Nov 27, 2025
"I spent 100 minutes building, and 60% of that was pointing out bugs."
That quote comes from a developer's experience with AI-powered coding tools. And if you've been using AI agents to build features, especially complex ones like real-time collaboration, messaging, or file sharing, you know exactly what this feels like.
You describe the feature. The AI builds it. It breaks. You point out what's wrong. The AI rebuilds it. Something else breaks. You're now on iteration seven, you've burned through $100 in API credits, and the feature still doesn't work right.
Welcome to the infinite fix loop.
Here's what's happening under the hood. Every time you ask an AI agent to fix something, it's not just reading your prompt. It's ingesting your entire codebase context, parsing dependencies, generating new code, and outputting a response. That's thousands of tokens per interaction.
When you're building something simple like a landing page or a CRUD form, this works fine. The AI gets it right in one or two shots, and you move on.
But when you're building complex features, especially ones involving WebSockets, real-time synchronization, authentication flows, or state management across multiple components, the AI starts to struggle. Why?
Because these features require architectural decisions that AI models weren't trained to make consistently. Should the WebSocket connection live in a context provider or a custom hook? How do you handle reconnection logic? What happens when multiple users edit the same document simultaneously? How do you manage optimistic updates?
The AI makes a guess. It's often wrong. You point out the issue. It makes another guess. The cycle continues.
One developer put it bluntly: "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."
AI coding assistants are incredible at pattern matching. They've seen millions of code examples and can replicate common patterns with impressive accuracy. But collaboration features like chat, messaging, activity feeds, and file sharing don't follow simple patterns.
They require:
These aren't just coding challenges. They're architectural challenges. And AI models, for all their capabilities, don't reason about architecture the way experienced developers do.
So what happens? The AI generates code that looks right but has subtle bugs. A WebSocket that doesn't reconnect properly. A state update that causes an infinite loop. A permission check that fails silently.
You catch the bug, describe it, and the AI tries again. Except now it's working with a codebase that already has problems, so it might fix one issue while introducing two others.
As one developer noted: "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."
Let's talk numbers. If you're using Claude, GPT-4, or similar models through an AI coding platform, you're paying for tokens. A typical back-and-forth to build and debug a chat feature might look like:
That's 128,000 tokens just to get one feature working. At current API rates, that's easily $50 to $80. And that's assuming you actually get it working after five iterations.
But the real cost isn't just money. It's time and momentum.
As one builder shared: "It's hard, because it's fun and exciting to build. However, it's far more saddening when you realise you wasted 3 months."
When you're stuck in fix loops, you're not moving forward. You're not shipping features. You're not getting user feedback. You're just burning time and tokens trying to get basic functionality to work.
Here's what experienced developers figured out early: some features shouldn't be rebuilt every time.
One developer explained their approach: "I find generating individual components better for bigger scale development. It'll be slower, but it's generally more stable."
This is the right instinct, but there's an even better solution: don't generate these components at all. Use pre-built ones.
Think about it. When you need authentication, you don't ask an AI to build OAuth from scratch. You use Auth0, Clerk, or Supabase Auth. When you need payments, you don't rebuild Stripe. You integrate it.
The same logic applies to collaboration features. Chat, messaging, feeds, file sharing, these are solved problems. They've been built thousands of times. The patterns are well-established. The edge cases are known.
This is where Weavy comes in.
Instead of asking an AI to generate a chat system from scratch (and then debugging it for three days), you drop in a pre-built component that already handles:
The AI's job becomes integration, not generation. And integration is something AI tools are actually good at.
Instead of: "Build me a real-time chat system with file sharing and user presence"
You prompt: "Integrate the Weavy chat component into this React app and style it to match our design system"
The difference in token spend? About 90% less. The difference in iterations? Usually one or two instead of ten.
There's another benefit to pre-built components that developers often overlook: finishing.
AI tools are great at getting you to 80%. They can scaffold an app, build out basic features, and create functional prototypes incredibly fast. But that last 20%, the polish, the edge cases, the small interactions that make software feel professional, that's where things slow down dramatically.
As one developer put it: "Good design is invisible. It whispers, it doesn't shout. It makes you forget you're using something because it just works."
When you're using AI to generate collaboration features, you're responsible for that last 20%. You have to prompt for error states, loading states, empty states, mobile behavior, keyboard shortcuts, accessibility, and dozens of other details.
With pre-built components, that work is already done. The last mile is already finished.
One experienced product manager explained their workflow: "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. I can prototype, test, and iterate on my own."
This is the unlock. When the complex parts are handled by production-ready components, AI-assisted development becomes about composition and customization, not generation and debugging.
Here's a practical workflow that eliminates most fix loops:
1. Identify your complex features early
Before you start prompting, list out the features that involve:
These are your candidates for pre-built components.
2. Integrate components first, then customize
Instead of generating these features, integrate battle-tested components. With Weavy, this means dropping in a few lines of code to add chat, feeds, or file sharing.
Let the AI help with integration, styling, and connecting these components to your existing data and auth systems.
3. Use AI for the unique parts
Now your AI agent can focus on what makes your app special: your business logic, your unique workflows, your specific user experience.
This is where AI actually shines. Not rebuilding WebSocket infrastructure for the hundredth time, but implementing your specific requirements.
4. Iterate on composition, not generation
When you need changes, you're tweaking how components work together, not regenerating entire systems. This cuts token usage and eliminates most fix loops.
As one developer noted about their process: "One step at a time is better, for multiple reasons. You are in control and know what's being added created. Easier to test features. Ideas improvements, kind of agile style building that you can amend as you go."
There's a broader point here about how AI has changed the economics of building software.
One developer captured this shift perfectly: "We are seeing the same thing, namely the power of Replit. In my multiple decades of building software systems, I don't think I've ever seen something so powerful to change the entire dynamic of building software. The ability to speak things into existence is the superpower here. And the calculus of build vs buy is needing to be reconsidered."
They're right. AI has made building easier than ever. But that doesn't mean you should build everything.
In fact, AI has made the opposite more true: you should only build what's unique to your product. Everything else should be composed from reliable, production-ready components.
Think about it from a token efficiency perspective. Why spend 100,000 tokens building a chat system when you could spend 5,000 tokens integrating one that's already production-tested?
Why spend three days debugging WebSocket reconnection logic when you could spend three hours implementing your actual product features?
The calculus isn't just build vs buy anymore. It's generate vs integrate. And for complex features, integration wins every time.
Not everyone wants to hear this. There's something satisfying about watching an AI generate an entire feature from a prompt. It feels like magic.
But as one developer pointed out: "That's absolutely true! Everyone got excited about vibe coding tools like Lovable and Bolt. I did too. But they're fun for demos. Want to ship complex products? Good luck."
The developers who are actually shipping products have learned to be pragmatic. They use AI where it excels, and they use pre-built solutions where those excel.
Another developer put it more bluntly: "Learning the harder tool once is always cheaper than fighting the easier tool forever."
The same applies to components. Integrating a pre-built collaboration system once is always cheaper than rebuilding it (poorly) every time.
If you're starting a new project or adding features to an existing one, here's the practical takeaway:
Stop asking your AI agent to generate complex collaboration features from scratch. You're wasting tokens, time, and sanity.
Instead, use pre-built components for the hard stuff (real-time features, file handling, complex state management), and let AI focus on integration, customization, and your unique business logic.
This isn't about avoiding AI. It's about using it strategically.
The developers who understand this are shipping faster, spending less on API credits, and avoiding the infinite fix loop entirely.
As one builder summarized: "It's an incredible productivity multiplier as it takes friction out of design, planning, and development work."
But only if you're not spending all that productivity fighting with bugs in AI-generated WebSocket code.
Want to see what AI-assisted development looks like when you're not stuck in fix loops?
Try the Weavy vibe prompt: https://www.weavy.com/get-started
You'll see how much faster development moves when the complex parts are already solved, and your AI agent can focus on what actually matters: building your product.
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