This is just what I’ve noticed from recent years of coding with agents and LLMs. I’m not claiming to be some guru expert, I’m just sharing some thoughts. Where the LLMs excel: One-shot development — Give them a prompt and let them architect a v0 to v1 codebase. They’re surprisingly solid here. Contained, small-scale coding — Self-contained scripts, prototypes, quick tools. And impressively, this works across essentially any language, framework, database, or devops stack. Platform/framework bugs — The kind of underlying bug that would appear across thousands of codebases. LLMs have seen these patterns countless times in training and can resolve them quickly. Where the LLMs start to struggle: As the codebase scales, you can reach a point where the architecture is fundamentally sound, the dependencies aren’t the problem, and yet things are breaking in subtle, systemic ways. These are codebase-centric issues — problems that are unique to your system and your accumulated decisions. Given these issues, the LLMs… Spin their wheels endlessly Burn through tokens chasing red herrings Make confident but incorrect guesses Require round after round of debugging that goes nowhere What’s actually needed here is the kind of thing an experienced developer does almost instinctively — lean back, think across the whole system, draw on pattern recognition built over years, and zero in on the real root cause. It’s less about knowing syntax and more about understanding systems . The vibe coding blind spot: This is also exactly where someone who has become overly reliant on LLMs is going to hit a wall. If you’ve been letting the AI do the heavy lifting from day one, you wouldn’t have built the holistic system understanding needed to debug these deeper issues. submitted by /u/WinOdd7962
Originally posted by u/WinOdd7962 on r/ClaudeCode
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