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In Which I Vibe-Code a Personal Library System
When I was a kid, I was interested in a number of professions that are now either outdated, or have changed completely. One of those dreams involved checking out books and things to patrons, and it was focused primarily on pulling out the little card and adding a date-due stamp.
Of course, if you’ve been to a library in the last 20 years, you know that most of them don’t work that way anymore. Either the librarian scans special barcodes, or you check materials out yourself simply by placing them just so, one at a time. Either way, you end up with a printed receipt with all the materials listed, or an email. I ask you, what’s the fun in that? At least with the old way, you’d usually get a bookmark for each book by way of the due date card.
As I got older and spent the better part of two decades in a job that I didn’t exactly vibe with, I seriously considered becoming a programmer. I took Java, Android, and UNIX classes at the local junior college, met my now-husband, and eventually decided I didn’t have the guts to actually solve problems with computers. And, unlike my husband, I have very little imagination when it comes to making them do things.
Fast forward to last weekend, the one before Thanksgiving here in the US. I had tossed around the idea of making a personal library system just for funsies a day or so before, and I brought it up again. My husband was like, do you want to make it tonight using ChatGPT? And I was like, sure — not knowing what I was getting into except for the driver’s seat, excited for the destination.
Vibing On a Saturday Night
I want to make a book storage system. Can you please write a Python script that uses SQL Alchemy to make a book model that stores these fields: title, author, year of publication, genre, and barcode number?
So basically, I envisioned scanning a book’s barcode, pulling it up in the system, and then clicking a button to check it out or check it back in. I knew going in that some of my books don’t have barcodes at all, and some are obliterated or covered up with college bookstore stickers and what have you. More on that later.
First, I was told to pip install sqlalchemy, which I did not have. I was given a python script called books_db.py to get started. Then I asked for code that looks up all the books and prints them, which I was told to add to the script.
Then things were getting serious. I asked it to write a Flask server and a basic HTML front end for managing the books in the system. I was given the Flask server as app.py, and then some templates: base.html to be used by all pages, and index.html to view all the books, and add_book.html to, you know, add a new book. At that point, I got to see what it had created for the first time, and I thought it was lovely for a black and white table. But it needed color.

Check It Out

I asked the chat-thing for features and implemented them piecemeal, as you do if you’re not a masochist. First up was a cute little trash-can delete-button for every entry. Then it was time to set up the CheckoutEvent. Each of these events records which book it belongs to, whether it’s a check-out or check-in event, and the timestamp of said event. Of course, then it was time to get the checkout history wired to the front-end and accessible by clicking a book’s title.
All I really had to do was add a history route to app.py, update index.html to make the titles clickable, and create the book_history.html it spat out. Then I had it add the buttons for checking in and out on the new checkout history page, which involved adding routes to app.py as well as a helper to compute the current status.
Then it had me modify the history route and update book_history.html with the actual buttons. And they’re super cute, too — there’s a little red book on the checkout button, and a green book on the check-in.
Barcode Blues
On the index.html page, can you add a barcode number-based search box? And when the user searches, redirect them to the book page for that barcode?
Now it was time to get the barcode scanning situation up and running. I was sure at some point that ChatGPT would time me out for the night since I use the free model, but it just kept helping me do whatever I wanted, and even suggesting new features.
I wanted the barcode handling to be twofold: one, it should definitely pull the checkout page if the book exists in the system, and it should also definitely go to the book-entering page if not.
Yes — that’s a great workflow feature.We’ll add a barcode search box to your index page, and when someone submits a barcode, the app will:
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Look up the book by barcode -
Redirect straight to that book’s checkout history page -
Show a nice error if the barcode doesn’t exist
I did what it told me, adding a barcode search route in app.py and updating the index() route to use it. I then added its barcode search form to index.html. It was at this point that I had to figure out a way to generate barcodes so I could make little stickers for the books that lack them entirely, or have otherwise obliterated ones.
I have a pretty basic 1D barcode scanning gun, and it won’t scan everything. As I soon found out, it prefers fake EAN barcodes to UPCs altogether. I finally found an online barcode generator and got to work, starting with a list of randomly-generated numbers I made with Excel. I decided I wanted all the fake barcodes to start with 988, which is close enough to the ISBN 978 lead-in, and happens to use my favorite number twice.
We took a brief detour as I asked the chat-thing to make the table to have ascending/descending sorting by clicking the headers. The approach it chose was to keep things server-side, and use little arrows to indicate direction. I added sorting logic to app.py and updated index.html to produce the clickable headers, and also decided that the entries should be color-coded based on genre, and implemented that part without help from GPT. Then I got tired and went to bed.
The Long, Dark Night of the Solo Programmer
I’m of a certain age and now sleep in two parts pretty much every night. In fact, I’m writing this part now at 1:22 AM, blasting Rush (2112) and generally having a good time. But I can tell you that I was not having a good time when I got out of bed to continue working on this library system a couple of hours later.
There I was, entering books (BEEP!), when I decided I’d had enough of that and needed to try adding more features. I cracked my knuckles and asked the chat-thing if it could make it so the search works across all fields — title, author, year, genre, or barcode. It said, cool, we can do that with a simple SQLAlchemy or_ query. I was like, whatever, boss; let’s get crazy.
Can you make it so the search works across all fields?
It had me import or_ and update the search route in app.py to replace the existing barcode search route with a generalized search using POST. Then I was to update index.html to rename the input to a general query. Cool.
But no. I messed it up some how and got an error about a missing {% endblock %}. In my GPT history it says, I’m confused about step 2. Where do I add it? And maybe I was just tired. I swear I just threw the code up there at the top like it told me to. But it said:
Ah! I see exactly why it’s confusing — your current index.html starts with the <h1> and then goes straight into the table. The search form should go right under the <h1> and before the table.
Then I was really confused. Didn’t I already have a search box that only handled barcodes? I sure did, over in base.html. So the new search code ended up there. Maybe that’s wrong. I don’t remember the details, but I searched the broader internet about my two-layer error and got the thing back to a working state many agonizing minutes later. Boy, was I proud, and relieved that I didn’t have to ask my husband to fix my mistake(s) in the morning. I threw my arms in the air and looked around for the cats to tell them the good news, but of course, I was the only one awake.
Moar Features!
I wasn’t satisfied. I wanted more. I asked it to add a current count of books in the database and display it toward the top. After that, it offered to add a count of currently-checked-out vs. available books, to which I said yes please. Then I wanted an author page that accepts an author’s name and shows all books by that author. I asked for a new page that shows all the books that are checked out. Most recently, I made it so the search box and the column headers persist on scroll.
I’m still trying to think of features, but for now I’m busy entering books, typing up check-out cards on my IBM Wheelwriter 5, and applying library pockets to the inside back covers of all my books. If you want to make your own personal library system, I put everything on GitHub.
On the Shoulders of Giants (and Robots)
I couldn’t have done any of this without my husband’s prompts and guidance, his ability to call shenanigans on GPT’s code whenever warranted, and ChatGPT itself. Although I have programmed in the past, it’s been a good long time since I even printed “Hello, World” in any language, though I did find myself recalling a good deal about this and that syntax.
If you want to make a similar type of niche system for your eyes only, I’d say this could be one way to do it. Wait, that’s pretty non-committal. I’d say just go for it. You have yourself and the broader Internet to check mistakes along the way, and you just might like some of the choices it makes on your behalf.
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What is Vibe Coding? A Comprehensive Guide
Vibe coding is emerging as a transformative shift in how developers write software. It’s not just a buzzword—it reflects a new, more natural way of interacting with code. At its core, vibe coding means working alongside AI to turn ideas into software through simple, intuitive prompts. You focus on what you want to build, and the AI helps figure out how.
This change is already well underway. According to the 2024 Stack Overflow Developer Survey, 82% of developers who use AI tools rely on them primarily to write code. That’s a massive endorsement of how AI is being integrated into everyday workflows. Vibe coding tools like GitHub Copilot, ChatGPT, Replit, and Cursor are leading the charge—helping developers stay in the zone, generate code faster, and reduce mental overhead.
These tools do more than autocomplete—they understand context, learn from your style, and adapt to your intent. Instead of switching tabs to search for syntax or boilerplate, you stay in flow. This is what vibe coding is all about: building software in a way that feels more like thinking out loud than writing line-by-line instructions.
As the pressure to ship faster and innovate grows, vibe coding is quickly becoming more than just a developer convenience—it’s a competitive advantage. In this guide, we’ll explore how it works, where it fits in real-world workflows, and why it’s shaping the future of development.
What Is Vibe Coding?
Vibe coding is a modern way of programming where you describe what you want to build in plain language, and A tool helps turn those ideas into working code. It shifts the focus from memorizing syntax to simply communicating your intent.
At the heart of vibe coding are AI agents powered by Large Language Models (LLMs) like GPT-4. These agents can understand context, suggest code, debug errors, and even make architectural decisions based on what you’re trying to do.
Instead of writing every line by hand, you might say, “Build a login page with email and password inputs,” and the AI will generate the layout and logic behind it. You’re not losing control and just coding at a higher level, faster and with fewer distractions.
This approach is redefining software development. By putting intention first and letting AI handle the heavy lifting, vibe coding allows you to focus more on solving problems and less on fighting with syntax.
Origin of the Term “Vibe Coding”
The term vibe coding was first coined by Andrej Karpathy, a prominent AI researcher and former director of AI at Tesla. He mentioned it casually on social media, but the phrase quickly gained traction among developers experimenting with AI-assisted workflows.
Karpathy used vibe coding to describe a new style of programming—one where you don’t sweat every detail of syntax. Instead, you describe your goals, and AI helps fill in the gaps. It’s about coding in a flow state, where you and the machine work together almost like a creative partnership.
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This concept took off with the rise of tools like GPT, Replit, and Cursor. These platforms let developers prompt in plain language, get structured output, and stay in momentum without switching contexts.
In that sense, vibe coding isn’t just a phrase—it reflects a shift in how we think about building software with AI as an active collaborator.
How Vibe Coding Works (Step-by-Step Breakdown)
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Vibe coding isn’t just about letting AI write code—it’s about guiding it with your ideas. You give the direction, and the AI helps build from there. The process is simple, intuitive, and keeps you in control. Here’s how it works, step by step.
Step 1: Start with a prompt
Vibe coding begins with you describing what you want to build. You don’t write raw code at first. Instead, you use plain, structured language. For example, you might say, “Create a landing page with a signup form and responsive layout.” The key is being clear and direct.
Step 2: AI interprets your intent
Once you submit your prompt, an AI agent—like GPT-4, Replit’s AI, or Cursor—steps in. It reads your input, understands the context, and generates the base code. This code isn’t random. It’s often clean, modular, and aligned with modern best practices.
Step 3: You review and iterate
After the first draft, you read through the output. If something’s off or missing, you give feedback in natural language. You can say, “Add error handling,” or “Make the layout mobile-friendly.” The AI updates the code instantly. It becomes a back-and-forth conversation, like working with a real teammate.
Step 4: Test and deploy
Once the code looks good, you can run tests right inside platforms like Replit. These environments often support live previews, version control, and easy deployment. From prototype to production, vibe coding supports the full workflow.
Throughout the process, you’re not digging through documentation or chasing syntax errors. You’re focused on solving problems and building—fast.
What are the benefits of vibe coding
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Vibe coding isn’t just faster—it’s smarter. It helps you build more with less effort and unlocks creativity at every step. Whether you’re a seasoned developer or just starting out, the advantages are real and immediate. Let’s break down what makes it so powerful.
Faster prototyping
Vibe coding helps you move from idea to working prototype in minutes. You don’t get bogged down in setup or boilerplate. Just describe what you want, and the AI builds a solid starting point. It’s perfect for testing concepts quickly.
More accessible for non-programmers
You don’t need to be a coding expert to use vibe coding. With natural language inputs, even designers, marketers, or founders can contribute to building tools and apps. This lowers the barrier and opens up software creation to more people.
Less boilerplate, more creativity
AI agents handle repetitive code patterns like form setup, input validation, and file structures. That frees up your brain for the fun parts—like design, user experience, and problem-solving. It shifts coding from a technical chore to a creative process.
Support for voice and visual prompts
Tools like Superwhisper are taking vibe coding even further. You can speak your ideas out loud, and the system will understand and respond. Some tools are also exploring visual prompting, where you sketch or describe layouts instead of typing everything.
Improved focus and flow
By reducing friction in the process, vibe coding helps you stay in a creative rhythm. You’re not constantly switching between tabs or looking up syntax. You just build—and the AI keeps pace with you.
Tools That Enable Vibe Coding
Vibe coding wouldn’t be possible without the right tools. These platforms bring the concept to life by turning natural language into usable code. Whether you’re typing, clicking, or even speaking your prompts, these tools help you stay in flow. Here are some of the most powerful ones leading the way.
Replit Ghostwriter and Replit AI Agent
Replit Ghostwriter is an AI pair programmer built directly into the Replit IDE. It suggests code, explains concepts, and helps debug in real time. With the introduction of the Replit AI Agent, developers now have an even smarter assistant. The agent can execute tasks, refactor code, and answer questions using plain language. This combo allows you to code faster without switching contexts.
Cursor
Cursor is a code editor built from the ground up with AI in mind. It integrates with GPT-4 and allows for conversational coding directly within your files. You can highlight sections of code, ask questions, or give instructions like “optimize this function.” Cursor tracks your intent and makes targeted edits, reducing the back-and-forth typical of traditional IDEs. It’s designed for deep workflow integration.
Superwhisper
Superwhisper brings voice-to-code functionality into the mix. It uses Whisper (by OpenAI) and integrates with your editor, allowing you to speak your coding prompts. This tool is especially helpful for hands-free coding or for users who prefer talking through their logic. It adds an entirely new dimension to vibe coding by combining speech recognition with intent-driven generation.
Quick comparison
Replit is great for all-in-one workflows with built-in hosting. Cursor is ideal for local, power-user setups with deep file integration. Superwhisper adds a voice layer on top of your existing tools. Together, they form a flexible toolkit for different styles of vibe coding.
Real-World Use Cases of Vibe Coding
Vibe coding isn’t just a cool concept—it’s already changing how people build. From solo creators to startups and enterprise teams, developers are using it to work smarter and faster. These real-world examples show how flexible and practical this approach can be across different goals and skill levels.
Personal project prototypes
Vibe coding is perfect for turning your ideas into working projects without writing every line from scratch. Whether it’s a portfolio site, a game, or a passion app, you can build faster by prompting AI in natural language. This lets you focus on creativity instead of getting stuck on boilerplate code.
Rapid MVP for startups
Early-stage startups often need to move fast with limited resources. Vibe coding helps founders and small teams create minimum viable products (MVPs) in days instead of weeks. You can describe features like “build a user dashboard with analytics” and have a functioning version ready for testing almost immediately.
Educational coding experiences
For students and beginners, vibe coding lowers the intimidation barrier. Instead of memorizing syntax, they can learn by doing—asking the AI questions, experimenting, and seeing code come to life. It’s also great for teachers who want to make programming more interactive and less overwhelming.
Corporate internal tools or automations
Software development companies can use vibe coding to quickly build internal dashboards, workflows, and automation scripts. Tasks like “create a form to collect team feedback and send it to Slack” can be executed with just a prompt. It cuts down development time and allows non-engineering teams to contribute to tool-building.
Best Practices for Effective Vibe Coding
To get the most out of vibe coding, it’s not enough to rely on AI alone. You still need to guide the process with intention and clarity. These best practices will help you write better prompts, clean up AI-generated code, and keep everything running smoothly. Think of them as your cheat sheet for coding with flow.
Write better prompts
The quality of your input shapes the quality of the output. Be specific and clear when describing what you want. Instead of saying “build a form,” try “create a contact form with name, email, message fields, and a submit button.” The more context you give, the better the AI performs.
Structure output for maintainability
AI-generated code can be quick, but it’s your job to keep it clean. Ask the AI to organize the code into functions or modules. Use consistent naming, add comments, and refactor where needed. Clean code is easier to maintain, debug, and scale later on.
Validate and test the results
Never assume the AI’s code is perfect. Test everything. Run unit tests, check edge cases, and verify outputs manually. AI can make small mistakes that break your app or cause security issues. Always review before shipping.
Keep a human in the loop
Vibe coding is powerful, but it’s not fully autonomous. You’re still the decision-maker. Use the AI as a creative assistant, not a replacement. Stay involved, guide the output, and step in when the code needs human judgment or domain knowledge.
Vibe Coding vs Traditional Coding
Vibe coding isn’t here to replace traditional coding—it’s here to complement it. Each approach has its strengths depending on the context, goals, and complexity of the project. Understanding the key differences can help you choose the right tool for the job and work more effectively. Let’s break it down.
Side-by-side comparison table
Here’s a quick breakdown of how vibe coding differs from traditional coding in key areas:
| Feature/Aspect | Vibe Coding | Traditional Coding |
|---|---|---|
| Input Style | Natural language prompts | Strict syntax-based code |
| Speed | Fast prototyping and iteration | Slower due to manual structure |
| Learning Curve | Lower—good for beginners and non-coders | Steep—requires strong technical knowledge |
| Creativity | Encourages experimentation | More constrained by syntax and structure |
| Tooling | AI agents, LLMs (GPT-4, Replit, Cursor) | Text editors, IDEs, compilers |
| Collaboration | Supports natural teamwork and voice/visual inputs | Often technical and siloed |
When to use each approach
Use vibe coding when speed, flexibility, or idea exploration matters—like MVPs, internal tools, or learning environments. It’s ideal for early-phase development or when working with non-technical teammates. Use traditional coding for performance-heavy apps, systems programming, or when full control over every detail is critical—like in large-scale enterprise or infrastructure projects.
Developer roles in a vibe-first workflow
In vibe coding, developers act more like product designers and strategists. They define features, guide AI agents, and review outputs. Senior devs may focus on system architecture and validating AI-generated code. Junior devs can contribute faster by learning from real-time feedback and prompt-based interactions. Non-developers (like designers or PMs) can even join the build process, giving input in plain language that the AI can understand.
Conclusion
Vibe coding is more than just a new way to write code—it’s a shift in how we think about building software. By using natural language, AI agents, and large language models, developers can move faster, stay focused, and spend more time solving real problems.
Throughout this guide, we’ve explored what vibe coding is, how it works, and where it fits into modern workflows. We looked at tools like Replit, Cursor, and Superwhisper, and saw how developers—from beginners to pros—are using them to prototype, learn, and launch real projects.
If you’re curious about vibe coding, the best way to understand it is to try it. Open a tool like Replit or Cursor and start prompting. Don’t worry about getting it perfect. Just experiment, explore, and build something.
The future of coding is more intuitive, collaborative, and creative. Stay ahead by embracing this new way of thinking—and see where it takes you.
The post What is Vibe Coding? A Comprehensive Guide appeared first on TopDevelopers.co.