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the reason for this weekend project is that we had a kubernetes upgrade that went wrong, and at some point a rollback wasn't possible anymore, so it had to be fixed live during the night while several problems came together. We run a lot of different systems, on-prem and several Kubernetes clusters, and in a situation like that you spend most of the time just figuring out what is actually broken and where.

So i thought that it would be pretty cool to have eyes in the dark in each system that can talk to your "brain".

so the idea is to put a baby owl into each environment. Each owl runs where the systems live, keeps that environment's credentials local, and only dials outbound to a central brain, so there is no inbound hole into prod. It exposes a set of read-only skills, and the agent uses them to gather evidence and form a root-cause hypothesis, so the on-call engineer starts with a head start instead of from zero.

read-only for now, i don't trust it near prod yet and honestly neither should you.

llocal-first for easy self-hosting and to keep credentials on your side. the clustering and recommendations run fully offline with no llm at all. the agent needs a tool-calling llm, you can point it at a remote one, or self-host one (ollama etc.) if you want to stay fully offline.

for non selfhosters: before every remote llm call, nightwatch strips real secrets (unrestorable) and swaps identifiers like ips, hostnames and paths for reversible placeholders, so the model only sees masked data while real values are restored only in the proposed commands and tool calls

Would love if you try it in your Systems

Lathe is an experiment in using LLMs to teach me something new, instead of doing the work for me. It generates a hands-on, source-backed tutorial for any technical topic you want to learn. Then you work through it yourself by reading and typing the code by hand (gasp) in a local UI built for exactly that.

It's a Go CLI plus LLM agent skills (Claude Code / Cursor / Codex). You prompt something like "/lathe build a 3D slicer in Erlang", run `lathe serve` to spin up a local webapp, and read it in your browser. Every tutorial comes with the things that have made self-learning a pleasant experience for me in the past:

- table of contents that follows along as you scroll - side-notes that nudge you to think - exercises for the reader - sources backing up the content that you can use to take you deeper

To help make up for the lack of human brainpower behind the tutorial, you can also ask questions about the content, have another LLM verify the tutorial actually compiles and runs, or extend it with another part (no more "Part 4 of 6" that hasn't seen an update since 2021).

I didn't build lathe to replace human-written tutorials. I built lathe because I _love_ human-written tutorials, but wanted to learn technical domains where no good human-written tutorial exists yet (building a 3D slicer from scratch, making embedded Zig approachable, etc). There's a longer story in the README about how I got started with programming through PSP homebrew tutorials, and why losing that to LLMs bugged me enough to build this.

I'm not here to sell you anything (there's nothing close to a VC-backed startup here :D). It's an LLM, and its output is usually good but not perfect by any means. So far, my experience is that because you're the one typing and actually engaged, you catch the weird stuff (and I'm finding that pushing back on it is its own kind of learning). And yes, it's vibecoded, because it's low scope, low risk, and scratching a personal itch. I run it on Claude Code + macOS personally, other setups should work but I haven't been able to verify them yet.

If you can find resources to learn something that was written by a human, read that first. But Lathe is here to fill in the gaps when that isn't the case, and I hope it serves as an example where LLMs can help us think better, rather than less.

Repo: https://github.com/devenjarvis/lathe

Would love your feedback if you decide to check it out!

You mark checkboxes for what you have on hand (ricotta, sour cream, kefir, buttermilk, yogurt, cottage cheese, lemon, cream of tartar, etc.) and it computes the best recipe based on targets for acid, fat, salt, sugar, and CO2.

My particular favorite are the yeast-raised lemon ricotta kefir pancakes - the best I've ever had.

The math is done in a small pure-ESM library: ingredient composition to component masses and acid moles, a stoichiometry layer, and a bisection solver for the target deficits.

I'm not a chemist, so if something is off, tell me and I will fix it!