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
We had ArgoCD running perfectly. Every deployment was reconciled from Git. Drift detection worked. Rollbacks were one-click. Our GitOps setup was clean. Developers still couldn't provision a staging environment without pinging the platform team. That gap — between "GitOps in place" and "developers can actually self-serve" — is where most platform engineering teams get stuck. GitOps solves a real p
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
Anthropic now ships at least three different memory models inside the Claude product family, and they don't behave the same way. Claude.ai has a chat memory feature for Pro, Max, Team, and Enterprise users that summarizes prior conversations and injects that summary into new chats. Claude Code has CLAUDE.md files plus a separate "auto memory" directory the model writes to itself, both loaded at se
Part 2 of 5 in The New Engineering Contract - what it means to lead engineers when AI is doing more of the coding. Stripe never skipped the boring stuff. They ship 1,300 AI PRs a week. Amazon skipped it. Their storefront went down for six hours. Kent Beck wrote the answer in Extreme Programming Explained in 1999. We read it. Then chose velocity anyway. A friend of mine leads engineering at a funde
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