Why Vibe Coders Can’t Build Useful Apps (+ how to save time & money / 2026)

These days, everyone is telling you that vibe coding means you don't need to learn programming anymore. And honestly, I think that's terrible advice. Not because modern AI tools don't work, but because there's a real skill gap that exists in order to ship something that's useful, secure, and maintainable. In this video, I will explain the five core skills that separate vibe coders who actually know what they are doing from the ones who are just gambling. Let's

quickly go over the foundations. This is probably what most consider is the boring part, but it's the stuff that makes everything click when you build your apps. And no, you don't need a computer science degree to understand these concepts. What I'm talking about is basic data structures, arrays, objects, and which ones to use when manipulating data. If the AI spits out a nested object, and you don't know what that is, you're kind of cooked. The same goes for control flow. It's how your code makes decisions, and you need to be able to follow the logic and loops. Anytime you're doing something repeatedly, there's probably a loop involved. So, you should know how they work. And then there's error handling. What to do when things break because things will break. If you can't digest and understand an error message, you won't be able to point the AI in the right direction for a fix. But I'll speak more on debugging later in this video. If you're looking for a place to learn all of this, Scriber has a great course on data structures and algorithms that I recommend checking out. It's only two and a half hours long, so just dedicate an afternoon to learning it and you'll be better off than 80% of the vibe coders out there that don't know any better. And if you don't know what Scrimber is, it's a platform that levels up your coding skills with interactive courses on everything from full stack development to AI engineering. Scribba was what I used to upskill in the early stages of my software engineering career. Unlike other platforms in Scriber, the video lesson is also the code editor, and you'll be prompted again and again by the teacher to pause and solve challenges right inside the browser. Check the description for links to the courses that will actually help you as a beginner vibe coder. You'll also get 20% off if you decide to subscribe to a pro membership. Now

let's talk about Git and version control. Imagine this. You're 3 hours deep into refactoring your authentication and payments logic. Everything was working before, now nothing works, and you have no idea what the breaking change was. And if you're ballsy enough to not use version control to manage your changes, you're basically screwed. So, here's my simple workflow. Branch off from main for any big features. Keep commits small and focused so that when things break, you can surgically revert whatever was responsible. Build it, test it, then merge it only when everything works. One thing that's been saving me a lot of time and brain power recently is getting the AI to group and write my commit messages for me. Here's the prompt. If you want to try it for yourself, if Git still feels scary or confusing to you, I highly recommend checking out this intro course to get you up to speed. If you're

using AI to help you build apps, then you should know how to get the most done with the fewest amount of tokens. Good prompting isn't about being fancy. It's about being clear. I typically structure my prompts in three simple parts: context, task, and constraints. Context is the background information. What feature am I trying to build? Which libraries do I want it to use? Why am I even building this feature? task is just what I want the agent to do. Here I will reference specific files and documentation to help with the task. If I am still unsure about the implementation, I will ask the AI to ask me clarifying questions so we can brainstorm a solution together. And constraints are simple guardrails on what it should not do. Here are a few phrases for constraints that I use regularly, which stops the AI from overengineering. And something I've noticed a lot these days is that people who know basic programming terminology and workflows tend to get way better results from the AI. Not because they're smarter, but because they can describe what they want precisely. For example, an experienced programmer will break down their problem into smaller pieces and then use the AI as assistance to help them with each step. Like implement this new endpoint, then create the client components and finally perform some migrations to update the database. Whereas a beginner will just ask the AI to build the entire feature in one go. And along the way, they won't understand what the AI is doing or how to fix it when it breaks because there was so much code generated all at once. The AI understands specific instructions and technical keywords way better than vague descriptions. So, make the effort to learn the proper vocabulary of your programming language and domain. If you're not sure where to start, I've linked a 1-hour prompt engineering course in the description that covers all of the basics. This might sound

familiar. The AI spits out 200 lines of code. You hit accept all and it doesn't work. Now what? You're staring at code you didn't write, errors you don't understand, and you have no idea where to even start. The thing about debugging is that most of the time it's just the same 10 errors showing up in different disguises. The good news is once you've seen them enough times, you'll know exactly how to fix them. So you're probably wondering, how do I get better at debugging then? And the hard truth is there's really no shortcut. It all comes down to reps, practice, and pattern recognition. So, just get building and circling back to the whole point of this video. Having a solid foundation of programming fundamentals can only help you. But aside from that, you should be familiar with the basic dev tools of whatever you're working on so that you can debug effectively. For web apps, it could be things such as analyzing network requests, inspecting DOM elements, and tracing through the console errors. But if you're looking for an AI related tool to help with debugging, I recommend installing some skills into your agents for a second pair of eyes. Now, let's talk app

security. In my experience, the AI won't care about security unless you explicitly point it out. It will happily generate code with SQL injection vulnerabilities, expose your private API keys, and not validate any user inputs. And if you don't know what any of that means, that's exactly the problem because cyber attackers don't even need to be sophisticated these days. They just need you to be careless. So, what can you actually do about this? Well, it sounds obvious, but you should learn the basics of cyber security. authentication and authorization. Know the difference between the two. Understand how API keys work, how OR tokens expire, and how to properly secure your endpoints. And before you ship anything to production that handles user data, run through a basic security checklist. Are your database rules configured correctly? Are you exposing any sensitive endpoints publicly? Is user input being validated and sanitized? You could also try experimenting with some of these agent skills to help spot exploits earlier. And Scrimber also has a solid cyber security section in their back-end developer path that covers all the fundamentals in a few hours. The whole point of this video is to say that Vibe coding can be powerful, but only if you understand the fundamentals. The people who are actually shipping useful and long-lasting products aren't just prompting and praying. They are usually the cracked engineers who already know what they are doing and are using AI to amplify their productivity. So, take the time to learn the skills mentioned in this video. It'll make you a better builder and honestly, you'll have way more fun when you actually understand what's happening when using AI.