A new approach to log analysis, dubbed “vibe coding,” is gaining traction among developers seeking to leverage large language models (LLMs) for practical tasks. Lee Hutchinson recently detailed an experiment building a Python-based log colorizer for Nginx access logs using Claude Code, highlighting both the benefits and limitations of this emerging workflow.
The Log Colorizer: A Practical Application
Hutchinson’s project resulted in a compact, roughly 400-line Python script designed to make Nginx access logs more readable. The script’s relatively small size was intentional, allowing for easier auditing compared to larger, more complex systems. The choice of Python was driven by its robust regular expression capabilities and overall readability, factors Hutchinson considered crucial for the project. A sanitized sample log was provided to Claude Code as input.
The development process unfolded over two sessions, with a delay between them due to limitations in the model’s credit availability. Despite this hurdle, Hutchinson was able to successfully complete the project, demonstrating the potential of LLMs to assist in software development even within constrained environments.
A Cache-Related Roadblock
The project wasn’t without its challenges. A cache-race condition emerged between Apple News bots and the Discourse commenting system, causing posts to be cached before comments could fully load. This resulted in a display issue where comments weren’t immediately visible. Hutchinson addressed this issue with a small modification – a “mu-plugin” that adds a “DO NOT CACHE ME” header until Discourse attaches its thread to the post. The root cause, Hutchinson determined, was a race condition in how upstream services cache and assemble post pages.
Problem spotted. Note the AppleNewsBots hitting the newly published post before Discourse can do its thing and the final version of the page with comments is ready. Credit: Lee Hutchinson
LLMs as Tools, Not Replacements
Hutchinson’s experience underscores a key point: LLMs are not a replacement for skilled developers. They are tools that can be helpful within well-defined boundaries, but require precise prompting, careful scoping, and human verification to avoid issues. “Vibe coding,” as Hutchinson terms it, can be a fun and valuable approach for constrained tasks, but it’s not a substitute for fundamental competence.
The final product, a functional log colorizer, served as a practical experiment that yielded tangible benefits and a clearer understanding of the strengths and weaknesses of AI-assisted coding. Hutchinson intends to continue exploring the possibilities of “vibe coding” in future projects, seeking to refine the workflow and identify realistic applications in real-world scenarios.
The colorizer itself is a 400-line Python script, and the entire codebase, along with the prompts used, fits within Claude Code’s context window, making it easily auditable.
