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Connecting the Pieces: How AWS Services Form Cloud Architectures

2026-06-07T05:47:45+00:00

One of the biggest surprises I encountered while learning AWS was realizing that understanding individual services and understanding cloud architecture are not the same thing. Like many beginners, I started by learning services one at a time. EC2 was a virtual server. S3 was object storage. RDS was a managed database. Load Balancers distributed traffic. Each service had its own purpose, features, and use cases. At first, this approach made sense. Understanding individual services is an important part of building cloud knowledge. In fact, every cloud architecture is built upon these individual components. However, as I continued working through cloud [...]

Connecting the Pieces: How AWS Services Form Cloud Architectures2026-06-07T05:47:45+00:00

How to Deploy a Lovable Site to AWS Lambda Using the AWS Lambda Web Adapter

2026-06-04T00:58:48+00:00

There is a real satisfaction in taking something you built yourself and putting it live on infrastructure you own and control. This guide shows you how to deploy a Lovable site to AWS Lambda using the AWS Lambda Web Adapter, so a page you designed in an afternoon can run on your own AWS account instead of staying inside someone else's platform. Tools like Lovable make the front end easy, and the AWS Lambda Web Adapter is what lets that app run on serverless hosting that costs almost nothing until someone visits. The full path, from idea to a live [...]

How to Deploy a Lovable Site to AWS Lambda Using the AWS Lambda Web Adapter2026-06-04T00:58:48+00:00

AWS Data and AI Journey: Building Agentic AI Systems

2026-05-25T16:14:40+00:00

Stage 5 of the AWS Data and AI Journey: Building Agentic AI Systems Building agentic AI systems is the next frontier for organizations that have already laid the groundwork with a modern data foundation, governed pipelines, and enterprise-wide generative AI. This is where agentic AI enters the picture. Generative AI systems can answer questions, summarize documents, and generate content. Agentic AI goes further by enabling systems to reason through tasks, interact with tools, make decisions, coordinate workflows, and complete multi-step objectives with limited human intervention. Instead of acting only as assistants, AI systems begin operating more like autonomous digital workers [...]

AWS Data and AI Journey: Building Agentic AI Systems2026-05-25T16:14:40+00:00

Tech Debt in the AI Era

2026-04-02T17:30:43+00:00

Every software team knows the feeling. You ship fast to hit a deadline, planning to clean it up late, but "later" never comes. Soon, quick fixes pile up, and instead of saving time, you’re spending more than you planned. That’s technical debt in a nutshell — and it’s not going away anytime soon. What is changing is how we manage and deal with it. Modern systems are more distributed, interconnected, and increasingly powered by AI. In this environment, periodic clean-ups, manual code reviews, and occasional rewrites aren’t enough. Costs compound faster, dependencies deepen, and risks become harder to trace. So [...]

Tech Debt in the AI Era2026-04-02T17:30:43+00:00

Reserved Instance (RI) Reporting

2026-03-26T04:51:20+00:00

Reserved Instance (RI) Reporting Cheat Sheet AWS provides built‑in tools to help you understand and manage your Reserved Instances (RIs). You can visualize RI data at an aggregate level, inspect individual subscriptions, access the most detailed usage information, and set custom utilization targets with alerts.   Reserved Instance (RI) Reporting Utilization and Coverage  The RI Utilization and RI Coverage reports are available in AWS Cost Explorer. They let you see your RI data at an aggregate level or drill into a specific RI subscription. RI Utilization – How much of your purchased RI capacity you are actually using. RI Coverage – How much of your instance [...]

Reserved Instance (RI) Reporting2026-03-26T04:51:20+00:00

AWS Transform

2026-03-19T09:25:04+00:00

AWS Transform Cheat Sheet AWS Transform is an agentic AI service designed to accelerate enterprise modernization of full-stack Windows, mainframe, and VMware workloads, as well as custom transformations of code, APIs, and frameworks. Built on 20 years of AWS migration experience, it uses specialized AI agents to automate complex tasks such as assessments, code analysis, refactoring, decomposition, dependency mapping, validation, and transformation planning. The service enables teams to modernize hundreds of applications in parallel through a natural language chat experience and shared workspaces.   Key Benefits of AWS Transform Accelerate modernization – Modernize Windows, mainframe, and VMware applications up to 5x [...]

AWS Transform2026-03-19T09:25:04+00:00

Amazon Q Business Cheat Sheet

2026-03-11T14:43:37+00:00

Amazon Q Business is a fully managed enterprise AI assistant from AWS that helps employees interact with company knowledge using natural language. It allows users to ask questions, summarize documents, generate content, and automate routine workplace tasks based on internal data. Key characteristics: AI assistant designed for employees Uses enterprise data to generate responses Built on Amazon Bedrock Generates answers with citations from internal sources Helps automate common workplace tasks This service helps organizations improve productivity by making company knowledge easier to search and use.   Why Organizations Use Amazon Q Business? Large organizations store information across many systems, such [...]

Amazon Q Business Cheat Sheet2026-03-11T14:43:37+00:00

Amazon SageMaker Canvas Cheat Sheet

2026-03-11T14:42:33+00:00

Amazon SageMaker Canvas is a visual machine learning service that allows users to build, train, evaluate, and generate predictions from machine learning models without writing code. Instead of programming machine learning algorithms, users interact with a graphical interface that guides them through the ML process. SageMaker Canvas is part of Amazon SageMaker Studio, which enables collaboration between business users and data scientists. The main goal of SageMaker Canvas is to democratize machine learning, allowing users of different skill levels to create ML models. SageMaker Canvas is designed for users who want to apply machine learning but may not have programming [...]

Amazon SageMaker Canvas Cheat Sheet2026-03-11T14:42:33+00:00

Deploying an AWS Application Load Balancer (ALB) Using Terraform

2026-04-12T14:58:37+00:00

If you’ve tried building an ALB manually in the AWS Console, you already know the drill create a VPC, configure subnets, set up security groups, launch Amazon EC2 instances, create a target group, add listeners, and then double check everything because one small misconfiguration can break the whole setup. It works, but it’s time consuming and not something you want to repeat every time you need a fresh environment. This is exactly where Terraform shines. Instead of clicking through multiple AWS console pages, you define your infrastructure in code and let Terraform handle the provisioning. Need to rebuild the lab? [...]

Deploying an AWS Application Load Balancer (ALB) Using Terraform2026-04-12T14:58:37+00:00

Why I Chose DigitalOcean Over AWS for OpenClaw

2026-03-02T10:34:54+00:00

Why I chose DigitalOcean over AWS for OpenClaw comes down to one thing: matching infrastructure complexity to project stage. When building AI infrastructure in 2026, the default answer seems obvious: use Amazon Web Services (AWS). After all, it dominates the cloud market: It powers startups, governments, enterprises, and hundreds of other services. So when I started building a simple OpenClaw project, AWS seemed like the natural choice.  But I didn’t choose AWS. I chose DigitalOcean instead. Here’s why. What Is OpenClaw? Before explaining the infrastructure decision, it’s important to understand what I was deploying. OpenClaw (formerly known as Clawdbot or [...]

Why I Chose DigitalOcean Over AWS for OpenClaw2026-03-02T10:34:54+00:00

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