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Source channel @githubtrending · Post #15076 · Aug 19

#python#aws#mcp#mcp_client#mcp_clients#mcp_host#mcp_server#mcp_servers#mcp_tools#modelcontextprotocol AWS MCP Servers use the Model Context Protocol (MCP), an open standard that connects AI tools with AWS data and services in a simple, secure way. These servers improve AI responses by providing up-to-date AWS documentation, best practices, and workflow automation for cloud development, infrastructure, and operations. You can run MCP servers locally for development or use AWS-managed remote servers for easy access and scalability. MCP servers support many AWS services like Lambda, DynamoDB, EKS, and more, helping you build, manage, and optimize AWS resources efficiently with AI assistance. Installation is easy with one-click options for popular tools like VS Code and Cursor. This makes cloud development faster, more accurate, and cost-effective. https://github.com/awslabs/mcp

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@githubtrending · Post #14846 · 06/20/2025, 12:00 PM

#go#cloudnative#grafana#hacktoberfest#logging#loki#prometheus Loki is a log aggregation system inspired by Prometheus but designed specifically for logs instead of metrics. It is cost-effective and easy to operate because it only indexes metadata (labels) about logs, not the full log content, which reduces storage and complexity. Loki works well with Kubernetes by automatically indexing pod labels and integrates natively with Grafana for easy log visualization. Its stack includes an agent (Alloy) to collect logs, Loki to store and query them, and Grafana to display them. This setup helps you efficiently manage and analyze logs with less cost and simpler operation compared to traditional logging systems[2]. https://github.com/grafana/loki