swq115

Thanks! Yeah, local-first was the main goal.

Thanks. Aliases live in your shell config, but the commands you actually run are tied to the venv. Felt natural to attach them there.

Previous Show HNs for this project focused on the CLI/MCP side; posting again because `backup drill` is the feature I think actually solves a real problem people pretend they don't have.

Repo: https://github.com/Higangssh/homebutler Happy to answer anything.

Single Go binary (~15MB, zero deps) for homelab management. The backup drill feature creates an isolated container, restores your backup into it, and health-checks it. Your running services are never touched. Also handles Docker, WoL, network scanning, and multi-server SSH.

I run a few homelab servers and got tired of SSH-ing into each one every time I needed to check something. Especially at 3am when an alert fires — I just didn't want to open my laptop anymore.

So I built homebutler. It's a single Go binary (~15MB), zero dependencies. Point it at your servers via SSH and you get: system status, Docker control, Wake-on-LAN, port scanning, network discovery, alerts, and backup/restore. There's also a web dashboard (homebutler serve) and a TUI (homebutler watch).

It has a built-in MCP server so you can plug it into Claude Desktop, ChatGPT, Cursor, or whatever. But the AI only talks through structured JSON commands — it can restart a container but can't rm -rf anything. No raw shell access, ever.

GitHub: https://github.com/Higangssh/homebutler

Feedback welcome — especially on the security model and what commands you'd want added.

I run a few homelab servers and got sick of opening SSH sessions every time something broke at night. So I built this — a single Go binary that handles status checks, Docker control, WoL, port scanning, and alerts across multiple servers. It can also self-heal basics like restarting crashed containers automatically.

The part I care about most: it has a built-in MCP server, so you can plug it into Claude Desktop or whatever AI tool you use. But the AI only talks to homebutler through structured JSON, it never gets a raw shell. Felt important given how agents have been behaving lately.

~15MB, zero dependencies, MIT licensed. Happy to talk about the architecture if anyone's curious.

I run a few servers at home (Mac Mini M4 + Raspberry Pi) and got tired of the same 3 AM routine — SSH in, check what crashed, restart a container, go back to sleep.

I tried Portainer, btop, Uptime Kuma — all great tools, but I ended up with three dashboards open and still had to SSH in to actually fix things.

So I built homebutler. It's a single Go binary (~15MB) that packs a CLI, a TUI dashboard (Bubble Tea), and a web UI (compiled in via go:embed — no Node, no external assets). It manages Docker containers, monitors CPU/memory/disk across multiple servers over SSH, scans ports, sends alerts, and does Wake-on-LAN.

It also has a built-in MCP server, so AI tools can manage your homelab too — but it works perfectly fine without it.

GitHub: https://github.com/Higangssh/homebutler

Happy to answer any questions about the architecture or design choices.

The Game Boy Camera was 128x112 pixels with 4 shades of gray. The fact that people are still finding ways to pull images off these things almost 30 years later is peak hacker energy

The irony of your vulnerability scanner being the vulnerability.

I run a small homelab (Mac Mini + RPi5) and tried Cockpit too. Great for single server monitoring, but once I had multiple nodes, I kept SSH-ing into each box anyway.

Ended up wanting something CLI-first that could check all servers at once without opening a browser. The web UI is nice for a quick glance though.

Interesting approach using Signal for the transport layer. I've been working with real-time audio pipelines (chrome.tabCapture → Whisper) and the latency tradeoff between STT chunk size and accuracy is always tricky. What's the end-to-end latency like on a video call?

Interesting approach. The mmap streaming idea is clever, but I'd love to see real-world benchmarks beyond TinyLlama — especially for the 140B claim. Running that on a Mac Mini with 16GB would be the real proof point.

For context, I run a Mac Mini M4 as a homelab server and the memory pressure from even 7B models is noticeable. Curious how this handles sustained inference without thermal throttling.

Pretty lightweight — it SSHes into each box and runs homebutler locally there, so the overhead is basically one SSH connection + a quick read of /proc and the Docker socket per check. No background daemon sitting there polling, it only runs when you ask or when alerts --watch fires.

I run it across 3 machines (Mac Mini, Pi 5, and another box) and haven't noticed any impact. The binary itself is ~15MB and idles at zero CPU since it's not a long-running service.

Added --theme matrix since posting — digital rain with text resolve animation. https://github.com/Higangssh/gitcredits