Claude + Mobile via MCP: Giving the Model Hands on a Real Phone I plugged in a Pixel two months ago, ran one command in Claude Desktop, and watched it open Maps and start navigation to my home address from a single sentence prompt. It was the first time I'd ever seen a language model physically operate a phone. Latency was about two seconds per action; the part that surprised me was the third st
AI-Native Mobile Testing: What It Actually Means in 2026 The phrase "AI-native" has been thrown around in the testing space since 2019. Almost every tool calling itself that just bolts a language model on top of Appium and ships the same brittle XPath selectors with a new label. That's not AI-native testing. That's Appium with a chatbot. This post is about what AI-native actually has to mean to
The Missing Control Plane for Local AI Agents I sat with my Pixel for 20 minutes trying to get Claude Desktop to dictate a Slack message via accessibility. It was miserable. The model was capable. The transport wasn't. That gap — between an AI that can reason and an AI that can actually do — is what I've been working on with Drengr. This post is the version of the argument I'd give to anyone bui
The Problem Most engineers deploy to Kubernetes by clicking buttons in a UI. I built Archnet — a fully automated Internal Developer Platform What is an Internal Developer Platform? An IDP is the infrastructure layer that sits between your code How code gets deployed How secrets are managed How the system monitors itself How failures get detected and fixed Most companies pay Humanitec or Backsta
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