周.乙 (⌐▀͡ ̯ʖ▀)ノJoey
21.7K posts

周.乙 (⌐▀͡ ̯ʖ▀)ノJoey
@JoeyDeepWorld
待人以诚,示人以真。求实求是,不说假话。 剖析技术、制度与资本交织的世界结构,也审视人的命运。 🪐 预言家,🩳 皇帝新装撕裂者。 🤖 AI 🧠思考与传播。 📅 94年起写代码,💻 UNIX 和 C 打底。 深度长文:文章 / https://t.co/QaI6ZYPFoD


If you aren't yet bold enough to install the Codex app, you can stay in the presence of your orange crab and point it at GPT 5.6 Sol. Takes 5 minutes. Kudos to Theo for explaining one of the ways to get this done. Step 1: Install CLIProxyAPI Step 2: Connect Step 3: Define following alias and enjoy claudex ``` alias claudex='CLAUDE_CODE_SUBAGENT_MODEL=gpt-5.6-sol \ CLAUDE_CODE_ALWAYS_ENABLE_EFFORT=1 \ CLAUDE_CODE_MAX_TOOL_USE_CONCURRENCY=3 \ ENABLE_TOOL_SEARCH=false \ claude --model gpt-5.6-sol' ``` If this gets blocked, I owe you a reset.







At 11k employees, our AI costs are going up. Which model & harness should we use to lower cost but also retain great quality? We didn't want to blindly trust public benchmarks. So we ran a comprehensive evaluation on our tasks, code base, infra. It's been produced by more than 3,000 software engineers, spans 3 hyperscalar clouds and many languages and tasks. The results are surprising. We find that for the SAME mdoel, the choice of harness can significantly save costs (~2x). We also find that GLM 5.2 performs extremely well. We run Omnigent in front of these and can easily multiplex different harnesses and models for different tasks. Check it out: databricks.com/blog/benchmark…

















