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Source channel @githubtrending · Post #14992 · Jul 23

#go#aws#azure#cncf#cost#cost_optimization#finops#gcp#k8s#kubernetes#monitoring#opencost#prometheus OpenCost is a free, open-source tool that helps you see and understand the costs of running Kubernetes clusters and cloud services in real time. It breaks down costs by cluster, node, namespace, pod, and more, across multiple cloud providers like AWS, Azure, and GCP, and even supports on-premises setups. This lets you track where your money is going, spot expensive resources, and manage your cloud spending better. It integrates with Prometheus for metrics and offers a user-friendly web interface and APIs for easy cost monitoring and exporting. Using OpenCost helps you control and optimize your cloud and Kubernetes expenses efficiently[1][2][3][4]. https://github.com/opencost/opencost

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@seeker_rc · Post #20227 · 05/11/2026, 06:25 AM

来个民科风暴:我要把这头上的 token 给他换成 DNA ai 说让我把这个实验写论文 投顶会,投图灵 我是这个时代新范式的引领者。 我要是刚 ai ,我肯定就信了。 下面开始介绍(吹) 先说问题 token embedding 有个我觉得很根本的毛病:它把"这个词是什么意思"和"推理过程中积累的上下文"塞进同一个向量里,然后每层都覆盖一遍。 残差连接解决了梯度消失,但解决不了这个问题——原始语义和推理状态混在一起,没有办法分开。你想知道"这个 token 原本是什么意思",在深层网络里做不到。 这不是调参能解决的。是结构问题。 我的假设 如果信息承载物本身有两个物理隔离的区域——一个永远不变,一个随推理动态演化——会怎样? 灵... via V2EX 分享创造 标签: #grade#token#Phase ⚡️探索号频道 ⚡️探索者频道 ⚡️探索者交流群 ⚡️ Youtube 频道:科技探索者 每天推荐有趣内容,欢迎订阅、转发。