Comments URL: https://news.ycombinator.com/item?id=46565184

Points: 3

# Comments: 0

That has been my experience as well. I have developed my own little technique around this idea, where you invite tight areas of your body to soften and spontaneously make tiny stretching or unwinding movements - without forcing, bracing, or following a scripted routine. I call it Intuitive Release.

https://dirk-loss.de/intuitive-release/

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.

Comments URL: https://news.ycombinator.com/item?id=46181923

Points: 1

# Comments: 1

I wonder if nsjails or gVisor may be useful as well. Here's a more comprehensive list of sandboxing solutions: https://github.com/restyler/awesome-sandbox

Comments URL: https://news.ycombinator.com/item?id=44982554

Points: 2

# Comments: 0

- Visualize log level patterns with --levelmap showing the density of different log levels over time (e.g., IIIWWEEE for a sequence of INFO, WARNING, ERROR)

- Visual separation of time gaps with --mark-gaps INTERVAL (e.g., 1h) to spot suspicious quiet periods

- Condense bursts of events with --fuse INTERVAL (e.g., 1h) to highlight when activity starts and ends

- Built-in regexes for common patterns (URLs, IPs, file paths, email addresses, common error messages, and many others)

- Python expressions for complex filtering: --where "int(status) >= 500 and 'timeout' in message.lower()"

- Built-in support for logfmt, JSON Lines, CSV, SQLite and others, plus ability to write ad-hoc parsers in Python on the command line

- Custom output formatting using Python code and templates

klp is a single Python file with no dependencies beyond Python 3.7+. Install with pip install klp-logviewer or download the file from GitHub:

<https://github.com/dloss/klp>

I originally built this for my own debugging needs at work where I couldn't install other tools. When my employer allowed me to open-source it (MIT license), I added more features to explore ideas I hadn't seen in other log tools.

Since I'm just a single developer maintaining this in my spare time, I've included a carefully curated list of more robust alternatives in the README.

I welcome your feedback on klp and its features, and would be delighted if these ideas inspire other log tools - especially approaches like visual time gaps, fusing events and log level maps.

Comments URL: https://news.ycombinator.com/item?id=42424411

Points: 3

# Comments: 0