Boost AWS Cert Prep with SAM Kiro Power

🚀 AWS SAM Kiro Power: Accelerate Your Serverless Development with AI Exciting news for serverless developers! AWS just launched the AWS Serverless Application Model (SAM) Kiro Power, bringing AI-assisted serverless development directly to your local environment. 🎯 What's New: The SAM Kiro Power integrates serverless application development expertise into Kiro's agentic AI development environment, enabling you to build, deploy, and manage serverless applications with intelligent AI assistance. ⚡ Key Capabilities: - One-click installation from Kiro IDE and Kiro Powers page - Initialize SAM projects with AI guidance - Build and deploy applications to AWS seamlessly - Locally test Lambda functions before deployment - Support for event-driven patterns with Amazon EventBridge, MSK, Kinesis, DynamoDB Streams, and SQS 🔒 Built-in Best Practices: - Enforces SAM resources usage from the start - Integrates AWS Lambda Powertools for observability and structured logging - Includes security best practices for IAM policies 💡 Perfect For: Whether you're building static websites with API backends, event-driven microservices, or full-stack applications, SAM Kiro Power accelerates your journey from concept to production with AI-powered guidance. 🚀 Get Started: Available now with one-click installation! Explore the power on GitHub or check the SAM developer guide to learn more. Ready to supercharge your serverless development workflow? What serverless patterns are you most excited to build with AI assistance? 🔗 Read more: https://lnkd.in/dwhuGHyZ #AWS #Serverless #SAM #Lambda #DevOps #AI #Kiro #CloudDevelopment #EventDriven #Microservices #CloudNative #AWSLambda

🚀 Scaling Code Modernization with Agentic AI & AWS Transform custom 🚀 Software modernization is one of the toughest challenges for large engineering organizations — especially when you’re managing hundreds or thousands of repositories. Manual upgrades, refactoring, and framework migrations can drain developer time and balloon technical debt. That’s why I’m excited about the new open-source solution detailed by AWS that helps teams modernize at enterprise scale using AWS Transform custom. ✨ What this offers: • 🧠 Agentic AI transformations — AWS Transform custom uses AI to automate large-scale modernization tasks like language upgrades, API migrations, refactoring, and custom code transformations. • ⚙️ Parallel execution at scale — Leverages AWS Batch + AWS Fargate to process thousands of repositories concurrently. • 🔐 Secure, reliable orchestration — REST API with IAM authentication allows programmatic control, queuing, and monitoring across your code estate. • 📊 Comprehensive monitoring — CloudWatch dashboards surface success rates, logs, and operational health so you can track modernization progress. This means less time rewiring legacy systems and more time delivering business value — crucial for organizations racing to reduce technical debt and accelerate innovation. 💡 Whether you’re upgrading runtimes, refactoring frameworks, or modernizing APIs, this approach scales modernization without adding manual toil. Read me about: https://lnkd.in/g-MaVRkh #AWS #DevOps #AI #CodeModernization #CloudComputing #aipilotasaservice #SoftwareEngineering #virtualanai #virtualan #ai_pilot_as_a_service

A few years ago, building a serverless app on AWS meant jumping between docs, templates, CLI commands, and StackOverflow threads. You’d write some code. Search the docs. Fix the IAM policy. Search again. Deploy. Debug. Repeat. It worked, but it was rarely smooth. Now something interesting is happening. AWS just introduced SAM Kiro Power, which brings deep knowledge of the AWS Serverless Application Model (SAM) directly into the Kiro AI development environment. Instead of an AI assistant that guesses, it now understands the full serverless workflow. Imagine asking: “Create a serverless API with Lambda, API Gateway, and DynamoDB.” And the assistant doesn’t just write a function. It: • generates the SAM template • structures the project • configures permissions • sets up local testing • prepares deployment All following AWS best practices. The real shift here isn’t just faster code generation. It’s AI assistants evolving from autocomplete tools into domain-aware engineering partners. Of course, tools like this don’t replace experience. They amplify it. You still need the judgment to guide the system, review the architecture, and make the right decisions. Less time fighting infrastructure. More time building. Serverless development might finally feel as simple as it was always supposed to be. Curious to see where this goes next. https://lnkd.in/ePsebqrm #AWS #Serverless #AI #DeveloperTools #CloudComputing

Here's my favorite framework for getting started with serverless development on AWS. Over the past few years, I've helped dozens of engineers transition to cloud roles, and serverless is always the biggest hurdle. The tool that makes this transition seamless is AWS SAM (Serverless Application Model). AWS SAM is an open-source framework that removes the complexity from building, testing, and deploying serverless applications. Here's why it's become my go-to recommendation: 🎯 Focus on Code, Not Infrastructure Serverless computing means AWS manages the infrastructure while you focus on writing code. SAM takes this further with simplified syntax for defining Lambda functions, APIs, and other resources. No more wrestling with complex CloudFormation templates. 🔥 Local-First Development This is where SAM truly shines: • Build and package with one command: sam build • Run a local API Gateway before touching AWS • Test locally using Docker to emulate Lambda • Catch issues on your laptop, not in production The Implementation Process: 🛠️ 1️⃣ Prerequisites: AWS account, AWS CLI, SAM CLI, Python, Docker 2️⃣ Initialize: Use built-in templates to start your project 3️⃣ Build: Package your code and dependencies automatically 4️⃣ Test: Invoke functions locally or run full API simulations 5️⃣ Deploy: sam deploy --guided walks you through everything 6️⃣ Clean up: sam delete removes all resources when you're done The real breakthrough? You're developing with production-ready best practices from day one. The "Hello World" template gets you started, then you extend it with more AWS services using the same workflow. Rapid iteration, minimal friction, zero surprise AWS bills. Have you tried AWS SAM for your serverless projects? Drop your experience below.

I built a self-healing AWS architecture. Then AWS shipped the same thing at re:Invent. Here's what I learned from the comparison. AWS DevOps Agent (in preview, us-east-1) is AWS's own frontier AI agent for incident response — announced at re:Invent 2025, already handling real incidents internally at Amazon with an 86% root cause accuracy rate. And the team at AWS published enough of the internals to make it interesting. Under the hood it's a multi-agent architecture. A lead agent acts as incident commander — it understands the symptom, builds an investigation plan, then dispatches specialised sub-agents in parallel, each working with a clean context window. Sub-agents return compressed findings. The lead agent synthesises and produces the root cause. It also continuously builds a topology graph of your resources, their IAM relationships, deployments, and log groups — that graph is the context for every investigation. What's notably not published is the internal routing layer — how an alarm gets triaged and handed off to the right investigation path. That architecture isn't documented. What I've built in this carousel is my attempt to reconstruct that same pattern from composable AWS primitives: → EventBridge does the routing (Path A / B / C based on alarm type) → Bedrock Agent does the reasoning over logs, config history, and metrics → Sub-agents are Lambda functions scoped per remediation type → IAM enforces the blast radius per cluster Autonomous remediation is on their roadmap but not live yet. What I've built goes one step further — the agent can execute a config revert when confidence is high enough, with strict guardrails. The full repo is available and will be open to the public next week. Terraform, Lambda, IAM policies, Bedrock Agent prompts, OpenAPI schemas. All open source, free. If you want early access, comment "REPO" and I'll DM you the GitHub link. 👉

I recently delegated an entire AWS migration to an AI agent without losing control. The approach that worked: • Detailed migration plan with inline annotations • Verification steps using AWS CLI to validate Terraform changes • Interactive shell requiring approval for every command The agent handled repetitive work while I focused on reviewing. Debugging became nearly automated as errors fed back into the agent's context. But this only works if you know what you're doing. Without solid Terraform and AWS knowledge, approving those commands would be risky. Agents don't replace technical expertise. They eliminate the boring parts. Full breakdown: https://lnkd.in/d9ngsGGb

I recently posted about AI hype posts and how LinkedIn is flooded with them. This is not one of those. Let’s see more realistic, down-to-earth examples of successful AI workflows please. Thank you Tadej Stanič, hoping you start a trend!

Are you facing challenges with AI coding assistants that generate Lambda functions lacking observability, overlooking event source best practices, or producing Infrastructure as Code (IaC) that fails in production? The Agent Plugin for AWS Serverless offers a solution by embedding production-grade guidance directly into Claude Code, Cursor, and Kiro. It dynamically loads expertise for SAM/CDK patterns, EventBridge and Step Functions integrations, and Lambda durable functions with checkpoint-replay for stateful workflows. https://lnkd.in/gn8T-PMf This approach minimizes the blast radius from AI-generated misconfigurations and reduces total cost of ownership (TCO) on rework cycles. It is built on the open Agent Skills format for enhanced portability. What percentage of your AI-assisted serverless code requires significant refactoring before production? #AWS #Serverless #DevOps #SolutionsArchitecture #IaC

Leveling up my First Cloud AI Journey with Amazon Q & MCP! ☁️🎨 Being part of the FCAJ bootcamp has been an incredible deep dive into AWS services like VPCs, EC2, and S3. However, as a beginner, I’ve always looked for ways to better visualize the complex architectures we’re building. Sometimes, moving from a conceptual mental model to a polished, professional diagram can be time-consuming. I wanted a way to make my documentation as "clean" as the infrastructure-as-code we're learning to write. Luckily, a friend shared a brilliant AWS Blog post about using Amazon Q Developer CLI and the Model Context Protocol (MCP) to automate this process. It’s the perfect companion for anyone starting their cloud journey! 🔹 Bridging Concept to Visual: It helps translate CLI commands and technical descriptions into structured diagrams instantly, making complex setups much easier to understand. 🔹 Reinforcing Best Practices: By seeing how Amazon Q automatically organizes components, you can learn the "standard" AWS design patterns used by professionals in the industry. 🔹 Focus on Logic, Not Just Icons: Instead of spending hours dragging and dropping shapes, you can focus more on the architectural logic and let AI handle the heavy lifting of the layout. If you’re also exploring AWS and want to take your project documentation to the next level, this is a must-read! 👉 Check out the full guide here: https://lnkd.in/gkXZhFmU Let’s keep building and growing! 🚀

🚀 From Code to Cloud: Automating Node.js Deployment on Azure In modern development, deploying an application isn’t just about “making it work” — it’s about building a reliable, repeatable, and scalable delivery pipeline. Recently, I implemented an end-to-end CI/CD workflow to deploy a Node.js + MongoDB application to Azure App Service using Azure DevOps. 💡 What this project demonstrates Instead of manual deployments, the entire process is automated: 🔹 Source Code → GitHub / Azure Repos 🔹 CI Pipeline → Build, package, and publish artifacts 🔹 CD Pipeline → Deploy directly to Azure App Service 🔹 Database → MongoDB integration for real-world data handling ⚙️ Workflow Breakdown ✔️ Installed Node.js (20 LTS) in pipeline ✔️ Executed dependency installation (npm install) ✔️ Packaged application into deployable artifact ✔️ Published build artifacts for release stage ✔️ Deployed to Azure Web App using Azure RM Service Connection ✔️ Automated trigger on every push to main branch 🌍 Real-World Relevance This is not just a demo pipeline — it reflects how production-grade applications are deployed today: ✅ Eliminates manual errors ✅ Enables faster and consistent releases ✅ Supports team collaboration and scalability ✅ Aligns with DevOps best practices used in enterprises 📈 Why this matters In real-world environments, applications are constantly evolving. A strong CI/CD pipeline ensures that: ➡️ New features reach users faster ➡️ Bugs are fixed and deployed quickly ➡️ Systems remain stable and predictable 💬 This project helped reinforce how automation + cloud + DevOps come together to deliver real business value. #Azure #DevOps #NodeJS #MongoDB #AzureDevOps #CloudComputing #CICD #SoftwareEngineering #AppService #CloudProjects