// Posts tagged: coder

My gaming rig’s Windows partition used to cost me 15-45 minutes of manual clicking every time I booted into it. One afternoon: a Coder workspace talking to it over Tailscale and SSH, an inventory script that catalogs every update channel on the machine, three Scheduled Tasks that actually do the work, a toast notification bug that took longer to fix than the automation itself, and a Next.js dashboard on Vercel to see it all without ever touching the machine.

115 days after building this blog from a cabana in Cabo, I spent one afternoon — 2:54 PM to 6:23 PM, back to back — running a chain of admin panel improvements: image upload, a visual TODO viewer, drag-and-drop reorder, a “launch agent” button, four rounds of troubleshooting it, and three mobile layout bugs found by actually using it on my phone at the end.

An AI Transformation team at a Fortune 500 financial services firm told me they’ve shipped 19,000 skills across 175 unique Coder workspace types in six months. That’s not a headcount story. It’s proof that as AI intelligence becomes a commodity, the real competitive advantage shifts to how businesses codify and distribute their proprietary workflows.

I kept typing the same paragraph into Coder Agents chat every time a session ran long or I needed to SSH into the homelab. A Slack conversation with a colleague turned that annoyance into five reusable slash commands — and along the way I discovered Coder Agents actually has two entirely separate skill systems, only one of which the `/` menu will ever show you.

Sometimes your agent’s turn just ends with no result — no error, no output, nothing. Nudging it usually gets you the answer that was there the whole time. It’s tempting to write that off as a UX hiccup. Sometimes it’s a real bug, and finding out is part of the fun of vibe coding. A walkthrough of why that’s worth investigating, when it crosses the line into “file this,” and how to do it well — using a hanging GitHub tool call as the live example.

I put Qwen 3.6 27B, Qwen 3.6 35B-A3B, Qwythos-9B, GLM-4.7-Flash, and Nemotron-3-Nano through the same real coding task on my homelab RTX 5090. Along the way I had to live-patch two separate llama.cpp bugs — and even after fixing them, I couldn’t fully prove one model’s failure wasn’t the harness’s fault.

I gave five local LLMs and one frontier cloud model the same coding task on my homelab: build a tag manager for the blog’s admin panel. Only two shipped anything. Here’s what happened.

A second user joined the homelab Coder instance and couldn’t push to GitHub. What looked like a missing config turned into five chained problems, a domain migration aftershock, an agent-debugging-an-agent meta-moment, and the discovery that the same credential helper bug had been “fixed” four times in ten days — and never actually deployed.

While waiting for massive open source models to download, I tackled the homelab backlog: custom domain for my Coder instance via Cloudflare Tunnel, security hardening (with a gotcha that could kill your AI search visibility), and wiring up MCP servers to give agents superpowers.

The journey from “I should build a home lab” to a fully configured self-hosted Coder server with GitHub integration, multi-user workspaces, and AI agents that actually know how to use the tools available to them.