花叔 (@AlchainHust)

多谢Fable5回归，对Fanbox（Coding agent的驾驶舱）做了大幅度的更新。 目前终端快捷启动的选项，已经从Claude Code、CodeX之外，又增加了Hermes Agent、OpenClaw、Kimi Code、ZCode等10多个主流Coding Agent产品。 新增「回合存档」功能，让不理解不了解Git机制的编程小白，也能自动化快速回到之前的项目状态，避免项目被搞坏的问题。 优化「项目记忆」功能，你可以根据打开的项目文件，快速识别和回到之前任意项目的agent对话历史中。

We're introducing GLM-5.2, our latest flagship model for long-horizon tasks. It marks a substantial leap in long-horizon task capability over its predecessor GLM-5.1 and, for the first time, delivers that capability on a solid 1M-token context. GLM-5.2's new capabilities include: Solid 1M Context: A solid 1M-token context that stably sustains long-horizon work Advanced Coding with Flexible Effort: Stronger coding capabilities with multiple thinking effort levels to balance performance and latency Improved Architecture: We propose IndexShare, which reuses the same indexer across every four sparse attention layers, reducing per-token FLOPs by 2.9× at a 1M context length. We also improve GLM-5.2’s MTP layer for speculative decoding, increasing the acceptance length by up to 20% Pure Open: An MIT open-source license — no regional limits, technical access without borders Supporting long-horizon tasks starts with making long context engineering-usable: the model must maintain quality across long, messy coding-agent trajectories, not just accept more tokens. A 1M context is easy to claim, but much harder to keep reliable under real engineering pressure. To this end, we substantially expanded 1M-context training for coding-agent scenarios, covering large-scale implementation, automated research, performance optimization, and complex debugging. The result is a long-context system that is not only wide in scope, but solid in execution: a practical substrate for sustained engineering work. This capability is reflected in GLM-5.2's performance on three long-horizon coding benchmarks. FrontierSWE measures whether an agent can complete open-ended technical projects at the scale of hours to tens of hours, spanning systems optimization, large-scale code construction, and applied ML research. On this benchmark, GLM-5.2 trails Opus 4.8 by only 1%, while edging out GPT-5.5 by 1% and Opus 4.7 by 11%. On PostTrainBench, where each agent is given an H100 GPU and evaluated by how much it can improve small models through post-training, GLM-5.2 outperforms both Opus 4.7 and GPT-5.5, ranking second only to Opus 4.8. On SWE-Marathon, an ultra-long-horizon software engineering benchmark covering tasks such as building compilers, optimizing kernels, and developing production-grade services, GLM-5.2 still has room to grow, trailing Opus 4.8 by 13% while remaining second only to the Opus series. Across all three benchmarks, GLM-5.2 is the highest-ranked open-source model, showing that its 1M context has translated into practical long-horizon delivery capability.

虽迟但到！一大堆人找我要的《Loop Engineering橙皮书📙》来了。 32页！老规矩：免费、开源、评论区自取！ 以及，你需要知道的是，这当然是我的橙皮书skill自动写的，它收集整理了现在关于这个概念几乎所有的可靠信源的讨论，然后读取了我的写作风格和经验输出。 不是关于loop enginnering的圣经，也不是你借此就能精通了。它完全可以是，以及只是一份帮助你快速全方位理解loop engineering概念、来源、以及实践要点的一份参考资料。 enjoy～