I’ve been tinkering with development since the ’90s, starting with HTML, JavaScript, PHP, and whatever else I could get running long enough to build something useful. I was never a full-time developer, but I knew enough to understand the basic logic of how things worked. The problem is that those skills atrophy when you’re not using them every day. Syntax gets rusty. Tools change. The simple thing you wanted to build suddenly turns into setup issues, outdated tutorials, and a reminder that programming rewards repetition.
That’s why vibe coding has changed the way I look at programming. I don’t think AI can do it all for you, at least not reliably. Pretending otherwise is where things can go wrong fast. But it can lower the barrier to entry in a way older tools never really did. Instead of needing to remember every command, structure, or convention before you can make progress, you can describe what you’re trying to build and start working through the logic. If you’ve considered programming in the past but never stuck with it, today’s AI tools make that first step feel a lot more realistic.
AI makes the blank screen less intimidating
It gives you something to react to instead of nothing to start from
The blank screen is really a stand-in for everything that happens before the actual code takes shape. It’s not just an empty code editor. It’s deciding which language to use, remembering the syntax, setting up the file structure, figuring out where the logic should live, and knowing what the first line is supposed to be. That’s where a lot of people get intimidated by programming. Even when you have a clear idea of what you want to build, the gap between that idea and a working first version can feel much bigger than the project itself.
Quiz
Artificial intelligence basics
Trivia challenge
From chatbots to neural networks — find out how much you really know about AI.
ConceptsHistoryToolsEthicsModels
What does the term ‘machine learning’ most accurately describe?
Correct! Machine learning is a branch of AI where systems improve automatically through experience and exposure to data. Instead of being explicitly programmed for every task, these systems identify patterns and make decisions with minimal human intervention.
Not quite. Machine learning refers to systems that learn from data to improve their performance over time. It’s less about physical movement or exact mimicry and more about finding patterns in large datasets to make predictions or decisions.
Who is widely credited with coining the term ‘artificial intelligence’ in 1956?
Correct! John McCarthy coined the term ‘artificial intelligence’ at the famous Dartmouth Conference in 1956, which is considered the founding event of AI as a formal field of research. He later invented the Lisp programming language, which became a staple in early AI development.
Not quite. While Alan Turing, Marvin Minsky, and Claude Shannon were all AI pioneers, it was John McCarthy who coined the term ‘artificial intelligence’ at the Dartmouth Conference in 1956. McCarthy went on to shape the field enormously throughout his career.
What type of AI model powers popular chatbots like ChatGPT?
Correct! ChatGPT and similar chatbots are powered by large language models, or LLMs. These models are trained on enormous amounts of text data and learn to predict and generate human-like language, making them capable of conversation, writing, and reasoning tasks.
Not quite. ChatGPT is built on a large language model (LLM). While decision trees and Bayesian classifiers are real AI tools, they’re used for much simpler tasks. CNNs are great for image recognition but aren’t designed for open-ended language generation.
What is ‘overfitting’ in machine learning?
Correct! Overfitting happens when a model learns the training data too well — including its noise and quirks — and then fails to generalize to new, unseen data. It’s like a student who memorizes practice exam answers but can’t handle different questions on the real test.
Not quite. Overfitting describes a model that has learned the training data so specifically that it performs poorly on new data. It’s one of the most common challenges in machine learning and is addressed through techniques like cross-validation and regularization.
What is ‘AI bias’ most commonly referring to?
Correct! AI bias refers to systematic errors or unfair outcomes that arise when a model is trained on skewed, incomplete, or unrepresentative data. For example, facial recognition systems have been shown to perform worse on darker skin tones due to biased training datasets, raising serious ethical concerns.
Not quite. AI bias is about systematic, often harmful unfairness baked into a model’s outputs, usually due to skewed training data or flawed design choices. It’s a major ethical concern in areas like hiring algorithms, criminal justice tools, and medical diagnostics.
What does ‘GPT’ stand for in AI model names like GPT-4?
Correct! GPT stands for Generative Pre-trained Transformer. ‘Generative’ means it can create new content, ‘pre-trained’ means it was trained on a large dataset before being fine-tuned, and ‘Transformer’ refers to the neural network architecture that made modern LLMs possible.
Not quite. GPT stands for Generative Pre-trained Transformer. The Transformer architecture, introduced in a landmark 2017 paper called ‘Attention Is All You Need,’ revolutionized natural language processing and laid the groundwork for today’s powerful AI chatbots.
Which of the following best describes ‘deep learning’?
Correct! Deep learning is a subset of machine learning that uses artificial neural networks with many layers — hence ‘deep’ — to model complex patterns in data. It’s the technology behind image recognition, voice assistants, and most modern AI breakthroughs.
Not quite. Deep learning uses multi-layered neural networks inspired loosely by the human brain. The ‘depth’ refers to the number of layers in the network, and more layers generally allow the model to learn more complex and abstract representations of data.
What was the name of the IBM AI system that famously defeated chess champion Garry Kasparov in 1997?
Correct! IBM’s Deep Blue defeated world chess champion Garry Kasparov in a six-game match in 1997, marking a landmark moment in AI history. It was the first time a computer beat a reigning world chess champion under standard tournament conditions, shocking the world.
Not quite. The IBM system was called Deep Blue. Watson is IBM’s later AI known for winning Jeopardy!, while AlphaGo is Google DeepMind’s system that mastered the board game Go in 2016. HAL 9000, of course, is the fictional AI from Stanley Kubrick’s 2001: A Space Odyssey.
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AI changes that dynamic, allowing you to start with the idea instead of the structure. Say you want to build a simple Python script that watches your Downloads folder and moves images, PDFs, and ZIP files into separate folders. Without help, you might need to look up how Python handles file paths, how to scan a directory, how to check file extensions, and how to move files without breaking anything. With an AI coding tool, you can describe that exact task in plain English and get a rough first version. It probably won’t be perfect, and you shouldn’t trust it blindly, but now you’re editing, testing, and asking better questions instead of trying to summon the entire project from memory. That’s a much better place to start from.
Fast feedback makes programming feel possible
Each small fix makes the project feel less out of reach
The biggest difference with vibe coding isn’t just that AI can generate code. It’s that it can help you move through the feedback loop faster. That matters because programming can feel impossible when every mistake sends you back to searching forums, reading documentation, or trying to decode an error message that assumes you already know what you’re doing. With AI, the error is no longer the end of the road. You can paste it in, explain what you were trying to do, and ask what went wrong. That doesn’t mean the answer will always be perfect, but it gives you a next step.
That’s especially useful for the kind of projects I’d actually want to build, like a simple website or a browser extension that saves reusable AI prompts. Maybe the first version has a text box, a save button, and a list of saved prompts. Then you realize the prompts don’t sync across browsers, so you ask how to make that happen. Then the layout looks terrible, so you ask how to clean up the CSS. Then the button works in one browser but not another, so you troubleshoot that next. None of those steps are magic, and you still have to test everything, but the project starts to feel like a series of solvable problems instead of one big wall you have to climb all at once.
You still need judgment once the code exists
AI can write the first draft, but you still have to know what you’re accepting
This is where the hype around vibe coding can get dangerous. Getting code on the screen is not the same thing as having finished software. AI can generate something that looks right, runs once, and still has obvious problems hiding underneath. It might use outdated methods, skip error handling, store data in a way that doesn’t make sense, or solve the immediate problem while creating three new ones you don’t notice until later.
That doesn’t make vibe coding useless. It just means your role changes. Once the code exists, you have to test it, question it, and understand it well enough to know whether it actually does what you asked for. If you’re building a browser extension to save AI prompts, for example, you need to know where those prompts are stored, whether they persist after a restart, what permissions the extension asks for, and whether it’s doing anything with your data that it shouldn’t. AI can get you moving, but judgment is what keeps the project from turning into a mess you don’t understand.
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- What’s included?
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Unlimited conversations, faster response speed, priority access, and more
- Brand
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ChatGPT
ChatGPT’s AI-supported assistance gets even better with a paid subscription; it Plus tier offers enhanced features including unlimited conversations, faster response speed, priority access, and more.
Vibe coding doesn’t replace programming, it opens the door
Vibe coding isn’t a shortcut around understanding what you’re building. It is, however, a better way to start learning. That difference matters. AI can help you turn an idea into something you can test, question, fix, and improve, but it does not remove the need for judgment or curiosity. For me, that’s the real appeal. It makes programming feel less like a skill you either have or don’t have, and more like something you can work your way back into one small problem at a time. For anyone who has thought about coding but never got past the friction of starting, that shift is a big deal.
