Nov 27, 2025
You just burned through $200 in API credits trying to get WebSockets working. Again.
The initial idea? Done in 12 minutes. The authentication flow? Smooth. The dashboard UI? Chef's kiss. But now you're on attempt number seven trying to get real-time chat to stop breaking every time someone refreshes the page, and your AI assistant keeps generating subtly different implementations that all fail in new and creative ways.
Sound familiar?
If you've spent any time vibe coding, you've hit this wall. There's an invisible line in every project where AI goes from feeling like magic to feeling like a money pit. The community has been talking about it for months, and the pattern is crystal clear: AI crushes about 60-80% of your build, then that last 20-40% becomes a special kind of hell.
Let's be honest about what works. AI-assisted development is legitimately incredible at certain things.
UI components and layouts? Absolutely demolished. One developer put it plainly: "I find generating individual components better for bigger scale development. It'll be slower, but it's generally more stable." And they're right. Need a modal? A form? A card grid? You'll have three variations to choose from before you finish your coffee.
Basic CRUD operations? Done. Standard authentication patterns? No problem. Static page generation? Please, that's child's play.
The sweet spot is anything stateless, anything that's been done a million times before, anything where the AI can lean on mountains of training data from GitHub repos and Stack Overflow threads. As one builder noted: "Very much depends on what you're wanting to do/create. I had success with mine but a lot of people tend to have issues with more complex builds."
This is where you get that magical feeling of speaking things into existence. This is the 80% that makes you feel like a wizard.
Then you need real-time features.
Or file uploads with progress tracking. Or collaborative editing. Or proper state management across WebSocket connections. Or any feature where multiple users need to interact with shared data without everything exploding.
Suddenly your AI assistant is hallucinating solutions. It confidently generates WebSocket code that looks right but randomly disconnects clients. It creates file upload handlers that work locally but fail in production. It builds real-time features that technically function but leak memory like a sieve.
One frustrated developer shared their experience: "I spent 100 minutes building, and 60% of that was pointing out bugs." Another was more direct about platform limitations: "I had hundreds in overages just to get them back to where they are now and it's still a bit of a gong-show."
The problem isn't that AI can't generate this code. It's that these features require architectural decisions, performance considerations, and edge case handling that AI struggles to reason about consistently. Every iteration introduces new bugs while fixing old ones. You're not building forward anymore, you're debugging in circles.
As one seasoned builder put it: "If you don't 1-shot your build, you're stuck with a messy, brittle codebase. Maintenance is a nightmare: adding one feature breaks three others."
Here's the thing: AI models are phenomenal at patterns they've seen thousands of times. React component? Seen it. Login form? Boring. Responsive navbar? Please.
But real-time collaborative features? File handling with chunking and resume capability? Proper WebSocket reconnection logic with exponential backoff? These are more complex, more varied, and way less represented in training data. There's no single "right way" that the model can confidently reproduce.
Plus, these features are inherently stateful. They require thinking about race conditions, network failures, concurrent updates, and a dozen other things that don't show up in the happy-path code that dominates training datasets.
One developer noted the difference clearly: "My experience with it, one step at time is better, for multiple reasons. You are in control and know what being added created. Easier to test features as replit sometimes say it's working while it's not."
The AI isn't lying to you. It just doesn't know what it doesn't know.
Let's talk numbers. When developers share their experiences, a pattern emerges:
That last chunk is where you're regenerating code, trying different approaches, debugging AI-generated solutions, and eventually just writing it yourself or finding a different way entirely.
One builder captured the frustration: "I spend a lot of time on perfecting the tiniest / unwanted things. I wrapped up the initial build pretty fast, but then I went down the rabbit hole."
Another was more blunt about platform costs: "I tried to build a feature with it, but it told me it didn't have the autonomy to do what I asked, so I had to switch back to regular build mode. It has tricked me into spending more money."
This is where smart builders are rethinking their approach. If AI struggles with the same 20-40% every time, and that's where all your money goes, maybe those are exactly the features you shouldn't be building from scratch.
Think about it: AI can generate a beautiful landing page in minutes. Should you build one? No, you'd use a template or a tool. The same logic applies to complex features that have already been solved.
This is where Weavy enters the picture. Instead of burning credits on your seventh attempt at building real-time chat or spending three days debugging file upload state management, you drop in pre-built components that actually work.
We're talking about the exact features that bleed budgets:
The ROI is straightforward: if AI would take 10+ attempts and $200 in credits to build a feature that still might not work right, and you can drop in a working version in an hour, that's not even a question.
As one developer observed about the changing calculation: "The ability to 'speak things into existence' is the superpower here. And the calculus of build vs buy is needing to be reconsidered."
So what belongs in the 80% that AI handles brilliantly? Stick to:
Presentation layer stuff: Pages, layouts, components, styling, responsive design. Let AI crank these out. If you're using something like Shadcn components, even better. As one designer noted: "I always use Shadcn components, they're already beautifully designed, so you'll have a clean, professional-looking site from day one."
Standard business logic: Form validation, data transformation, API calls to established services, basic state management. This is well-trodden ground.
Glue code: Connecting services, formatting data, handling standard error cases. AI excels at boilerplate.
Prototyping anything: If you're just validating an idea, let AI build the whole thing. Speed matters more than perfection. As one builder wisely said: "Build it anyways. Worth the experience of problem solving. Gets the juices flowing into other ideas down the line."
Avoid having AI generate from scratch:
These are the features that one developer described perfectly: "I think with the varied responses it is likely dependent on what people are building. I have found it not that great with hard problems. Might be ok at building a website though."
The developers who ship fastest aren't using AI for everything or nothing. They're strategic.
Use AI for the 80% it dominates. Use pre-built solutions for the 20-40% that bleeds budgets. Only custom-build the 5-10% that's truly unique to your product.
One experienced PM explained their flow: "I'm a product manager, but I build my own POCs, tweak designs, and even spin up lightweight features across our platform. I'm not replacing devs, final delivery still goes through them, but I'm no longer blocked by the 'wait for engineering' cycle just to validate an idea."
Another developer shared the wisdom of incremental building: "One step at a time is better. You are in control and know what being added. Easier to test features. Ideas improvements, kind of agile style building that you can amend what being created as you go."
This is the path forward. Fast iteration on the parts where AI shines, and smart component choices for everything else.
Before you burn through another $200 in credits, ask yourself what one community member asked bluntly: "Is what you're building worth spending money on?"
Because here's the truth that nobody wants to hear: "More than building, what demotivates all devs is that it doesn't take off and then dies a slow death. Marketing and keeping a steady stream of growth is critical."
If you're spending 70% of your budget debugging AI-generated WebSocket code instead of talking to users, you've already lost. The goal isn't to build everything yourself. The goal is to ship something people want.
As another founder reflected: "I get you! But yeah, defo see if there's a need before building it. Its hard, because its fun and exciting to build. However, its far more saddening when you realise you wasted 3 months."
AI-assisted development is genuinely transformative for 60-80% of most applications. But that last 20-40% will eat your budget, your timeline, and your sanity if you're not careful.
Know what AI builds fast. Know what bleeds money. And for everything in the bleeding-money category, seriously consider whether you should be building it at all.
The developers winning right now aren't the ones building everything from scratch. They're the ones shipping fast by being smart about what deserves custom code and what deserves a better solution.
Your AI assistant is incredible at making you feel productive. Make sure that productivity is actually moving you forward.
Ready to skip the expensive 40% and ship faster? Try the Weavy vibe prompt: https://www.weavy.com/get-started
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