AWS

Everyone in financial services talks about resilience. We have DR plans, architecture diagrams, dashboards, and increasingly, tools like AWS Resilience Hub. On paper, it all looks good. In practice, most resilience programs don’t fail because of missing tooling — they fail because they never move beyond isolated assessments. A team runs a Resilience Hub assessment. They get a score. Maybe they even fix a few findings. And then nothing happens. No aggregation. No cadence. No program. Just a snapshot that lives in one account, attached to one application, reviewed once. ...

Richmond AWS User Group: An Evening of AI-Powered Development The Richmond AWS User Group hosted another successful meetup on March 5th, featuring a fascinating presentation on Kiro, AWS’s cutting-edge agentic coding tool. The event brought together local cloud enthusiasts, developers, and students for an evening of learning, networking, and pizza. Kiro: Revolutionizing Software Development Dinesh Balaaji Prabakaran from Amazon Web Services delivered an impressive talk on Kiro, showcasing how this innovative tool is transforming the software development landscape. Kiro represents a significant advancement in AI-assisted development, helping developers bridge the gap between ideas and implementation through intelligent code generation, problem reasoning, and accelerated development workflows. ...

I had the privilege of speaking at the Department of Computer Science at Virginia Commonwealth University’s College of Engineering on March 3rd, presenting “Cracking the Cloud: How AWS Certifications Can Launch Your Career” to a group of engaged Computer Science seniors. The Presentation My talk focused on how early-career technologists can differentiate themselves in an increasingly competitive field. Three themes anchored the session: strategic certifications — particularly AWS certifications as a way to validate cloud skills before you have the job title to back them up — self-directed projects that demonstrate practical problem-solving, and building genuine relationships in the industry. That last one always gets a laugh because I tell students it sometimes means getting away from the keyboard entirely and, as I like to say, “doing a little grass touching.” ...

Introduction On August 1, 2012, Knight Capital Group—one of the largest market makers on the New York Stock Exchange—lost $440 million in 45 minutes due to a software deployment failure. The incident nearly bankrupted the firm and sent shockwaves through financial markets. While the technical details are fascinating, the real lesson lies in what wasn’t there: an effective, centralized mechanism to stop runaway automation before catastrophic losses occurred. This post explores how modern streaming architectures using Apache Kafka and Apache Spark can implement the kind of real-time risk controls that regulations now require—and that Knight Capital desperately needed. We’ll connect the dots between a historic trading disaster, regulatory requirements, and a hands-on demo you can deploy yourself. ...

Managing AWS costs becomes increasingly complex as infrastructure grows. Organizations often struggle with cloud cost management, spending valuable engineering time manually analyzing Cost Explorer data, identifying optimization opportunities, and implementing changes. Even with dedicated cost management tools, the analysis and remediation process remains largely manual, requiring specialized expertise to interpret cost data and translate it into actionable steps. This post demonstrates how to build an automated agent that analyzes AWS costs and generates actionable recommendations to reduce cloud spend. By combining AWS Bedrock’s analytical capabilities with Cost Explorer data, the system identifies cost outliers and provides specific optimization steps that go beyond basic visualizations to deliver meaningful insights. ...

Code reviews are essential for maintaining code quality, but they can be time-consuming and often repetitive. Developers find themselves commenting on the same issues across multiple pull requests: missing tests, inconsistent naming, inadequate error handling, and numerous other routine concerns. This creates a bottleneck in the development process, as team members wait for their code to be reviewed while reviewers struggle to balance thorough reviews with their own development work. ...

“Where can I find our vacation policy?” “What’s the process for requesting new hardware?” “Can you explain our security guidelines?” These questions echo through company Slack channels daily, interrupting workflows and creating redundant work for team leads and HR staff. The same questions get asked repeatedly, and answers are buried in documentation that’s difficult to navigate. In this post, I’ll show you how to build a simple yet powerful Q&A bot for Slack that leverages your company’s documentation to provide accurate, contextual answers. The best part? It runs entirely on AWS managed services, minimizing operational overhead while delivering immediate value to your organization. ...

Live Demonstration of FastMCP on AWS The February meetup of the Richmond AWS User Group featured a hands-on demonstration of FastMCP on AWS, exploring how modern agent frameworks can be deployed and operated in real cloud environments. Rather than focusing on theoretical concepts, the session provided attendees with practical insights into what it actually takes to run AI agent frameworks in production. Behind the Scenes: Unscripted AI Engineering What made this demonstration particularly authentic was that I hadn’t tested the solution beforehand. Armed with an impressively detailed prompt I’d crafted (available on GitHub), I wanted to make this a genuinely live experience—including all the potential hiccups and surprises that come with real AI development. ...

The Real Problem: Production, Not Prototypes Everyone can demo generative AI. Almost no one can run it safely in production. Enterprises in finance, healthcare, and the public sector aren’t blocked by technology capabilities—they’re blocked by governance requirements that today’s AI implementations rarely satisfy. These organizations face three critical blockers: Data leakage risk: Sensitive information, from PII to trade secrets, flowing through public model APIs Lack of auditability: No reliable record of prompts, responses, or who accessed what information Unclear ownership: Ambiguous rights over prompt engineering IP, training data, and generated outputs AWS customers don’t want AI that behaves like a chatbot toy. They need AI that behaves like enterprise infrastructure: secured, monitored, audited, governed, and compliant with their existing security posture. ...

What is the Model Context Protocol? The Model Context Protocol (MCP) is an open standard for connecting AI agents to external systems. Think of it as a universal adapter that lets any AI agent talk to any tool or data source without custom integration code. Anthropic announced MCP in November 2024 and donated it to the Linux Foundation’s Agentic AI Foundation a month later. The adoption has been swift: OpenAI integrated it into ChatGPT, Google DeepMind uses it for Gemini agents, AWS built AgentCore around it, and development tools like Zed, Sourcegraph, Replit, and Codeium all support it. In just a few months, the community has built thousands of MCP servers. The protocol has become the de-facto standard for agent-to-tool communication. ...