Kiro

Headlines · 3

1. 2h
   AWS playbook: six-week program turns non-engineers into AI agent builders

   In an AWS Machine Learning Blog post, Amazon Web Services describes a six-week program that pairs non-engineering business staff with mentors and production-grade tools—Kiro, Amazon Bedrock, Strands Agents SDK and AWS Lambda agents—for about four hours a week, ending in a working prototype. A team of four customer-facing employees with no engineering background built WealthWise, a multi-agent financial advisory prototype with five agents (portfolio analysis, risk assessment, financial planning and more), a dual-server Node.js and Python Flask architecture, four DynamoDB tables and, the team says, sub-5-second responses for complex reasoning; it took first place in the demo judging. AWS reports that self-rated “strong or expert” understanding of agentic AI rose from 27% to 82% among participants, and readiness to identify AI opportunities from 41% to 85%.

   AWS Machine Learning Blog · 🔥 9
2. 4h
   Diagram Design 2.5.10 adds ten more diagram layout grammars

   Diagram Design is a diagramming skill/plugin for agent hosts including Claude Code, Codex, GitHub Copilot, Factory Droid and Pi. It emits self-contained HTML + SVG with no build step, JavaScript or external image dependencies, and each visual type ships in minimal-light, minimal-dark and full-editorial variants. Creator Cathryn Lavery (LittleMight) added ten layout grammars in v2.5.10: Sankey, fishbone, Wardley map, kanban, user journey, deployment, dependency graph, UML class, story map and database schema. The skill can also read a website to match brand colors and redraw draw.io, Mermaid or Excalidraw sources at a chosen format, size and detail level; official builds come only from the repository.

   GitHub Trending（每日） · 🔥 12
3. 2d
   AWS adds SageMaker inference-optimization skill for coding agents

   AWS introduced the aws-ai-ml skill through the Agent Toolkit for AWS, giving MCP-compatible coding agents such as Kiro, Claude Code and Codex SageMaker AI inference optimization and benchmarking expertise. The skill load-tests existing endpoints and reports measured throughput, latency percentiles and concurrency, ranks instance types for models stored in S3, in SageMaker JumpStart or on Hugging Face Hub, compares two benchmark runs, and generates runnable SageMaker Python SDK v3 code. It can be installed locally via an npx command or used in a preconfigured image inside a private Amazon SageMaker Studio JupyterLab space.

   AWS Machine Learning Blog · 🔥 0

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