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
Power BI is a powerful business analytics service developed by Microsoft that empowers users to visualise data and share interactive dashboards across their organisation. While Power BI can handle data from various sources, its true potential is unleashed when connected to robust data sources like SQL databases. SQL databases—such as PostgreSQL, MySQL, and SQL Server—are the industry standard for
When you have 5 unrelated questions, should you pack them into one message to the LLM, or send 5 requests simultaneously? Which is faster? Splitting into multiple independent parallel requests is almost always faster. This isn't a gut feeling — it's determined by the underlying inference mechanism of LLMs. Let's walk through the reasoning from first principles. To understand this problem, you firs