Claude-Code

How I discovered that LLM limitations reveal fundamental truths about software architecture in the AI era The Breaking Point Isn’t What You Think After months of AI-assisted development, I hit a wall that had nothing to do with prompting skills or context limits. The problem was deeper and more fundamental: LLMs lose their minds when applications exceed their cognitive capacity. I wasn’t building monoliths. My applications had proper separation of concerns, clean data models, comprehensive tests, and type safety. The repository pattern, service layers, configuration management - all the architectural best practices were there. But as these well-structured applications grew in complexity, something strange happened. ...

This continues my exploration of AI-assisted development workflows, building on insights from Making Security Ambient and the practical lessons learned from daily Claude Code usage. The Evolution of AI-Assisted Development The progression has been fascinating to observe—and participate in. We started with simple one-shot prompts: “Generate this function.” Then we got smarter about light planning, adding some structure to our requests. From there, we developed systematic AI-assisted workflows like the /plan-task approach I’ve written about, where architectural guardrails transform unpredictable AI assistance into something more craftsperson-like. ...

In the previous article on Beyond Comprehensive Context, I explored how AI-assisted development revealed challenges with comprehensive documentation and token efficiency. What follows are my approaches for memory optimization that emerged from daily use—not as a definitive solution, but as a possible option. The Layered Memory Paradigm What I’ve discovered through daily practice is that Claude Code’s memory system reveals something pretty cool about how expertise actually functions. The system’s three-layered structure mirrors the way I think about knowledge—from universal principles to contextual applications. ...