Opencode

CLI-dependent degradation on a quantized 552B MoE: concurrency hammers, a logging proxy that exposed what opencode really sends (nothing — and that was the bug), one reproduced episode of fluent Chinese nonsense in twelve cycles, and the missing generation_config.json that separated an uncapped top_p 1.0 tail from DeepSeek’s official recipe. Plus the reasoning-effort knob that exists as 0-100 in one engine and string levels in another.

I have been building Cairnkeep since early July: project-scoped memory that survives sessions and crosses harnesses, a retrieval-first protocol agents must follow instead of new autonomy, least-authority MCP surfaces (read-only tool profiles, capability gates, immutable context packs), and optional, explicit opt-ins for everything networked. This post is the what and the why, the production deployment I run it in (a VPS-hosted memory server for a four-machine fleet, with the work laptop deliberately isolated), the 26-lesson learning path with its course labs repo, and the companion video series on my BitsEntangled YouTube channel.

Instead of picking one model per task, I made three machines cooperate: a big thinking model for primary reasoning, a fast local 27B for subagent loops and images, a router with automatic failover in front of everything, and shared agent memory. Total cost: electricity. Here is the full architecture, the benchmarks, and the lessons learned the hard way.