More rules should mean better output. That's the intuition. I spent weeks building a comprehensive CLAUDE.md — 200 lines covering naming conventions, security rules, error handling, architectural patterns, import ordering, type safety requirements, and more. I was proud of it. I'd thought through every scenario. Then I scored the output. 79.0 / 100. My carefully crafted documentation was actively
I used two Amazon Bedrock AgentCore capabilities, Amazon Bedrock Registry for hybrid search over 10k+ Kiro resources, and AgentCore Harness for testing generated skills against a real agent, to build an AI-powered skill generator for Kiro Hub. Try it at kirohub.dev/generate. I've been building Kiro Hub for a few months now. The hub has over 10,000 community resources, including steering files, hoo
The Dangerous Bugs Are the Ones That Don't Crash: Building Input Validation for My MCP Server I was building an MCP server for an event platform that automates speaker communications (confirmations, reminders, calendar invites, follow-ups). An agent created a session confirmation for "Monday March 8th." March 8th was a Sunday. I caught it. But catching it was just the beginning. The confirmation
Have you ever looked at code you wrote six months ago and thought: "Who wrote this monster?"? Relax, it happens to all of us. In software engineering, writing code that a machine understands is the easy part. The real challenge is writing code that other humans (including your future self) can understand, maintain, and scale. This is exactly where Software Design Principles come into play. In this
Part 1 of 5 in The New Engineering Contract — what it means to lead engineers when AI is doing more of the coding. SWE-CI tested 18 AI models across 71 consecutive commits. Most broke something on commit 47 they'd already broken on commit 1. That's not an intelligence problem. That's a learning system that isn't learning. A paper made me uncomfortable this month. Not because of what it found about