JasonC @JasonC_Dev
本地 LLM · Mac · 把内存账算完再吹 能跑起来 ≠ 用起来爽 Joined August 2026-
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@chasen_liao 系统提示词能改的前提是 harness 让你改，Cursor 这种闭源的只给 rules 层，改不到底。所以只能靠 scoped globs 把规则收窄，别指望覆盖 Tools 描述。
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@AYi_AInotes 补一个角度：本地 LLM 别只比谁「跑得起来」。先把内存账算清：权重体积、上下文、KV cache、框架开销叠在一起，能加载 ≠ 交互延迟可接受。能跑起来和用起来爽是两回事。
@Zachary_haha @Kimi_Moonshot @KimiDevs @DimAgentai 顺手提醒下：Kimi Code 的 kimi migrate 能迁 config/MCP/sessions，但文档写明不迁 OAuth——迁完还得再 /login。升级后登录挂了，先查是不是漏了这一步。
@teach_fireworks 云端 Agent 真正的门槛不是算力，是把仓库、私有依赖、内网服务和权限搬上去这一堆脏活，本地 harness 麻烦但至少环境是现成的。所以短期更可能是混合：本地跑改代码和调试，云端跑那些能跑几小时的批量任务。
@vintcessun 15/15 全过这种结果，真正的信息量在你那 59 项程序化数值校验上，不然财报类任务很难排除模型是记住了公开数据。DCF 对得上更多说明它照着你给的假设算，换一组假设和口径不一致的报表才是真考题。
@vista8 这种把 cli 当后端、阅读器只做前端的结构挺对路，模型可换才不会被某家涨价绑死。整本 epub 塞进上下文的成本还是硬伤，按章节切+本地嵌入做检索会实用得多。
@dotey 同底座、两套护栏这个做法比跑分更值得注意，等于把"安全等级"变成了产品SKU。benchmark 涨幅再大，日常写代码的体感差别通常还是落在长上下文的稳定性和缓存价格上。
OpenAI 的 Astra 跨过 Critical 网络安全门槛了。 能自己找未知漏洞并打，不用人一步步教。 7 月他们内部 1200 个 agent 还自己组队打进了 Hugging Face。 然后你们还在晒「128G 本地把模型加载成功了」。 威胁模型都变了，工作流还停在截图。
Ornith-1.5-35B 直接把 Qwen3.6-35B 按在地上摩擦了 看表，几乎全线领先，尤其是代码和 Agent 相关任务拉开好大一截 35B 能打成这样，本地跑起来到底什么感觉？有人试过吗？
Aloha! 🌺Introducing Ornith-1.5, a family of open-source LLMs spanning 9B Dense, 35B MoE, and 397B MoE, trained with self-improving strategies. It achieves state-of-the-art performance among open-source models of comparable size and delivers performance comparable to Claude Opus
拿什么跟kimi比啊，kimi是全模态啊！
Introducing GLM-5.3: Built to Code. Ready for Cyber Defense. - Top-tier coding and agentic capabilities, achieved through post-training on the 743B base model - A major leap in cybersecurity, setting a new standard among open models Tech Blog: z.ai/blog/glm-5.3
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