tool, created by StackBlitz (a company known for in-browser developer tools). Bolt is often praised for its flexibility and advanced integrations[33][34]. It provides a vibe coding experience similar to Lovable - you chat to build your app - but it also exposes more under-the-hood controls for those who want them. Bolt's interface feels a bit more like a programmer's IDE, which can be advantageous as you grow comfortable with AI development. For example, Bolt includes a built-in terminal and shows you the dependency installations and build process as the app is being assembled[35]. It generates the project file-by-file, and you can watch as it creates each component, config, and test. If an error occurs during generation, Bolt will detect it and automatically offer to debug it for you, or you can pop open the terminal and run commands yourself[35]. This means Bolt sometimes catches issues earlier in the process, whereas other tools might only surface a bug when you try to run the app.

One of Bolt's standout features is an "Enhance Prompt" button (or "prompt zero" enhancer) - you can jot down a rough idea and let Bolt rewrite it as a structured specification[36]. For instance, you type "I need a site for selling handmade jewelry," and Bolt might transform that into a clearer set of requirements (pages needed, database schema, etc.) before building. This is great if you're not sure how to articulate your ask; Bolt helps you flesh it out. Bolt also offers granular control through its "Code" view: you can select specific files and tell the AI to only modify those ("Target file") or protect certain files from changes ("Lock file")[37]. As a beginner you may not use those immediately, but they become incredibly useful as your app grows - you can prevent the AI from accidentally messing up a part of the app that is working well, for example.

In terms of integrations, Bolt shines: it can import designs from Figma (just give it a Figma file URL, and it will pull in the design to guide UI generation)[38], integrate with Stripe for payments (so you don't rely on the AI to hallucinate a payment flow - it uses real APIs), connect to Supabase similar to Lovable, and sync with GitHub for code export[39]. This makes Bolt a good choice if your project might involve external services or if you anticipate evolving it beyond the prototype (since you can lock down and export code more readily). Bolt's free tier is also generous - currently around 1 million tokens/month (roughly equivalent to many dozens of prompts, depending on length)[40]. Beginners often alternate between Bolt and Lovable to take advantage of both free quotas and different strengths[41]. In summary, Bolt gives you a bit more of a "power user" vibe coding experience - it's just as friendly for a newbie's first project, but it has depth that you can grow into, making it possible to take a vibe-coded app further with real developer workflows when needed.

v0 (by Vercel) - Transparency and Control in AI Generation

v0 (v0.dev), developed by Vercel (the company behind Next.js and a major cloud hosting provider), takes a slightly different approach to vibe coding: it's known as the platform that gives you the clearest view of what the AI is doing under the hood[42][43]. If Lovable tries to hide complexity and make everything smooth, v0 leans into transparency. When you prompt v0 with an idea (for example, "Build an e-commerce store app with a product gallery and search filter"), it will respond with a detailed breakdown of what it plans to do. It might list: "Pages: Home, Product List, Product Detail, Search. Features: search bar with real-time filter, Supabase database for products, etc. Tech stack: Next.js (React), Tailwind CSS, Supabase for DB, Vercel Functions for API." This happens before or as it writes the code, so you can see exactly which components and services will be involved[44][43]. Many beginners find this educational - it's like the AI is narrating its implementation strategy.

As v0 generates the app, it doesn't shy away from showing code. For example, if your app needs a database, v0 will actually display the SQL commands it uses to set up the tables, so you can review fields and data types[44]. This level of detail helps you learn what's going on, and you can catch if something looks off. The interface also has a cool aesthetic: code scrolls by on a black background as it's being written, giving you that "Matrix" feel while still being useful to inspect[44]. Despite this more technical vibe, v0 is still easy to use. You chat with it like any other tool to add features or modifications, and it implements them. The difference is you'll see a log of changes and structured summaries of each action. Another neat feature: you can "fork" a project to a new chat at any time[45]. This means if you want to experiment in a different direction (or if the conversation with the AI got complicated), you can branch off without losing the original version - a bit like version control for your vibe.

Since v0 is by Vercel, once you're done, deployment is first-class: the app can be deployed on Vercel's infrastructure with essentially one click[46]. Vercel's platform is robust, so hosting your vibe-coded app there means you benefit from proper scalability and reliability out of the box. v0's free tier is somewhat limited in credits (to encourage upgrades)[47][48], and it currently supports a specific tech stack (Next.js/React, etc.), but it's a fantastic tool if you want to learn from the AI's process. Many users say v0 feels "addictive" because it shows the why and how of each step, which in turn helps you prompt smarter the next time[49][50]. For a beginner, v0 provides a gentle introduction to concepts like pages, components, and database schemas in a real app, all while still building everything for you.

Google AI Studio (Build Mode) - Big-Model Power for Free

AI Studio (Build tab) - We'd be remiss not to mention the recent entrant by Google. In October 2025, Google revamped its AI Studio with a dedicated "Build" mode tailored for vibe coding[51]. This platform leverages Google's latest models (like Gemini 2.5 and others) and can mix in various AI capabilities (text, images, even video AI) to create rich applications from a simple prompt[52]. One of the best parts: it's free to start and no credit card needed upfront[53], which lowers the barrier for anyone just tinkering with an idea. AI Studio's Build tab guides you through selecting what AI features your app might need (e.g. image generation, maps, language model type) and then presents a big prompt box: you describe your app and hit "Build."* The system will automatically assemble components using the selected models and generate your app.

After generation, you get a very user-friendly editor: the left side has a chat panel where you can ask for changes or get explanations, and the right side shows the full code editor with files (plus a live preview pane for the UI)[54]. This means you can both chat with the AI and, if you're curious, peek at or even directly edit the code it wrote. Google added helpful tooltips on code files so beginners know what each file is for (e.g. "This is the main app file, App.tsx")[55]. AI Studio also introduced a fun "I'm Feeling Lucky" button that generates a random app idea and builds it on the spot - great for inspiration or learning by example[56]. For instance, it might create a trivia game app with an AI host or a garden planner with image AI, entirely on its own mix of services[57]. Another strength is multimodal features: you can incorporate things like the Imagine image generator, or a Veo video analysis module, simply by toggling them on before building[52]. This allows creation of apps that not only have standard web UI but also AI capabilities like image recognition or generative graphics with no extra code from you.

Perhaps the biggest advantage of AI Studio is that it seamlessly blends into a traditional development cycle if needed. You can save your project to GitHub or download the code easily[17], then continue working in a normal environment, or you can deploy it straight from the Studio (it uses Google's infrastructure, with options like Cloud Run for more serious hosting)[16]. Google is clearly positioning this as an all-in-one playground where novices can go from zero to a running app in minutes, and then gradually learn or scale up. In testing, even non-developers have been able to create and deploy apps (like the dice roller example) with virtually no manual coding[51][18]. The integration of suggestions is also noteworthy: the AI will proactively suggest improvements to your app via a feature called Flashlight, which analyzes the app and lists enhancements (e.g. "Hey, how about adding a history of generated images?")[58]. You can implement these by just clicking or asking it to proceed. For beginners, this feels like the app is evolving with a friendly nudge rather than you having to think of every feature from scratch.

Other Noteworthy Tools

In addition to the above, there are a few other vibe coding or AI-assisted development tools worth mentioning:

• Replit (Ghostwriter & Deploy): Replit, an online IDE, introduced AI features that support vibe coding. It has an "AI Agent" that can plan your app structure before writing code, ensuring there's a coherent blueprint[59]. Replit's strength is that it's a full dev environment - you can switch between AI guidance and manual coding anytime. It also provides database management UI and one-click deployment, making it a solid choice for building and hosting in one place. For instance, you can prompt Replit's Ghostwriter, "Create a Flask app with a homepage and a database of quotes," and it will generate the scaffold, then you can refine or run it instantly on Replit's cloud.
• Cursor: While not a vibe coder from scratch, Cursor is an AI-powered code editor that many vibe-coders use to debug or refine code after generation[60][61]. The Zapier guide to vibe coding noted that users often export their Lovable or Bolt projects to GitHub, then open them in Cursor for improvements[62]. Cursor's AI can read your entire codebase and suggest optimizations, find bugs, and even implement features in a more controlled way (with your approval of diffs)[63][64]. Think of it as a companion to vibe coding: vibe tools get you a running app, and Cursor helps polish it to production quality. It is a bit more technical to use (you'll need to run the app locally to preview it, for example[65]), but it provides an excellent learning bridge - it will explain its changes and teach best practices as it helps you fix issues[66][67].
• Tempo, Base44, Memex, etc.: A number of other startups (Tempo.new, Base44, Memex) offer their spins on vibe coding. Tempo, for example, emphasizes visual product design - it creates a product requirement doc and screen flow diagram for you, and even lets you tweak the UI elements visually before code generation[68][69]. It also notably doesn't charge for error-fixing prompts - recognizing that debugging can eat a lot of tokens[70]. Base44 focuses on enterprise needs like fine-grained security rules in apps[71]. Memex allows local runs and more control over the AI's reasoning steps[72]. While these are more niche, the ecosystem is rapidly evolving. The good news is that all these tools share the core principle: you write zero code at the start, and progressively refine via natural language. The choice often comes down to the complexity of your project and personal preference for UI and features.

No matter which tool you pick, the barrier to creating software has never been lower. As one Zapier reviewer quipped, vibe coding tools make you feel "superpowered" as a creator[73][74]. An entrepreneur with no coding background can build a functional prototype over a weekend. A designer can go from Figma mockup to a live app by just describing how the design should behave. A project manager can skip the wireframes and actually build a mini-app to demonstrate a concept. You don't need to be an engineer to vibe code - it's truly democratizing who can develop software[7].

Vibe Coding Pro-tips for intermediate engineers

To an experienced developer, "vibe coding" isn't about letting an AI take the wheel, but rather using it as a force multiplier for their own expertise. A senior engineer knows that the AI's output is only as good as the prompt, the context, and the review process that follows. It's a collaborative approach that pairs the AI's speed with human judgment.

Here are 20 tips for senior engineers to master vibe-coding and deliver production-grade results.

Mindset and Process

1. Be a pilot, not a passenger. The AI is your copilot, not the pilot. You are ultimately responsible for the code that ships. Do not blindly copy and paste - always review, understand, and own every line.
2. Act like it's an over-enthusiastic junior dev. Think of the AI as a junior who needs constant guidance and feedback. You wouldn't let a new hire write and merge a large feature without a clear plan and code review. Treat the AI the same way.
3. Start with the design, not the code. Use the AI as a rubber duck for architecture. Before writing any code, prompt it to propose a plan, discuss tradeoffs, and outline the steps.
4. Embrace the “spike” approach. Treat the AI's initial output as a prototype or a spike. Use it to explore concepts quickly, then discard or rewrite the code from a position of understanding.
5. Build a collaborative feedback loop. Review the AI's work and use its failures as a teaching moment. Learn from what it gets wrong and refine your instructions for the next iteration.

Prompting Techniques

6. Provide explicit examples. If you want the AI to follow a specific pattern, include a well-formatted code example. "Follow the createUserInput Zod schema format" is better than asking for one from scratch.
7. Give constraints liberally. Tell the AI what not to do. For example: "Do not use a new library", "Avoid using any", or "Never write tests that hit a real database."
8. Leverage a custom rulebook. For team settings, maintain a shared rulebook (e.g., .cursor/rules file). This codifies conventions and ensures consistency.
9. Load the relevant context. Always provide relevant files - type definitions, configuration files, etc. - so the AI has enough context.
10. Break down complex tasks. For large, multi-step features, create smaller, chained prompts. This improves accuracy and allows step-by-step verification.

Quality and Maintenance

11. Test-driven prompting (TDP). Just as with TDD, write your tests before asking the AI to generate code. This ensures correctness.
12. Focus on security. Never trust the AI with security-critical code. Always run automated scanners and do manual reviews for sensitive areas.
13. Conduct targeted code reviews. AI often misses edge cases. Review error handling, input validation, and pattern consistency carefully.
14. Document the "why." When integrating AI code, explain why you accepted its output. This preserves context and transparency.
15. Schedule “AI code cleanup” sessions. Dedicate time to refactor and reduce AI-introduced technical debt regularly.

Performance and Optimization

16. Profile the generated code. AI favors readability over efficiency. Profile data structures, loops, and queries before committing.
17. Refine bad refactoring. Let the AI suggest refactors but guide it when it's inefficient. Your judgment remains key.
18. Use AI for data-heavy tasks. Let it handle tedious, structured tasks like writing SQL, regex, or schemas - it excels there.

Continuous Learning

19. Learn from the code. Use the AI to explore new libraries and languages. Ask for explanations instead of full solutions.
20. Experiment with different models. Some models excel at debugging, others at planning. Test and adopt the right tool for each task.

Vibe Coding Pro-tips for senior engineers

50 Tips for Working with AI in Development

1. Spend more time providing context to the AI before generating code to ensure better alignment with your vision.
2. Break the work into small, manageable chunks to maintain control and reduce errors.
3. Associate tests with each chunk of work to verify functionality early.
4. Make tests robust and exhaustive to catch issues before they compound.
5. Review and understand every piece of AI-generated code to avoid black-box problems.
6. Commit changes to a repository only after tests pass for easy rollbacks.
7. Use a structured format: Find the issue, create a plan, review the plan, then execute it.
8. Do a two-step process: First plan without coding, then implement.
9. Use different AIs for planning (e.g., Gemini) and coding (e.g., Sonnet) for optimal results.
10. See yourself as the manager and the AI as a literal direct report - give clear, specific instructions.
11. Go slower to enjoy the process and build deliberately, rather than rushing.
12. Log sessions for future context to track decisions and iterations.
13. Create a full plan but don't give it all to the AI at once - vibe and chat to build features gradually.
14. Spend time on context engineering to elevate beyond hobbyist "slop" and achieve professional results.
15. Start with a readme file including requirements, tech stack, and milestones (vibe PMing first)
16. Break tasks into one small micro-step at a time for better manageability.
17. Never stop vibing, but always break into smaller parts.
18. Start with components that have no dependencies to build momentum.
19. Use frameworks to standardize and speed up development.
20. Avoid ambiguity in prompts - be explicit about what you want.
21. Name variables, functions, and components consistently and clearly.
22. Commit to GitHub with prompt response summaries and changed files for better tracking.
23. Work alone if you want to go fast; add resources later for scaling.
24. Trace the full user flow and identify root causes when fixing bugs, treating symptoms as clues.
25. Commit changes to Git often for easy rollbacks and to help agents reference older code versions.
26. Apply systems thinking: Design the architecture yourself, let AI handle execution details.
27. Start with something simple and deploy/test every turn.
28. Make incremental improvements rather than big leaps.
29. Ask AI to explain technical aspects and confess limitations.
30. Create an ARCHITECTURE.md file with explanations of files and functions; update it with every change.
31. Understand the vibe as grounded in technical reality - know what's possible, expensive, or fragile.
32. Use a setup with multiple tools for efficiency, like Cursor for coding and Claude for planning.
33. Baby-step prompts: Feed context gradually, read docs, and plan in small, concrete steps.
34. Adopt practices like frequent testing to prevent unintended consequences from piling up.
35. Write a high-level plan and break it into small phases.
36. Add as much context as possible to each phase's plan, including what to build and avoid.
37. Discard and restart if the AI messes up - don't force fixes on bad foundations.
38. Know the AI's coding personality to tailor prompts effectively.
39. Create frequent checkpoints to revert to working versions easily.
40. Start with plain English descriptions, then iterate by specifying refinements.
41. Solve hyper-specific personal problems first - they often scale to broader use.
42. Embrace browser-centric development, like Chrome extensions, for high user engagement.
43. Integrate real-world inputs like image recognition or voice for intuitive apps.
44. Automate repetitive workflows, such as meeting prep or email handling.
45. Build for daily use and stickiness by solving ongoing pain points.
46. Use Supabase for easy backend integration in tools like Lovable or Bolt
47. Deploy and share early to evolve personal tools into shared products.
48. Use animation and visual feedback for engaging UIs.
49. Generate detailed product requirements and user stories with AI before coding.
50. Learn new technologies through AI-assisted exploration, troubleshooting issues as they arise.

Pitfalls and Gotchas to Watch Out For

Vibe coding is powerful, but it isn't magic - you still need human judgment to avoid bugs, security issues, and other pitfalls. As a beginner using these tools, keep the following caveats in mind to make your journey smoother and safer:

• The 70% Problem (AI's last-mile weakness). It's often said that AI can get you 70% of the way to a working app very quickly, but the final 30% of polish is the hardest[92][93]. In practice, you might have an app that basically works, but edge cases and subtle bugs lurk underneath. For example, you generated a shopping cart app in minutes, but upon testing you find it doesn't handle an empty cart correctly on checkout, or it fails if the product name has special characters. These kinds of issues often require traditional debugging and careful thinking. Don't be discouraged - this is a normal part of software development. The AI got you very far very fast (which is amazing!), but be prepared to roll up your sleeves for the troubleshooting phase. Use the AI to help with debugging by all means (ask it to explain errors or suggest fixes), but know that you might need to iteratively test numerous times. Many vibe coders say the first 1-2 prompts feel like "magic," but the refinement stage can still be time-consuming. This is where you build your understanding and improve the app beyond the initial vibe.


"Vibe coding your way to a production codebase is clearly a terrible idea. Most of the work we do as software engineers is about evolving existing systems, and for those the quality and understandability of the underlying code is crucial." - Simon Willison

• Test as you go - ruthlessly. When you're not inspecting code, testing becomes your safety net. Run your app frequently (most tools let you preview live in the browser) and try to break it. Enter weird inputs, click all the buttons, use it as a real user would. After every major change the AI makes, do a quick sanity check that previous features still work (sometimes a new change can introduce a regression - a phenomenon one might call the "two steps back" pattern[94]). It's much easier to catch and fix issues when you test incrementally, rather than letting the AI build 10 features and then discovering nothing works. In vibe coding, small, iterative testing prevents nightmare debugging later[95]. If something breaks, you can immediately point it out to the AI while the context is fresh.
• Don't blindly trust generated code - especially for sensitive logic. Vibe coding tools can and do make mistakes. They might use an inefficient approach, miss a security check, or produce code that works on the surface but isn't best practice. If you're building something that involves payments, personal data, or anything security-critical, pause and review the relevant code carefully (or have the AI explain it line-by-line). For instance, if you had the AI integrate Stripe for payments, double-check that the API keys aren't exposed and that webhooks are handled properly. The AI might not handle secrets safely by default - there have been cases of vibe-coded apps accidentally leaking database credentials or API keys because the AI didn't know better[96]. The motto here is "trust, but verify." Use vibe coding to accelerate development, but if you intend to deploy to real users, allocate time for a thorough code review or use tools like Cursor's code analysis to catch issues[97][98].
• Be mindful of security and privacy. Building on the above: remember that the AI is trained on general data and doesn't automatically know your security or privacy requirements. It may, for example, include an analytics script you didn't ask for, or allow actions that should be restricted. Always specify security needs in your prompts (e.g. "ensure only logged-in users can access the dashboard") and test those boundaries. If your app handles user data, confirm that the AI set up proper access control. Some vibe coding platforms include basic guardrails (Zapier's review noted all the top tools had authentication and security checks by default)[99][100], but you should not assume everything is safe until you've verified it. Also, be cautious not to paste truly sensitive info into these tools - while many have privacy policies, you're still sending data to an AI service.
• Watch out for model limitations and hallucinations. AI models sometimes "hallucinate" - produce code or answers that look confident but are incorrect. You might see it import a library that doesn't exist, call functions with wrong parameters, or invent an API response format. If something in the output looks unfamiliar or too good to be true, double-check it. When the AI says, "I've set up a secure authentication for you," confirm that by testing registration and login flows. If it says, "Using the payment API's createCharge method," verify that method in the API's docs (some AIs might reference outdated versions). In short, keep a critical eye. The AI is a partner, but not infallible.
• Token limits and cost. Most vibe coding platforms run on powerful models like GPT-4 or Claude, which have context length limits and can incur cost as you use more tokens. If your conversation becomes very long, the AI might forget earlier details or code (though many tools handle this by summarizing or focusing on recent messages). You might need to re-provide context occasionally. Additionally, pay attention to your credit usage on each platform. It's easy to get carried away adding features and then find out you've exhausted the free tier. As one blogger joked, "the risk of overspending" on API calls is a real consideration[101]. To avoid this, plan your prompts thoughtfully (batch small changes into one prompt if you can) and take advantage of free tiers across tools if needed (e.g. do some in Bolt, some in Lovable). Some platforms like Tempo won't charge for bug-fix prompts to alleviate this[70]. Still, it's good to keep an eye on usage dashboards.
• When in doubt, ask for help or search the community. Vibe coding is new, and you may run into an issue where you're not sure if it's your prompt or the tool's fault. The good news: communities are forming around each of these platforms (Discord servers, forums, subreddits like r/vibecoding). If you hit a wall - say the AI just isn't producing the feature you want - chances are someone else encountered a similar challenge. Don't hesitate to ask, "Has anyone gotten X to work in Tool Y?" Often, another user or the devs will have guidance (maybe a special syntax or a workaround prompt). This human layer of support can complement the AI's assistance.
• Maintain a learning mindset. If you're new to programming, vibe coding is a fantastic gateway. But if your goal is to eventually build more complex or production-grade systems, treat each AI interaction as a learning opportunity. When the AI fixes a bug, read the diff if you can and try to understand the change. When it outlines a plan, absorb the rationale. Over time, you'll start picking up programming concepts and patterns. One potential downside of relying solely on AI is skill atrophy - if you never challenge yourself to understand the code, you won't grow your own abilities[102][103]. To counteract that, take moments to review or even manually tweak small parts of the code. You might try writing a tiny function yourself and see if the AI accepts it. This keeps you in the loop and helps you become a better builder. Remember, vibe coding should empower you, not make you complacent. As I argued in "Vibe Coding is not an excuse for low-quality work," you still want to uphold some standards and continuously improve your understanding of what the AI produces[104].

In short, vibe coding gives you a tremendous head start - but you're the team lead, and the AI is like an eager junior developer. It will do exactly what you say (and sometimes what you don't say but it thinks you implied!). It's up to you to test, double-check critical parts, and guide the project to success. If you use these tools safely, effectively, and responsibly[105], you'll avoid the common traps around security, privacy, and reliability while benefiting from insane development speed.

Conclusion: The Future of Prototyping is Conversational

Vibe coding democratizes software creation - it's turning non-programmers into app builders and letting developers work at a higher level of abstraction. We're witnessing a shift where anyone with an idea can potentially bring it to life without writing code, simply by communicating their vision to an AI[7][106]. This is a profound change. As Simon Willison noted, most people in the world never learned to code, but thanks to vibe coding tools, those people now have a path to build custom software to solve their problems[105][107]. That's a huge expansion of who gets to be "a coder." We're not just empowering developers - we're expanding the very definition of a developer to include anyone who can articulate an idea in words.

As someone deeply involved in this space, I'm excited by how quickly the ecosystem is evolving. The big players like Google are integrating vibe coding into their platforms, startups like those behind Lovable and Bolt are innovating rapidly, and open-source communities are exploring their own AI builder tools. The concept of "personal software" - apps tailored for an individual's needs, built by that individual via AI - is becoming real[108]. We are moving toward tools that adapt to people, rather than people having to learn complex tools.

For beginners today, the opportunity is unprecedented. You can build real, working software without years of programming education. My advice is: just start building. Pick a tool (try the free ones first), and spin up a small project that interests you. It could be anything - a mood tracker, a simple game, a website for your hobby. Don't worry that you don't know how to code feature X or Y; describe what you want and see what the AI comes up with. You'll learn by doing, and by the end, you might have something useful to show for it. Each project will teach you more about how to prompt and how apps are structured. Who knows - that little weekend project could turn into a startup MVP or a handy tool that saves you and others time.

Before we close, a word of realism: vibe coding doesn't eliminate the need for traditional software engineering, especially for large-scale or mission-critical systems. Instead, it augments and accelerates the early stages of development and prototyping. It's Beyond Vibe Coding (to borrow my book title) - meaning, vibe coding gets you started, and then you apply engineering rigor to go beyond the prototype. If you find yourself loving the process, I encourage you to deepen your skills. Explore resources on prompt engineering (there's an art and science to it), context management, and AI ethics/safety. These will be valuable as AI continues to integrate into development workflows.

Feel free to check out my book Beyond Vibe Coding for a comprehensive look at mastering AI-assisted development across the spectrum - from quick vibes to production-ready systems[109][110]. And if you're interested in a step-by-step guide on one tool in particular, Building Web Apps with Bolt will walk you through an AI-first development approach using Bolt, reinforcing many of the principles we discussed (with a deeper dive into prompt techniques, iterative design, and case studies). I also regularly share insights and updates on my Substack newsletter - including comparisons of tools and new techniques - which you might find helpful as you continue your journey.

The bottom line: We've entered an era where coding is becoming a conversation. You bring the ideas and the "vibes," and the AI helps manifest them in code. So go forth and create! Turn your vibes into apps, experiment boldly, and don't be afraid to push the limits of these tools. The more you play with them, the better you'll understand both their power and their boundaries. Happy vibe coding - I can't wait to see what you build next.