Chaotic Commits | Software, AI, and Uncomfortable Truths

I built a serverless AI dungeon master on AWS. Within twenty minutes, a player had broken it completely. Within forty minutes, a dragon had killed everyone at the table. Including the people who hadn't done anything wrong.This is the story of building a real AI agent system using Amazon Bedrock Agents, AWS Lambda, API Gateway, and DynamoDB; snd then watching what real users actually do with it.We go deep on the architecture: how Bedrock Agents handles tool orchestration and game state, why a three-second Lambda timeout will absolutely kill your AI inference workload, and what happens when an LLM gets an API contract that even AWS can't implement consistently.But the technical failures aren't the point. The point is what a Dungeons and Dragons campaign reveals about production AI systems: the gap between what you designed for and what users actually do. Hallucinations, tool misuse, unconstrained autonomous behavior, and the cost of building AI systems you can't observe.The same failure modes that made my friend's halfling rogue get eaten by a dragon show up in clinical decision support tools, customer service bots, and production AI agents. The sandbox is just where you can afford to find them first.If you're building agentic AI systems, or thinking about it, this one's for you.Dev.to Article: https://dev.to/aws-builders/building-a-serverless-dungeon-master-agent-on-aws-3j7kopics: AWS Bedrock, serverless AI, agentic AI architecture, LLM tool use, prompt engineering, AI observability, AWS Lambda, API Gateway, DynamoDB, production AI failures, responsible AI design

What does "senior software engineer" actually mean? Why isn't being the smartest person in the room enough to get there? In this episode, I break down the real ingredients of engineering seniority: technical depth, the judgment you can only build by being catastrophically wrong in production, and the career trap of staying comfortable in a pond that stopped teaching you things.Featuring a production incident when I was a mid-level engineer that ended up costing the company way more than my year's salary, and the moment the bosses laughed -- a reaction that quietly shaped everything that came after.This episode is for software engineers who have ever wondered if they've actually earned the title, engineering managers trying to calibrate what they're hiring for, and anyone who's shipped something that worked in staging and absolutely did not work in prod.Topics covered: senior engineer career growth, production incidents, engineering judgment, technical leadership, software engineering seniority, developer career advice, pair programming failures, and building experience as an engineer.If you're building a career in software and you want the honest version of what it takes, not the LinkedIn version, this one is for you.Hosted by Joanne Skiles: engineer, PhD, AWS Community Builder, Director of Engineering, professor, and someone who has definitely taken down prod.

It was 10:30 PM. The kids were asleep, the meeting summaries were done, the inbox was triaged... and the laundry was still on the floor. In the first episode of Chaotic Commits, I, a software engineer with a PhD and 15 years in tech, ask the question that lies hidden in that moment: Who is AI actually efficient for?From Amazon's infamous gender-biased recruiting algorithm to building AI tools for exhausted radiologists, this episode explores why the productivity revolution keeps skipping over entire categories of human labor, and why diverse teams aren't just a values issue; they're a design quality issue.If you've ever wondered why the best AI tools feel built for someone else's life, this episode is for you.Topics: AI bias, ethical AI, women in tech, inclusive design, transparent systems, diversity in engineering, workplace productivity, healthcare AI