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Articles by Dennis Traub
How to run Codex with GPT-5.6 on Amazon Bedrock
A few days ago, AWS released OpenAI's GPT-5.6 on Amazon Bedrock, and I'm going to show you how to configure the Codex CLI so you can use these models directly through the Bedrock API, with the credentials you already have. Depending on your configuration, Codex might already work with GPT models on Bedrock - out of the box.
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Your agent keeps using that word ...
"You keep using that word. I do not think it means what you think it means." — Inigo Montoya, The Princess Bride (1987) Two people using the same word while meaning completely different things has been a staple of comedy for centuries. Lewis Carroll built an entire scene around it in 1871: "When I use a word," Humpty Dumpty tells Alice, "it means just what I choose it to mean - neither more nor less." Now imagine what happens if you give Humpty Dumpty a code editor and 1M tokens of context...
AI isn't replacing junior devs. Your org chart is.
Two of this year's most-shared pieces on AI's effect on junior software engineers read as direct contradictions. In What about juniors?, Marc Brooker (VP, Distinguished Engineer at AWS) argues that junior developers have a structural advantage. Their accumulated heuristics haven't calcified into reflex yet, so they don't have to unlearn anything. The field, he says, is "more powerful than ever" for someone willing to expand their scope.
Rediscovering Domain-Driven Design, one MCP server at a time
A few days ago, a devops engineer posted on r/devops: "MCP servers just showed up in our infrastructure and I genuinely have no idea how to secure them, anyone been through this?" Filesystem access, shell permissions, database connectors - all callable by agents without human approval.
My 8 Agents Wrote Perfect Components - And Nothing Worked
TL;DR: Parallel AI agents don't coordinate on shared contracts, such as column names, URL paths, parameter formats, or identifiers. Extract those contracts into a single reference file before generation, and run a review agent that traces end-to-end data flows once the parallel agents are done. This single step fixed all 17 bugs in one pass. I launched 8 AI agents in parallel to build a full-stack app on AWS: infrastructure stacks, a React frontend, and a Java backend.
Missing from the MCP debate: Who holds the keys when 50 agents access 50 APIs?
There are two debates happening right now: CLI vs MCP - should agents call the existing CLIs or use an MCP server? And API vs MCP - does wrapping a REST API in an MCP server add value, or just complexity? Both focus on how agents call tools. What both aren't asking is, who holds the credentials when they do. Fifty agents, fifty sets of keys When one developer runs one agent on one laptop, credentials are simple. You store them locally, maybe rotate them, and move on.
3 Things I Wish I Knew Before Setting Up a UV Workspace
I love uv, it's so much better than pip, but I'm still learning the ins and outs. Today I was setting up a Python monorepo with uv workspaces and ran into a few issues, the fixes of which were trivial once I knew about them. 1. Give the Root a Distinct Name First, a virtual root (package = false) still needs a [project] name - and it can't match any member package.
TIL: How to Use Strands Agents' Built-In Session Persistence
Today I learned that the Strands Agents SDK has a built-in persistence layer for conversation history. Pass a SessionManager to the Agent constructor, and every message and state change is persisted automatically through lifecycle hooks. No manual save/load calls. The Code Save this as session_demo.py and run it with uv run session_demo.py. The # /// script block is PEP 723 inline metadata - uv run reads it to install dependencies automatically, no venv or pip needed.
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Why Your Chatbot is the BlackBerry of the 2020s
This is my third major technological shift, and every time I hear the same question echo through the C-Suites: "But where's the ROI?" In the 90s, we put our print brochures and yellow pages on the web - and called it digital transformation. In the 2000s, we put tiny keyboards or a Windows start button on a phone and called it mobile computing. Both times, the answer wasn't better brochures or smaller buttons. The answer was Google and Facebook. It was the iPhone and Android.
How a subtle MCP server bug almost cost me $230 a month
An important part of my job is to collect and distill feedback into recommendations for product and engineering teams. Sure, not quite as glamorous as traveling the world, but it's a lot of fun: I'm getting paid to experiment with brand new tech - and I get to directly influence the developer experience of our products. But - just like every job - there's also a lot of routine work involved, including the boring type.
Everything You Need to Know About AWS for Your First Developer Job
A real-world guide for day-one developers You just landed your first developer job! Congratulations! You’re excited, proud and already imagining what it’ll be like to finally build things for real, not just for personal projects and experimentation. Day one comes around, you open your new company laptop, join a few team channels, and begin receiving access to an AWS account, or maybe several.
In this walkthrough, I'll show you the simplest way to deploy an MCP server on AWS, using the Amazon Bedrock AgentCore Runtime - and how to call it with your IAM credentials - no complicated OAuth setup required. Note: This is a code-only walkthrough. For detailed explanations, see the full tutorial.
From Local MCP Server to AWS Deployment in Two Commands
“What's the simplest way to deploy an MCP server on AWS?” Last week a friend asked me this, and I realized: if you already have a local MCP server, it's really just two CLI commands - agentcore configure and agentcore launch. The entire workflow - from local development to production - takes just a few minutes. Let me show you how. To get started, we'll prepare a small example MCP server and test it locally to make sure it works.
We Need To Talk About AI Agent Architectures
AI agents are getting easier to build and host. With agentic frameworks and cloud-based hosting environments, you can deploy an agent to the cloud in an afternoon. It is now possible to assemble a multi-agent setup with memory, observability, and MCP connected tools without a huge amount of code or infrastructure work. This convenience, paired with AI coding assistants making it easier than ever to ship, has created a trend that is worth talking about.
Yay, I've been featured on the top 7 for the very first time! Thanks DEV ❤️
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Top 7 Featured DEV Posts of the Week
Welcome to this week's Top 7, where the DEV editorial team handpicks their favorite posts from the previous week. Congrats to all the authors that made it onto the list 👏 @wizsebastian details how they used Python and LLM vision capabilities to automate the transcription of 91 poems from Instagram screenshots. They walk through the entire process, from image processing to the final structured output, providing a practical example of AI-powered automation.
No OAuth Required: An MCP Client For AWS IAM
When Anthropic published the Model Context Protocol (MCP), I immediately started experimenting with deployment options on AWS: First, I tried running MCP servers as AWS Lambda functions. A great solution in terms of simplicity and cost, but it also meant I had to manually manage session state across invocations. Next, I deployed them as containers on Amazon ECS, which immediately solved session state management for me, but I had to pay for a running server, even when it sat idle.
Visualizing AI Agent Memory: Building a Web Browser for Amazon Bedrock AgentCore Memory
When building and testing multiple AI agent frameworks with Amazon Bedrock AgentCore, I realized I needed a tool to visualize and explore what my agents were actually remembering. AgentCore Memory provides powerful capabilities for managing both short-term conversation context and long-term knowledge extraction, but debugging memory patterns meant diving into AWS CLI commands or writing custom scripts just to see what was stored.
Building AI Agents with Strands: Part 2 - Tool Integration
Welcome to the second part of our series on building AI agents with Strands! In Part 1, we created a basic subject expert agent for our Integrated Learning Lab. Now, we'll take it to the next level by integrating tools to extend our agent's capabilities. Tools are what transform a basic conversational agent into a truly useful assistant that can interact with the world. In this tutorial, we'll explore both built-in tools provided by the Strands SDK, and learn how to create our own custom tools.
Building AI Agents with Strands: Part 3 - MCP Integration
Welcome to the third part of our series on building AI agents with Strands! In Part 1: Creating Your First Agent, we created a simple agent that acts as a computer science expert, and in Part 2: Tool Integration, we enhanced it with custom tools, like a glossary of terms and the ability to directly access the web. Now, we'll integrate the Model Context Protocol (MCP) to connect our agent with external specialized services.
Welcome to this hands-on tutorial series on building AI agents with the Strands Agents SDK! Artificial Intelligence is rapidly evolving, and AI Agents are at the forefront of this revolution. These intelligent systems can understand context, make decisions, use tools, and collaborate to perform complex tasks, moving beyond simple question-answering systems.
In this first tutorial, I'll walk you through creating a simple but functional AI agent using the Strands Agents SDK. We'll set up our development environment, install the necessary packages, and create a subject expert agent - the first component of our Integrated Learning Lab project.
Transcript ⚠ The following transcript was automatically generated. ❤ Help us out, Submit a pull-request to correct potential mistakes Dennis Traub 0:00 What I realized over the years really is, if you are in software, you will always be confronted with your own limitations, you will always be at the edge of what you know. Because if you're building software, you are doing something new, you are creating something that didn't exist before. That in itself brings failure. It's inherent to that process.
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