Your blog deserves to know it's being read. Know exactly how many people are reading every post. This article shows you how to build a simple view counter that runs in your AWS account using Lambda, DynamoDB, and API Gateway. Start building ↓
AWS Developers
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Connecting developers to the resources, community, and technical guidance that turn ideas into reality with AWS.
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Connecting developers to the resources, community, and technical guidance that turn ideas into reality with AWS.
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What if the most valuable tuning on your EKS cluster was a single line of YAML you didn't know existed? AWS Community Builder, Darryl R. published a breakdown showing how adding "trafficDistribution: PreferSameZone" to Service manifests cut cross-AZ traffic on a multi-AZ EKS cluster by 50%. His article covers: • The three sources of cross-AZ waste (east-west traffic, load balancer routing, NAT Gateway placement) • Terraform code for optimized VPC setup • Zone-aware tooling to measure actual traffic patterns This single configuration change can translate to cost savings on production EKS workloads, especially for clusters running high-throughput services across multiple availability zones.
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What does it actually take to run AI in production? 1,200 developers spent two days at Spring I/O 2026 in Barcelona trying to answer that. Three themes kept coming up: Integrating AI capabilities directly into Spring applications Connecting AI systems to external tools and data sources Building multi-agent systems with Spring AI and Amazon Bedrock AgentCore Some of the best exchanges happened off stage. They happened in workshops, hallway conversations, and between developers who'd never met but were solving the same problems. That's community in practice. Check out the link in the comments to learn how to get involved with the AWS Community. Huge thanks to James Ward, Maximilian Schellhorn, Ruben Afonso, Yuriy Bezsonov, Arnaud Jean, Josh Long for the energy and the conversations that kept going long after the sessions ended, and to everyone who engaged, shared, and pushed the thinking forward.
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Get to your first working agent in minutes with these new features on Amazon Bedrock AgentCore. The managed agent harness in AgentCore lets you declare an agent and run it in just three steps, without writing orchestration code. You define what your agent does: which model it uses, which tools it can call, and what instructions it follows. AgentCore handles compute, tooling, memory, identity, and security. Want to try a different model or add a tool? It's a config change, not a code rewrite. You can test several variations of an agent in minutes by changing the API parameter on the fly. When you need custom orchestration logic, specialized routing, or multi-agent coordination, you switch from config to a code-defined harness. Same platform, same microVM isolation, same deployment pipeline. AgentCore persists session state to a durable filesystem, so agents can suspend mid-task and resume exactly where they left off. The AgentCore CLI keeps you in one workflow from prototype to production, all from the same terminal. You iterate on your agent locally, and when it's ready, you deploy it without switching tools or building a separate pipeline. You can get started in minutes then add more capabilities and control when your needs evolve, without any rearchitecture. Learn more in the comments.
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Developers who know how to work with AI productivity tools are shipping faster than ever. Flávio Pimenta showed what's possible at the AWS User Group Rio Grande do Sul meetup in Brazil. Starting from an empty directory, he built a fully functional event management app in minutes with FastAPI backend, Vue.js frontend, auth, CRUD, by working with Kiro. With back and forth conversation interations with Kiro, asking questions before writing anything shaped the app around what Flavio actually wanted. This is the difference between a code generator and an actual productivity tool. Watch Flavio's full session and read his blog post in the link in the comment.
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Here's what we bookmarked this month on Builder Center 📌 MCP is becoming the USB-C of AI tooling. One protocol for any tool and any agent. Jubin Soni walks you through how to build an agent and give it prod-ready tools using FastMCP & AgentCore. Here's the workflow: 1. Build an MCP server in Python with typed tools 2. Test locally with the MCP inspector 3. Deploy to AgentCore in two CLI commands The agent discovers your tools at runtime and orchestrates multi-tool calls on its own without hand-written orchestration logic needed. Find the link to the full tutorial in the comments.
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