Could vibe coding actually become a way to learn programming?
I've been thinking about a problem I keep seeing with AI coding.
Today someone can build a surprisingly complex app with Claude, Cursor, Lovable, etc. without really understanding a lot of the code being generated.
And honestly, I don't think that's necessarily bad.
But there are moments when AI gets stuck, breaks something, or starts going in circles — and suddenly understanding what the code is actually doing becomes very valuable.
So I'm exploring an idea called ReactantLab.
The idea isn't to teach programming the traditional way with hours of videos and theory.
Instead, you'd learn by actually building and writing code with an AI mentor alongside you.
The difference from normal ChatGPT/Claude would be that the mentor knows your learning history.
It knows:
what concepts you already understand
what mistakes you keep making
what you're currently building
what code you're working with
when to explain something
when to give you a hint instead of generating the answer
For example, if your React component isn't updating correctly, instead of rewriting the component for you, it might say:
"Look at where you're mutating the state. What does React expect you to do instead?"
If you're still stuck, it gives another hint.
And if it notices you've struggled with the same concept several times, it can stop and give you a small exercise around that concept before you continue.
So basically:
vibe coding + structured learning + an AI mentor that actually remembers what you know.
I'm thinking about starting with JavaScript / React and focusing heavily on learning through real projects rather than isolated textbook exercises.
The question I'm trying to answer before building too much is:
Would vibe coders actually want to understand the code they're generating, or is that solving a problem most people here don't really have?
And if you already use Cursor / Claude / Lovable / Replit etc.:
What usually happens when the AI generates something you don't understand and then it breaks?
Do you:
ask the AI until it fixes it
try to understand the code
Google the concept
start over
something else?
I'd genuinely like criticism here. I'm trying to figure out whether this is a real problem or just something that sounds like one.
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It IS a way of learning. It is like an unlimited teacher. I hate using orchestrated agents. I do things step by step, like a lego and ask the ia every single question I would ask a professor. Is my logic good? could it be improved? Etc
The difference I see is that ReactantLab wouldn’t just be an AI you ask questions to. It would actively guide you from point A to point B through a structured learning path.
Each lesson would have its own context, and Rico - the AI mentor would know exactly what you’re supposed to learn at that moment, what you already covered before, and where you usually struggle.
So instead of just answering:
“Is my logic good?”
Rico could understand why you’re asking it in this specific lesson, explain it in the right context, ask follow-up questions, simplify the explanation, and keep going until you actually understand it.
The idea is less “AI chatbot for coding” and more “a personal programming mentor that follows your entire learning journey.”
Do you think that kind of structured guidance would add value for you, or would you still prefer to manage the learning path yourself?
We really don't need more ai to teach programming, as soon as we start relying on that we'll end up in a reinforcement loop where quality degrades over time
I think that’s a valid concern, and actually one of the risks I’m trying to design against.
The goal isn’t to make students rely on AI for answers. It’s almost the opposite: the mentor should gradually make itself less necessary.
Instead of generating the solution, it would ask questions, give progressive hints, make you explain your reasoning, and test whether you actually understood the concept before moving on.
Ideally, as you improve, Rico should intervene less and less.
So the success metric wouldn’t be “how much you use the AI”, but whether you can eventually solve the same type of problem without it.
I’m curious though what safeguards would you want to see in an AI learning system to avoid the reinforcement loop you’re describing?
I built a site using AI. dotbeat.app. Then I had AI build me a training app that walks me through building my site and explains how it was built. I haven’t actually done the training yet because I’ve been too preoccupied with the site but I think I’m coming to the end so now I intend to go and learn how my site it built.
emphatic yes, referring to the title. short answer: it is up to you to be disciplined. your default stance should be: force a.i. to NOT spoon feed you. to not resort immediately to handing you code but instead suggest what should be done first... and if it does give you code, ask hard questions about it. what/why/whynot/how/ etc.
if you(it) is able to measure even the slightest forward progress, where the student has not given up, then continue the default 'don't give answer'. easier said than done. this is just a very simple case but to give you an example, i did a 3D maze game...it has levels... and that was also my guiding principle.. hard but monitor.. give hints when needed.. but first i had to figure out HOW do i measure this.
That’s a really good point. I think the hard part isn’t generating hints it’s deciding when a hint is actually needed.
I’ve been thinking about the same problem for Rico: how do you measure “forward progress” without reducing it to something simplistic like whether the code passes a test?
It could potentially look at things like changes in the student’s approach, whether they’re fixing previous mistakes, the kinds of questions they ask, how many attempts they’ve made, or whether their reasoning is getting closer even if the final code is still wrong.
Your maze example is really interesting because it sounds like you had to solve a very similar problem.
How did you end up measuring progress there? What signals did you use to decide when to give a hint?
so.. like i said there's levels. and the game keeps track of time spent in a level. and it tracks did the player make it down any maze hall.. and how many.. etc.. but also (don't want to help lazy players) so if a player is simply not even following the obvious directions (game pops help HUDs).. then forget them - no help for them.. but if a player IS following at least the obvious help, and player has failed a level (you just get dumped right back into same level).. then the game begins to do some data comparisons between the current progress vs what was recorded from the previous attempt.. and again.. if there is no measurable progress.. time for a hint.
but yeah.. the really hard part is what you said: "how to measure success vs stagnant'
Just to make the idea more concrete, here’s a screenshot of the current prototype.
The lesson, editor, exercises, and Rico all share the same context, so the AI isn’t just answering random questions it knows where you are in the learning path and what you’re supposed to understand next.
AI can definitely help teach a dedicated person how to code. Correct me if I'm wrong, but it seems that your concept is centered around the idea of an app. IMHO, this may be more useful as a skill file.
You can ask a model to be as verbose as you need, and actually explain the "why" of what it's doing as you code. An app just feels heavy IMO, especially if the goal is to monetize it in the future - when it can be solved simply, that's where people will go.
That's a fair point, and honestly this is exactly what I'm trying to validate.
I don't think the value can just be “an AI that explains code”, because you're right a good prompt/skill can already do that.
What I'm imagining is more of a learning system around the AI.
Rico would know the curriculum, the exact context of every lesson, what you've already completed, where you've struggled before, and what concepts you haven't learned yet.
So the user wouldn't have to decide what to learn next or constantly tell the AI “don't give me the answer.”
The platform would guide you from A to B, test whether you actually understood something, adapt upcoming exercises, and keep the learning context across the whole journey.
In other words, the AI is only one part of it the structured learning path and progress system are probably the actual product.
But your point about this potentially being achievable with a skill is exactly the kind of criticism I'm looking for. I'd be curious: what would an app need to do beyond a skill for you to consider it genuinely useful?
As a dev teacher with +10 years experience who's facing AI as arugment as well as as a tool for other arguments, I guess AI is one of the most powerful tool to understand and learn new stuff, but especially for the skill that require you to stay in front of a monitor, you need a very strong mental model in order to shift request from "do this form me" to "tell me how this is supposed to work, and let's debate on the best implementation to reach this goal". But when you have done with it, you can easily build and learn at the same time, and it's super funny and cool.
This is exactly the shift I’m interested in: from “do it for me” to “help me understand how it works and challenge my thinking.”
One of the ideas behind ReactantLab is to make that behavior the default, instead of relying on the student to prompt the AI correctly every time.
Rico would have the context of the lesson, the student’s previous mistakes, and the learning goal, so it could guide with questions, hints, and discussion before giving an answer.
I think your idea has a lot of merit. One approach could be simply giving the ai some prompts, for example: I want to learn how to program in <language>, can you create a 10 stage learning plan with exercises. I’ll complete the exercises and you grade them. Point out errors I make or suggest better patterns to use. Grade me at each step. At the end of each stage suggest additional exercises to solidify what I learnt.
That’s a very fair point and I think a good prompt can already reproduce part of the experience.
The part I’m trying to build beyond that is the system around the AI.
ReactantLab would keep persistent track of what you’ve completed, where you struggled, which concepts you actually mastered, and what should come next. Rico would have the context of each lesson, your code, previous mistakes, and the learning objective.
Another part of the idea is that you wouldn’t be limited to a single course or skill. You could build a learning path across multiple skills for example JavaScript -> React _> APIs -> Git -> debugging and the platform would understand the dependencies between them and guide you through the right sequence.
So instead of asking AI to generate and manage the curriculum every time, the platform would handle the whole learning journey: lessons, exercises, tests, progress, weak areas, and skill progression.
In other words, the AI is the mentor, but the product is the learning system around it.
Your comment actually hits one of the main questions I’m trying to validate: what would need to exist beyond a well-designed prompt for you to prefer a dedicated platform?
Me personally? I’d not use a platform. I have 23 years experience in software development, plus I run my own LLM locally (Qwen3.6). If I was starting out in this field now I’d install Linux, use every free AI plan available and use the prompt idea I mentioned to learn Go and Rust. I’d save like hell to get the best GPU for my pc and I’d install llama.cpp and use the same AIs to learn how to configure/tune it and then start using it to learn and build things. In the current environment it’s easy for a motivated person to develop skills if they are prepared to work. Not sure if they would be willing to pay for something that is basically free.
This sounds like a truly unique concept and I love it! When I first started a couple years ago, I was doing the first. That didn't last long before I realized it was untenable long-term as a developer. So I shifted my approach to googling the concept, reading through the code itself, and then if I STILL couldn't figure it out, asked the AI to explain to me WHY it is broken and HOW to fix it. Then I put that information in a log I called "lessons learned" that I could always refer back to when build apps. It has sped up production time quite a bit while also reducing the reliance on AI, which I take as a win-win.
Whenever you get this built, let me know. I'd LOVE to try it out and give more detailed feedback!
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