Dec 5, 2025
"I'm in Vibe Coding Hell," reads a recent post from a frustrated developer. The pattern is always the same: the project started smooth, features flew together, everything worked. Then came the first real bug. Then another. Then a cascade of fixes that each broke something else. Now the codebase is a tangled mess, and every AI-generated fix makes it worse.
If you've been there, you know the feeling. You describe the bug, Claude or Cursor confidently rewrites a function, and suddenly three other features stop working. You ask it to fix those, and it introduces new problems. The cycle accelerates. More prompts, more patches, more complexity. Eventually you're sitting on a mountain of code you don't understand, held together by AI-generated duct tape.
AI coding tools excel at generating code from scratch. Give them a clean slate and clear requirements, and they'll produce working implementations quickly. But they struggle with something humans take for granted: understanding the messy, implicit architecture of an existing codebase.
When you ask AI to fix a bug in poorly structured code, it doesn't see the tangled dependencies you've created over dozens of prompting sessions. It sees the immediate problem and proposes a local fix. That fix might work in isolation, but it often violates assumptions elsewhere in the code. Those violations create new bugs, which require new fixes, which create new violations.
As one experienced developer put it: "The problem with vibe coding isn't that the output is bad, it's just that if you need more than your initial idea, it gets ugly quite fast from a management standpoint. It's great for patches, but maintenance on a big project... I've yet to see it successfully refactor any real codebase."
In AI-assisted development, architectural debt compounds faster than traditional coding because developers often lack the deep understanding needed to guide refactoring decisions. When you can't read the code well enough to spot the structural problems, you can't give AI the context it needs to fix them properly.
Here's what's actually happening under the hood. Your vibe-coded project started as a series of independent features, each generated in isolation. The AI created reasonable implementations for each prompt, but it had no memory of the architectural decisions from previous sessions. Maybe it put authentication logic in three different places. Maybe it created two overlapping state management patterns. Maybe it hardcoded assumptions that later features would violate.
These aren't syntax errors. The code runs. It just has no coherent structure.
When you ask AI to add features or fix bugs in this context, it's working blind. It can see the code you paste into the prompt, but it can't see the implicit contracts, the hidden dependencies, or the fragile assumptions holding everything together. So it makes changes that seem locally correct but globally destructive.
One developer described the spiral perfectly: "You have a bug and you tell Claude about the bug and two minutes later the bug is not there. Magic? Maybe." But then comes the reality check when that fix breaks something else, and the next fix breaks two more things.
The hardest skill in vibe coding isn't writing prompts. It's recognizing when you've crossed the threshold from fixable to fundamentally broken. Here are the warning signs:
When you hit this point, more prompting won't save you. The codebase has accumulated too much structural debt. The AI can't refactor its way out because refactoring requires understanding the global architecture, and you can't provide that understanding because you don't have it either.
As one frustrated community member noted: "You cannot guide it if you don't actually understand what's going on. How could you? Vibe coding is just you not knowing what is actually going on."
The solution isn't to give up on AI-assisted development. It's to recognize that some messes require human architectural thinking that can't be prompted into existence.
The developers who succeed with vibe coding long-term do something crucial: they architect before they prompt. They make deliberate decisions about structure, patterns, and boundaries before asking AI to generate code.
This doesn't mean writing detailed specs. It means answering questions like: Where does state live? How do features communicate? What's the data flow? What are the core abstractions? When you have clear answers, you can guide AI to generate code that fits together coherently.
One experienced dev shared: "The problem with vibe coders is they don't know what or how to ask, which probably messes things up. When you have many complex components working together, along with server components, if you leave out important details it will surely fxxk up. It never fxxks up for me cause I know how my app works inside out."
That inside-out understanding is the difference between code that scales and code that collapses.
One category of features consistently causes vibe coding projects to spiral: real-time collaboration. Chat, notifications, live updates, file sharing, presence indicators. These features require carefully coordinated state management, WebSocket handling, conflict resolution, and edge case handling that AI struggles to generate coherently.
When developers try to vibe code these features from scratch, they often create the exact kind of tangled architecture that leads to the unfixable mess. The AI generates code that works in isolation but breaks when multiple users interact, or when the connection drops, or when state needs to sync across components.
This is where pre-built collaboration components like Weavy become essential. Instead of asking AI to generate real-time chat, feeds, or file management from scratch, you drop in tested components that handle the complexity for you. The AI can still generate your core application logic, but it's integrating with stable APIs instead of trying to invent real-time architecture on the fly.
Weavy provides drop-in chat, files, feeds, and AI agent components that handle the stateful, multi-user complexity that causes vibe-coded projects to collapse. You avoid the architectural debt entirely by using components that already solve the hard problems.
Vibe coding works brilliantly for generating initial features and exploring ideas quickly. But it fails catastrophically when you try to use more AI prompting to fix the structural problems that AI prompting created.
The developers who succeed long-term do three things: they architect before prompting, they recognize when code has crossed into unfixable territory, and they use stable components for complex features instead of generating everything from scratch.
When you find yourself in vibe coding hell, stuck in the spiral of fixes that break fixes, the answer isn't better prompts. It's stepping back, understanding the structure you actually need, and either refactoring with that clarity or starting fresh with better architectural boundaries.
Weavy is the collaboration layer that lets AI build the easy 80 percent while you avoid the expensive 40 percent.
Try the Weavy vibe prompt: https://www.weavy.com/get-started
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