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TL;DR: We created Remote Execution and Computer Use APIs for macOS, packaged them as a MCP server, and now your AI assistant can spin up Virtual Machines (VMs), run shell commands, transfer files, and even click around the Graphic User Interface (GUI). It's a prototype. It's a bit wild. We'd love to know what you'd build with it.

Companies are rapidly adopting AI, but it's not all roses. The excitement comes with significant risks, such as shadow AI, runaway costs, and security nightmares. This post explores the real challenges organizations face in AI governance today and highlights how forward-thinking companies are beginning to tackle them. Companies are charging headfirst into AI, with research around agentic AI in the enterprise finding as many as 9 out of 10 organizations are actively working to adopt AI agents.

One such industry that has been on a growth trajectory since then is the Retail/ Shopping Industry. So, if you’re looking for a way to boost your sales and take your business to new heights, then one thing that should be on your radar is to create a shopping app. When eCommerce hit a jackpot in 2020, everyone wanted to be the next Amazon. So, it’s clear that you won’t succeed in this cutthroat market by creating a low-budget Amazon clone, rather you need something extraordinary.

"Too much information running through my brain." When The Police sang this opening line on their 1981 album Ghost in the Machine, they weren't thinking about artificial intelligence, but the sentiment perfectly captures the state of modern AI. The song warns of an overload of data that parallels how modern AI agents process extensive collections of messages and data.

Most AI analytics projects are failing to deliver on their promises, and the cause isn’t what you might expect. This creates widespread project failures and undermines confidence in AI-driven analytics. What are the problems with AI analytics and how can organizations address them?