Links & Stats
👉 https://github.com/caronc/apprise
Every service you touch has its own way of pinging you. Telegram has a bot API, Discord has webhooks, Slack has incoming hooks, Gotify has its own thing, and ntfy — the one you actually like — has a REST endpoint. Wiring each one into your scripts, cron jobs, and monitoring means learning N different APIs and maintaining N different code paths.
Every few weeks a new AI gateway appears on my radar, and every one tells the same seductive story: “Stop paying too much for LLMs. One endpoint, adaptive routing, zero markup. You’ll cut your bill 40%.”
I keep almost believing it. Whenever I do, I pull the price sheets — not the homepage claims, the per-token numbers. That exercise keeps saving me from a mistake, and it’s a repeatable enough pattern that it’s worth writing down. If you run any kind of LLM routing layer, you’ve probably heard this pitch and wondered if you’re leaving money on the table.
If you’re evaluating AI coding agents — Claude Code, OpenHands, Codex CLI, whatever — you’re probably doing it wrong. Running them against your own infra is dangerous. Running them manually is slow. Running them unrepeatably is pointless.
Harbor fixes that. It’s a framework from the creators of Terminal-Bench that lets you define sandboxed agent tasks, run evaluations against any agent/model combo, and scale across cloud providers.
Harbor wraps each agent task in an isolated environment (Docker locally, or Daytona/Modal/LangSmith/Novita Sandbox in the cloud). You specify:
Markdown is the lingua franca of documentation, but it’s static. You write ./deploy.sh, and six months later someone runs it in a terminal with different state and gets a different result. The code rots. The docs drift.
Three tools try to fix this by making Markdown executable, but they take very different approaches. Let me break them down.
“Jupyter notebooks, but for your ops runbooks.”
Two Go SSH tools landed within the last year, both hovering around the same star count, both solving very real pain points. But they’re almost entirely different tools that happen to share a protocol prefix.
Let’s break them down.
Links & Stats 👉 https://github.com/alebeck/boring
A dedicated SSH tunnel manager with a daemon architecture. You define tunnels in TOML, boring open starts them, and a background process keeps them alive with automatic reconnection and keepalives. Supports local, remote, and dynamic (SOCKS5) forwarding, works with your SSH config and ssh-agent, and handles Unix sockets.
