Nov 27, 2025
You're 40 prompts deep into building a real-time chat feature. Your AI was crushing it at first, but now it's confidently rewriting working code, breaking the authentication flow you built yesterday, and suggesting you "just restart the WebSocket connection" for the third time. Your context window is packed with 3,000 lines of half-implemented messaging logic, and every new prompt feels like rolling dice.
Sound familiar?
This is the hidden tax of building everything from scratch with AI tools. The more code you generate, the more your AI has to track, and the faster it loses the plot. One developer on Bolt put it simply: "I find generating individual components better for bigger scale development. It'll be slower, but it's generally more stable."
The problem isn't your prompting skills. It's mathematics.
Every AI coding tool has a context window limit. That's the amount of code, conversation history, and instructions it can actively "remember" while helping you build. When you're generating everything from database schemas to WebSocket handlers to UI components, you're filling that window fast.
Once you hit the limit, the AI starts forgetting things. It can't see the authentication middleware you wrote 100 prompts ago. It doesn't remember that you already solved the real-time sync issue. So it confidently rewrites things, introduces bugs, and hallucinates solutions that sound great but break on deployment.
The community has noticed. One Replit user shared their workflow: "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."
But going one tiny step at a time isn't sustainable when you're trying to ship. There's a better way.
Here's the insight that's changing how smart developers build with AI: not all code needs to be in your context window.
Think about how you already work. You don't regenerate React every time you need a button. You don't ask your AI to rewrite your authentication library when you're building a dashboard. You import the solution and move on.
The same logic applies to entire features, especially complex, real-time ones that AI tools consistently struggle with.
A developer in the Cursor community explained their evolution: "Cursor is basically VSCode with a brain. It started as a real code editor that developers already trusted, then they baked AI into something solid. That means you get freedom, version control, debugging, and the full power of a real environment. It is harder to learn at first, but you grow into it instead of growing out of it."
But even with the best AI coding tools, there's a decision point: when do you prompt, and when do you integrate?
Here's a practical framework that's working for developers shipping real products:
Prompt when:
Integrate when:
One developer summed up the turning point: "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."
Let's look at actual numbers. Say you're building a project management tool with team collaboration features.
The everything-from-scratch approach:
The component-first approach:
That's a 90% reduction in context pollution. Your AI can actually see your whole application architecture in one view. It stops hallucinating fixes for problems that don't exist.
When you're not burning tokens on rebuilding chat systems, you can focus your AI on the things that actually differentiate your product. Your domain logic. Your unique workflows. Your user experience.
This is where Weavy becomes relevant. It's a set of pre-built, customizable components that handle exactly the kind of features AI tools struggle with: real-time chat, messaging, activity feeds, and file sharing.
Instead of prompting your AI to implement WebSocket connections for the fifth time, you drop in a component and configure it. The collaboration features work out of the box with proper real-time sync, presence tracking, and all the edge cases you weren't going to catch anyway.
Your AI stays focused on your product. Your context window stays clean. You ship faster because you're not debugging phantom WebSocket issues at 2 AM.
One indie hacker captured the philosophy: "Hard not to build something because I develop fast. I am keeping it a service and not building a product (so far)." Speed matters, but only if what you're building actually works.
Here are specific practices that developers use to keep their AI tools effective:
1. Set context boundaries early
Before starting a feature, decide what goes in your context and what doesn't. If you're integrating a collaboration component, tell your AI upfront: "We're using Weavy for chat. Don't generate messaging code. Focus on connecting user events to the chat system."
2. Use separate conversations for separate concerns
Don't build your authentication system, payment flow, and real-time features in the same chat thread. When context gets muddled, start fresh. Your AI performs better with focused conversations.
3. Iterate on integration, not implementation
Instead of asking "build me a chat system with typing indicators," ask "show me how to trigger a notification in our activity feed when a new chat message arrives." You're orchestrating, not generating.
4. Watch for the warning signs
If your AI starts:
Stop. You've hit context limits. Either start a fresh conversation or reconsider whether you should be generating this code at all.
A product manager who builds their own POCs shared: "I can prototype, test, and iterate on my own. If it works, the team gets a proven concept and a clear starting point. It cuts a ton of friction for everyone and accelerates actual product decisions."
One developer watching the vibe coding revolution noted: "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. When you can generate a landing page or a dashboard in minutes, the old "build everything" mentality doesn't make sense anymore. But neither does the "generate everything" approach.
The new calculus is: generate what's unique, integrate what's standard.
Your product's value isn't in how you implemented WebSocket reconnection logic. It's in what your users can do with the tools you've given them. A developer on Netlify put it well: "Good design is invisible. It whispers, it doesn't shout. It makes you forget you're 'using' something because it just works."
That applies to your code architecture too. The best systems are the ones where everything just works, and you don't think about the plumbing.
The developers shipping fastest right now aren't the ones with the most tokens or the cleverest prompts. They're the ones who've figured out what not to build.
They're using AI for rapid iteration on business logic. They're integrating proven solutions for commodity features. They're staying in flow because their tools stay sharp.
As one builder reflected: "Learning the harder tool once is always cheaper than fighting the easier tool forever."
The same applies to your architecture. Setting up components once is cheaper than regenerating features forever.
The component-first strategy isn't about doing less. It's about doing what matters. Your AI is incredibly powerful when it's focused on your unique product challenges. It becomes a liability when it's drowning in boilerplate collaboration features.
Keep your context clean. Let your AI do what it does best. Integrate the rest.
Your users don't care how you built your chat system. They care that it works, that it's fast, and that they can get their job done. Give them that, and you win.
The 5x speed improvement isn't hype. It's what happens when you stop fighting your tools and start using them strategically.
Try the Weavy vibe prompt today: https://www.weavy.com/get-started
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