opencode

A commit added 119 lines of execution rules to AGENTS.md. I expected fresh bureaucracy; the opposite: capped interviews, scaled cards, capped iterations.

TL;DR: A 119-line update to the AGENTS file initially looked like heavy process but actually aims to cut friction. It uses lightweight work cards scaled to task size, caps fix attempts at three, and requires verifiable checks before marking done. Clear priorities and scope fences prevent guesswork, silent creep, and wasted context while letting you step away confidently.

OpenCode's usage board is led by cheap open models, not benchmark kings. How I read usage, retention, and price before switching models.

TL;DR: Benchmark leaders like GPT-5.6 top SWE-Bench, yet deepseek-v4-flash leads OpenCode's real-world usage, suggesting cost, speed, and context length matter more than peak scores. The author recommends checking usage volume, momentum, and weekly retention as satisfaction signals. Treat official model lists as a starting point, then validate against usage data and a small trial in your own repo before committing.