I have been working on a new digital badge demo application that you can use to generate and then issue digital certificates. You have probably seen these in your LinkedIn newsfeed - those digital badges saying that you have completed this or that activity or training course. I have been using Kiro to create this demo application, leveraging the Strands Agent framework to make adding generative AI a trivial exercise (seriously, if you have not tried it yet do yourself a favour and check it out).

Over the Christmas period I spent some of my down time catching up on blog posts and open source projects that I had bookmarked to “read later”. I have been interested in learning about opinionated workflows that use agentic tools like Kiro CLI to generate better output. One of those projects looked at how you can improve the output from agentic tools through something called player/coach (I think it might be similar to the actor/critic method that I used back in the reinforcement learning days of Deep Racer). The basic gist of it is this: you generate output from one agent (the player), which is then assessed and reviewed by another (the coach). The coach provides feedback back to the player based on the output generated by the player, and thus a (hopefully) virtuous circle is formed.

I have spoken at many events this year sharing how I see developers using AI coding assistants like Kiro and Kiro CLI. The top use case was debugging code, and I wrote about that in Debugging and troubleshooting issues with AI coding assistants. In this post I am going to talk about another very common use case, automation. I will share a couple of things that I found trivial to automate with the help of Kiro, but also some unexpected things I learned along the way.

I am at Build Stuff doing my live coding talk, Zero to Shipped in 30 minutes. I have done this “talk” many times this year, and as I look back to January when I did the same session at PyCon+Web in Berlin, what amazes me is how far AI Coding Assistants have come in such a short period of time.

Back then I used Amazon Q Developer (pre agentic mode), and a lot has changed. At Build Stuff I did the same talk, but now I am using Kiro and Kiro CLI. After the talk I had a lot of folk who asked me to write down the good practices so I could share with them. Whilst we wait for the video to appear, this is that post. Whether you are at Build Stuff or not, you can follow along as I share how I was able to go from Zero to Shipped in 30 minutes. The idea is to build a finished working application, that is ready to ship to AWS. I have tried different applications over the last 11 months, but at Build Stuff I created a simple fact checking application - we need more trust in the world!

Like many folk who have been spending their time with AI Coding Assistants like Amazon Q Developer and Amazon Q CLI, understanding how to manage context is one of the key things you need to develop intuition for to improve the outputs these tools give you. More recently I have started hearing about new terms such as "context rot", and others exploring the field of context engineering. Understanding how to manage your context will be key to your success.

I have a long history with testing, and specifically load testing. Many many years ago, I spent many a happy hour designing, building, running, and then assessing load tests for the applications I was working with. It was one of the first technical interviews I ever did and it was also how I found AWS, spinning up Centos load runners to simulate thousands of virtual test users. Ahh the good old days!

I was chatting with AWS Hero Matt Lewis on the topic of how to run MCP Servers via a container image, and realised that I had not actually tried this yet. So this post was inspired by that conversation, and I hope it helps anyone else who is looking to try it out. In a previous post I introduced how Amazon Q CLI now supports Model Context Protocol (MCP) (check out Configuring Model Context Protocol (MCP) with Amazon Q CLI for more details).

Amazon Q CLI is a next generation developer tool that brings IDE-style autocomplete and agentic capabilities to your terminal. I have spent a lot of time recently writing about this amazing tool, and so was super excited by the news today that with the v1.9.x release, Amazon Q CLI now supports Model Context Protocol (MCP) for tools use.

What is Model Context Protocol (MCP)? if you have not heard about MCP (where have you been?) then check out my colleague’s post on this, Standardizing AI Tooling with Model Context Protocol (MCP)

I show you how you can use the Finch to run Apache Airflow using the mwaa-local-runner tool, and how you can do this for your applications too

As some of you may know, I have been creating content on Apache Airflow for a few years now. One of the open source projects that AWS has produced to make it easier for developers to get started with Apache Airflow, is mwaa-local-runner. If you have seen me at an event, it is likely you will have seen my live coding/demos, where I use this project. It is awesome!

The future is here folks! I share my experiences using Amazon Q in the IDE, and how I was able to write more quickly and with less friction.

Some of you might be familiar with the AWS open source newsletter, a weekly publication that features a curated set of content for open source developers on AWS. I rely on a number of folk to share with me interesting open source projects that I feature, but I wanted to see if I could create an automated script that I could run on a weekly schedule that would find new open source projects for me to review.