skill信息: GitHub - op7418/guizang-social-card-skill: 🪧 Claude Code / Codex skill — generate Xiaohongshu carousels & WeChat 21:9+1:1 cover pairs. Editorial × Swiss visual systems, 28 layouts, 10 themes, single-file HTML → PNG. 小红书图文 + 公众号封面对 · GitHub 大家有什么好玩的skill 吗，最近在尝试这些 2 个帖子 - 2 位参与者 阅读完整话题
最近在看 Kubernetes 上 AI 推理服务的冷启动问题，发现很多时候慢的不只是模型加载，容器镜像本身也很夸张。 比如 vLLM 这类镜像，里面有 PyTorch 、CUDA 、Python 依赖、系统库，动不动就是 10GB+。传统 containerd / overlayfs 路径下，节点要先完整下载并解压镜像，Pod 才能真正起来。对 Karpenter 这种弹性扩容场景来说，这部分时间会很明显。 我们做了一个小项目 Hermes： https://github.com/cloudpilot-ai/hermes 想法是：不让业务团队改 Dockerfile 、不重建镜像、不改 CI/CD ，也不改原来的 image reference 。平台侧定义一个 HermesPolicy ，controller 在集群内自动为匹配到的镜像构建并缓存 SOCI index ，节点上的 daemon 再用这些 index 做 lazy loading 。 这次用 EKS + Karpenter 跑了一个简单对比，镜像是： 763104351884.dkr.ecr.us-east-1.amazonaws.com/vllm:0.9-gpu-py312-ec2 大概 10.8GB 。 普通节点上，从 Pod 调度到节点后，到容器 Running/Ready： 5m04s - 29s = 4m35s 开启 Hermes 的节点上，在 HermesPolicy 已经 Ready 、SOCI artifact 已经构建好的前提下： 44s - 30s = 14s 也就是这个场景里，镜像拉取/挂载到容器启动这段，从 4m35s 降到了 14s 。 需要强调一下：这个结果不包含首次 index 构建耗时，也不等于 vLLM first token latency 。Pod Ready 变快，只说明容器镜像这条路径被 lazy loading 优化了。后面还需要继续测 vLLM readiness 、first request TTFT 、warmup 后真实请求延迟。 Hermes 现在的定位更像一个集群侧能力：应用继续发原来的 OCI image ，平台通过策略决定哪些镜像需要被 lazy load 。类似： apiVersion: hermes.cloudpilot.ai/v1alpha1 kind: HermesPolicy metadata: name: prod-large-images spec: paused: false imageSelectors: - imageRegex: ".*vllm.*" platforms: - linux/amd64 目前还比较早期，欢迎大家关注项目： https://github.com/cloudpilot-ai/hermes
最近在看 Kubernetes 上 AI 推理服务的冷启动问题，发现很多时候慢的不只是模型加载，容器镜像本身也很夸张。 比如 vLLM 这类镜像，里面有 PyTorch 、CUDA 、Python 依赖、系统库，动不动就是 10GB+。传统 containerd / overlayfs 路径下，节点要先完整下载并解压镜像，Pod 才能真正起来。对 Karpenter 这种弹性扩容场景来说，这部分时间会很明显。 我们做了一个小项目 Hermes： https://github.com/cloudpilot-ai/hermes 想法是：不让业务团队改 Dockerfile 、不重建镜像、不改 CI/CD ，也不改原来的 image reference 。平台侧定义一个 HermesPolicy ，controller 在集群内自动为匹配到的镜像构建并缓存 SOCI index ，节点上的 daemon 再用这些 index 做 lazy loading 。 这次用 EKS + Karpenter 跑了一个简单对比，镜像是： 763104351884.dkr.ecr.us-east-1.amazonaws.com/vllm:0.9-gpu-py312-ec2 大概 10.8GB 。 普通节点上，从 Pod 调度到节点后，到容器 Running/Ready： 5m04s - 29s = 4m35s 开启 Hermes 的节点上，在 HermesPolicy 已经 Ready 、SOCI artifact 已经构建好的前提下： 44s - 30s = 14s 也就是这个场景里，镜像拉取/挂载到容器启动这段，从 4m35s 降到了 14s 。 需要强调一下：这个结果不包含首次 index 构建耗时，也不等于 vLLM first token latency 。Pod Ready 变快，只说明容器镜像这条路径被 lazy loading 优化了。后面还需要继续测 vLLM readiness 、first request TTFT 、warmup 后真实请求延迟。 Hermes 现在的定位更像一个集群侧能力：应用继续发原来的 OCI image ，平台通过策略决定哪些镜像需要被 lazy load 。类似： apiVersion: hermes.cloudpilot.ai/v1alpha1 kind: HermesPolicy metadata: name: prod-large-images spec: paused: false imageSelectors: - imageRegex: ".*vllm.*" platforms: - linux/amd64 目前还比较早期，欢迎大家关注项目： https://github.com/cloudpilot-ai/hermes
最近在看 Kubernetes 上 AI 推理服务的冷启动问题，发现很多时候慢的不只是模型加载，容器镜像本身也很夸张。 比如 vLLM 这类镜像，里面有 PyTorch 、CUDA 、Python 依赖、系统库，动不动就是 10GB+。传统 containerd / overlayfs 路径下，节点要先完整下载并解压镜像，Pod 才能真正起来。对 Karpenter 这种弹性扩容场景来说，这部分时间会很明显。 我们做了一个小项目 Hermes： https://github.com/cloudpilot-ai/hermes 想法是：不让业务团队改 Dockerfile 、不重建镜像、不改 CI/CD ，也不改原来的 image reference 。平台侧定义一个 HermesPolicy ，controller 在集群内自动为匹配到的镜像构建并缓存 SOCI index ，节点上的 daemon 再用这些 index 做 lazy loading 。 这次用 EKS + Karpenter 跑了一个简单对比，镜像是： 763104351884.dkr.ecr.us-east-1.amazonaws.com/vllm:0.9-gpu-py312-ec2 大概 10.8GB 。 普通节点上，从 Pod 调度到节点后，到容器 Running/Ready： 5m04s - 29s = 4m35s 开启 Hermes 的节点上，在 HermesPolicy 已经 Ready 、SOCI artifact 已经构建好的前提下： 44s - 30s = 14s 也就是这个场景里，镜像拉取/挂载到容器启动这段，从 4m35s 降到了 14s 。 需要强调一下：这个结果不包含首次 index 构建耗时，也不等于 vLLM first token latency 。Pod Ready 变快，只说明容器镜像这条路径被 lazy loading 优化了。后面还需要继续测 vLLM readiness 、first request TTFT 、warmup 后真实请求延迟。 Hermes 现在的定位更像一个集群侧能力：应用继续发原来的 OCI image ，平台通过策略决定哪些镜像需要被 lazy load 。类似： apiVersion: hermes.cloudpilot.ai/v1alpha1 kind: HermesPolicy metadata: name: prod-large-images spec: paused: false imageSelectors: - imageRegex: ".*vllm.*" platforms: - linux/amd64 目前还比较早期，欢迎大家关注项目： https://github.com/cloudpilot-ai/hermes
今天无意间看到deepseek的招聘 https://app.mokahr.com/social-recruitment/high-flyer/140576#/jobs，14号发布的，这种大概率会上市的公司，能进去还是蛮不错的，发出来给各位佬友看看 2 个帖子 - 1 位参与者 阅读完整话题
要发力了吗 https://app.mokahr.com/social-recruitment/high-flyer/140576#/job/54f386a9-913b-4626-9bf4-e1709b62fcda
要发力了吗 https://app.mokahr.com/social-recruitment/high-flyer/140576#/job/54f386a9-913b-4626-9bf4-e1709b62fcda
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Hello, An internal investigation of suspicious signals associated with your account indicates a violation of our Usage Policy. As a result, we have revoked your access to Claude. To appeal our decision, please fill out this form or learn more about the appeals process here. Regards Anthropic's Safeguards Team
Hello, An internal investigation of suspicious signals associated with your account indicates a violation of our Usage Policy. As a result, we have revoked your access to Claude. To appeal our decision, please fill out this form or learn more about the appeals process here. Regards Anthropic's Safeguards Team
Hello, An internal investigation of suspicious signals associated with your account indicates a violation of our Usage Policy. As a result, we have revoked your access to Claude. To appeal our decision, please fill out this form or learn more about the appeals process here. Regards Anthropic's Safeguards Team
Hello, An internal investigation of suspicious signals associated with your account indicates a violation of our Usage Policy. As a result, we have revoked your access to Claude. To appeal our decision, please fill out this form or learn more about the appeals process here. Regards Anthropic's Safeguards Team
Hello, An internal investigation of suspicious signals associated with your account indicates a violation of our Usage Policy. As a result, we have revoked your access to Claude. To appeal our decision, please fill out this form or learn more about the appeals process here. Regards Anthropic's Safeguards Team