Lee Hutchinson experiments with Claude Code to build a Python-based log colorizer aimed at making Nginx access logs easier to read. The project is a compact, roughly 400-line single-file script that Hutchinson says can be audited more easily than larger systems.
Using Claude Code in plan mode, Hutchinson steered the model toward Python for its regex capabilities and readability, then fed it a sanitized sample log. The effort unfolded across two sessions—the second delayed by the model’s credit limits but ultimately productive—a hallmark of what he calls “vibe coding.”
One major snag emerged: a cache-race between Apple News bots and Discourse, causing the post to be cached before comments could load. The workaround involved a small mu-plugin that adds a “DO NOT CACHE ME” header until Discourse attaches its thread. He notes the root cause is a race condition in how upstream services cache and assemble post pages.
The piece leans on a larger point: LLMs are not magic developers. They are tools that can help within well-defined boundaries, but they require precise prompting, careful scoping, and human verification to avoid dead ends. Hutchinson concludes that vibe coding is fun and sometimes valuable for constrained tasks, but not a substitute for competence.
In the end, the log colorizer served as a personal experiment that yielded practical gains and a clearer view of when these AI-based assistants shine—and when they don’t. Hutchinson hints at future experiments, continuing to explore what “vibe coding” can realistically accomplish in real-world workflows.
