Mid-research on the LocalAI bakeoff, I got asked a blunt question: why not just run Coder Agents as the homelab supervisor too? The answer split my original coding-vs-general-purpose hypothesis into a sharper dichotomy — ephemeral/invoke-driven/git-centric vs. persistent/event-driven/tool-centric — and pointed straight at Turnstone as the next thing worth a real bakeoff.
Two Discord bots, one 14B model, five fitness-tracker tasks. Both agents failed on the first try. Getting them working required debugging context overflow, silent tool parameter drops, and a chat template flag that changes everything. The results reveal as much about the state of local AI agents as they do about which framework won.
Two weeks of using Qwen3.5-35B as my daily AI assistant — the Jinja template fix that made it work, the thermal spam incident that almost ended the experiment, and the session-context gap that makes it feel like a junior dev every morning. Plus: what's next with Qwen 3.6.
I built a Model Context Protocol server into the fitness tracker I vibe coded a year ago, wired it through Vercel and Coder workspaces, and ended the afternoon asking my Discord bot what my last workout was. Here's the build, the wrong turn into Coder's AI Bridge, the workaround, and how the same endpoint now serves Claude Desktop, Codex, Coder Agents, and OpenClaw.