Lixie

When I wrote the first iteration of the Lixie tool about year ago (early 2024), my idea was to identify which logs were boring (most of them), interesting (very few of them) and unknown (not yet classified). At the time I chose not to use ‘AI’ (LLMs) for it, and I am still not that convinced they are the best way to approach that particular problem. Ultimately it boils down to human judgment of what is useful is much more realistic (at least in my context) than what the LLMs ‘know’ (absent fine-tuning and-or extensive example sets which I do not by definition have for my personal logs). After choosing not to use LLMs for it, it was just matching exercise - structured logging messages against an ordered set of rules. ...

During the new home router infra exercise, I also chose to set up as reasonable as possible (lightweight) observability stack for my use. The holy trinity is defined to be metrics, logs, and traces, but as I do not really do much that requires tracing I focused initially on the first two. This exercise occurred mostly in February. Software choices Visualization I chose to go with Grafana. I am very familiar with it, and there seems to be an ecosystem of third-party tools which replicate some of what Aiven had (e.g. dashboard backup/restore tooling, dashboard rewriter I wrote fingon/gg-grafana: Grafana dashboard sanitizer as I could not find something similar in the open world, and so on). ...