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Jerry Liu agrees with Jesse Zhang that people underestimate the importance of good abstractions and maintainability, noting that LLMs are popular partly due to the ease of using them for inference rather than training.
The article warns that relying on LLMs to write code without maintaining good patterns teaches the AI bad habits, leading to a codebase full of duplicated logic and ever-worsening code quality.
The article argues that current AI agent frameworks treat agents as black boxes, making them unmaintainable, and proposes a Git-native architecture (Lyzr GitAgent, OpenGAP) where agent logic is version-controlled as flat files with pull requests for rollback and auditability.
The author presents a hierarchical priority list for software development: usefulness to end users, correctness, and maintainability/efficiency, arguing that all efforts should maximize user utility.
The article highlights the problem of AI memory becoming unreliable after six months, with contradictions and drifted summaries, and questions whether the industry is focusing on adding more storage rather than improving maintainability.
The article argues that software teams often over-optimise for micro-performance benchmarks at the expense of developer experience and engineering throughput, which are the true bottlenecks for long-term delivery speed and maintainability.