When Perfect Code Fails: My Journey with AI IDEs

Last month, I asked Claude to help refactor a React component. The code it generated was undeniably impressive—clean, well-documented, and aligned with best practices. It felt like the perfect solution at first glance.

Then, I tested it.

It quietly broke our error tracking system, removed a crucial race condition check (which, admittedly, looked like a bug), and duplicated three utility functions with slightly different implementations.

Sound familiar?

The AI coding space is evolving at an incredible pace. Companies like Cursor have reached $50M ARR, Lovable.ai hit $4M in revenue within just four weeks, and new "AI-powered IDEs" pop up daily on Product Hunt. Clearly, there’s strong developer demand for AI-assisted coding.

Yet, despite these impressive figures, I believe that current AI coding tools are solving the wrong problem. And that’s why I’m building another one.

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The Real Problem: It’s Not About Code Generation

I’ve spent countless hours with AI coding tools. The code generation capabilities are jaw-dropping, and the autocomplete suggestions often feel magical. But after using these tools extensively on real projects, a deeper issue becomes apparent.

A Reddit comment summed it up perfectly:

“Have AI agent fix what I ask for and on the way break 2-25 other things is the usual timesink where 95% of my time spent with agents goes to.”

This struck a chord with me. The issue isn’t that AI writes bad code—the code is usually quite good. The problem is that AI lacks an understanding of the hidden complexity and implicit patterns within a project’s codebase.

Here’s a real example: I recently asked an AI assistant to add error handling to a React component. The result?

• It added try-catch blocks that swallowed errors we specifically wanted to bubble up.

• It created new utility functions that slightly differed from existing ones, introducing redundancy.

• It removed what appeared to be duplicate checks, but in reality, these were essential safeguards for handling obscure edge cases we had identified months ago.

This is the paradox—the AI wrote technically correct code, but in doing so, it subtly broke our established patterns.

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The Missing Piece: Project Context and Memory

When a senior developer joins a team, they don’t immediately start rewriting components. Instead, they take time to absorb the project’s unique nuances:

• Why a seemingly odd-looking useEffect hook is actually handling a critical race condition.

• Why we use a specific error-handling pattern instead of the more common approach.

• Which parts of the “messy” code are actually deliberate workarounds for edge cases discovered in production.

Yet, AI coding tools skip this crucial learning phase. They generate code in a vacuum—without truly understanding how a project has evolved over time.

To quantify this, I surveyed 33 developers about their AI-assisted coding experience:

• 76% use AI primarily for code generation.

• 85% said they spend significant time fixing AI-generated inconsistencies—not because the code was incorrect, but because it failed to align with their project’s reality.

This gap in understanding is the fundamental flaw in current AI-assisted coding tools.

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A New Approach: AI That Learns Your Codebase

This is why I’m building a new AI-powered IDE extension—one that prioritizes understanding over generation. Instead of treating each request in isolation, it maintains a memory of your project through:

• Git history & commit messages – Understanding past changes, bug fixes, and development rationale.

• Codebase patterns & structure – Recognizing how similar problems have been solved in the past.

• Existing utility functions & best practices – Avoiding redundant or conflicting implementations.

By doing this, the AI moves beyond just generating code—it starts understanding the codebase the way a human developer would.

The technical challenges of building this are substantial, but the value is undeniable. Because, ultimately, line-by-line code generation is powerful, but without project context, it’s like having a genius programmer with severe short-term memory loss.

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Building in Public: Your Input Matters

I’m developing this project openly and would love to hear your thoughts.

• How do you currently maintain project knowledge while using AI tools?

• What aspects of project context do you find most critical for AI-assisted development?

• Have you encountered similar frustrations with existing AI-powered IDEs?

I’m actively gathering insights from developers to make this solution genuinely useful. If you’d like to be part of the conversation, drop a comment or reach out.

AI-assisted coding should feel like working with an experienced teammate—not like debugging a random stranger’s PR.

Let’s fix that together.

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