https://blog.chuanxilu.net/en/tags/opencode/Recent content in OpenCode on Chuanxilu for Skilled Homo sapiensHugoen-USSat, 18 Apr 2026 10:00:00 +0800https://blog.chuanxilu.net/en/posts/2026/04/managing-context-length-in-ai-coding-sessions/Sat, 18 Apr 2026 10:00:00 +0800https://blog.chuanxilu.net/en/posts/2026/04/managing-context-length-in-ai-coding-sessions/Someone in a group chat complained that GPT-5.4 performed worse than Doubao, ByteDance's chatbot—the model would give irrelevant answers without even reading the question. After asking some follow-up questions, I learned they had fed it many documents and the conversation had gone on for a long time. This probably wasn't the model's problem—it was context rot. The conversation had gotten so long that the model could no longer 'see' the current task clearly. This raises an overlooked problem: in the process of vibe coding or writing, how do you manage context effectively to avoid token and time wasted on model performance degradation?https://blog.chuanxilu.net/en/posts/2026/04/from-markdown-to-mcp-server-gear-protocol/Thu, 16 Apr 2026 19:00:00 +0800https://blog.chuanxilu.net/en/posts/2026/04/from-markdown-to-mcp-server-gear-protocol/Aristotle's reflection rules started as a flat Markdown file — append-only, forgotten, no rollback. When dozens of rules accumulated, I realized the file wasn't enough. This started a design iteration path from append-only to Git-backed MCP Server. That path led to something called GEAR.https://blog.chuanxilu.net/en/posts/2026/04/from-scars-to-armor-harness-engineering-practice/Sat, 11 Apr 2026 01:00:00 +0800https://blog.chuanxilu.net/en/posts/2026/04/from-scars-to-armor-harness-engineering-practice/The first version of Aristotle looked smooth. In practice, it exposed four architectural problems. Fixing them validated the trust model and harness engineering framework from Part 3 — every constraint encodes a trust judgment.https://blog.chuanxilu.net/en/posts/2026/04/a-trust-boundary-design-experiment/Mon, 06 Apr 2026 18:00:00 +0800https://blog.chuanxilu.net/en/posts/2026/04/a-trust-boundary-design-experiment/The same reflection mechanism on different platforms, their complexity differing by an order of magnitude — but the complexity itself reveals a deeper question: when should we trust AI's judgment, and when should we step in?https://blog.chuanxilu.net/en/posts/2026/04/aristotle-ai-reflection/Mon, 06 Apr 2026 10:00:00 +0800https://blog.chuanxilu.net/en/posts/2026/04/aristotle-ai-reflection/Installing reflection capability into AI coding assistants—when the model makes a mistake, immediately trigger root cause analysis and transform the correction into persistent rules.