Martin Fowler

AI editors like Cursor can generate code with remarkable speed using LLMs, handling boilerplate and providing functional snippets for various tasks. However, when building robust systems, functional correctness is only the starting point. Code must also be safe, predictable, maintainable, and free from subtle side effects.

This article demonstrates, through a dialogue between a developer and an LLM, how expert guidance is crucial to transform an initial, potentially unsafe code snippet into a robust, system-ready component. This development session was done as part of a larger development effort to write a BoltDB like storage engine for an internal workshop. This also demonstrates how breaking the larger development efforts into smaller programming sessions can be more effective with LLM based development. We’ll use the common task of reading and writing a fixed-structure header within a Java ByteBuffer.

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