Show HN: Kelora – Turn messy logs into structured data

I'm sharing Kelora, a hobby project that I have been developing over the last 6 months. It's a scriptable log processor for the command line, with 150+ built-in functions for parsing, transforming, and analyzing log files and streams.

My original idea was to have an easy to deploy, potentially faster and more correct "rewrite in Rust" of my Python log processing tool klp. It quickly turned into an AI coding experiment: how far I could get with vibe-coding, in a programming language that I barely know?

Kelora's code and extensive test suite have been generated entirely by AI agents (Claude CLI with Sonnet 4 to 4.5, Codex CLI with GPT5-codex). I come up with feature ideas and discuss it with the AI agents. The AIs then write the spec, the implementation, tests, docs and CI. I don't review the code, but I use the resulting program myself and review the docs. The result is fully functional and quite useful, in my opinion.

I am fully aware that this vibe-coding process has its problems. Without human review we cannot be sure that Kelora does (only) what it's supposed to do. And although Kelora passes 1000+ automatic tests and several checks (clippy, cargo audit, cargo deny, cargo fuzz), that probably shouldn't be sufficient to use it in production. In this sense, it's an experiment, or a prototype. So maybe just run it against the example logs I've provided in the GitHub repo. Or read the docs to get inspiration for your own log processing tool.

Because that's what I want to share: My ideas about a log processing tool with embedded scripting that can help turn messy logs into structured data. Some interesting features like level maps, windows and spans, tracking and state, JWT parsing, pseudonymisation, etc. And last but not least, my joy of working together with AI agent on a software project that would otherwise have been much too big for me. I've never had so much fun in 30+ years of (hobby) programming.