Picture a warehouse stocked with every power tool ever made. Now picture the lights off, no labels, and no inventory list. That’s roughly where AI assistants are today. Modern AI clients like Claude, ChatGPT, Copilot, and Gemini aren’t limited to what the model knows anymore. They can plug into external tools, MCP servers, APIs, Skills, […]
Read More →More than 70% of CEOs say their organization’s IT operating model is not ready for AI, according to Gartner. The problem is not a lack of ambition — it is a structural mismatch. Traditional IT models were built for centralized control, predictable delivery cycles, and clear ownership inside the IT function. AI breaks that model apart. […]
Read More →Forward Deployed Engineer (FDE) is the most in-demand technical role in AI right now. Not because of the title, because of the problem it solves. AI research labs are shipping capabilities faster than enterprises can absorb them. The gap between what is technically possible and what is actually running in production is enormous, and closing […]
Read More →“Most people are using MCP wrong,” said MCP’s co-creator, David Soria Parra. I agree. Because I made the same mistake. Engineers would do the obvious thing: wrap each REST endpoint as an MCP tool and shove them into a custom registry. The result? Zero value over just calling the APIs directly. Worse than zero, honestly. […]
Read More →Large Language Models (LLMs) all predict text, but they differ a lot in how they follow instructions, use context, handle tools, and optimize for safety, speed, or cost. If you treat them as interchangeable, you’ll ship brittle prompts. If you treat them as different runtimes with different affordances, you’ll get reliable results. This post explains the major differences across […]
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