Full recording of this week's State Change Mastermind livestream with Ray Deck and Dimitris Goudis. We talked about MCP connectors as the new interface for applications, turning experts' videos into knowledge-graph MCPs, and controlling a Mac from the Claude app. 00:00 Intro 01:11 Dimitris: MCP connectors are the new user interface (ClickUp to Airtable in one prompt) 02:52 Expert knowledge bases as public MCP connectors (Caleb Ralston, Rory Sutherland) 05:23 Live demo: adding the Rory Sutherland connector in Claude and asking it how to promote the livestream 16:33 How it was built: 3-4 hours, the MCP shaped around the database 19:34 Robert: notes from the MCP Dev Summit in Toronto (auth, governance, stateless and async) 22:35 Live demo: controlling my Mac from the Claude app by voice through the Snappy OS connector 28:33 How notifications get back to you (MCP tasks, the Claude app) 32:27 Does it only work on a Mac? 34:55 Experts and machines as connectors, and bespoke vs off-the-shelf MCPs 38:11 Getting things done vs gold-plating auth 41:00 Dimitris: lessons from designing the ontology for a custom knowledge graph 46:46 Wrap-up

Some great talks here at The Linux Foundation's Model Context Protocol Summit in Toronto, and great people as well! Great to see what people are working on too - MCP has proven to have staying power in a market that moves very quickly. It is at the heart of the client engagements I am doing right now, because it not only provides utility and value at multiple levels, but also has a great deployment story. If you're building with MCP, or thinking about it, feel free to DM or leave a comment 🙂

I've been using Jev from TypeSafe a lot lately and honestly I think it's a very powerful thing, but it really depends on how you use it. The way it works is basically like asking an LLM a question, except instead of getting text back you get a typed decision: a probability, a confidence score, one pick out of a set of options, or a score on a scale. That's the part that makes it powerful for me. You get typed judgment back instead of a paragraph you have to parse. A good example is linting prose. Say you want to check writing for something that depends on meaning, not just a banned word. Regex can't really do that. A regular LLM can, but it's slow and expensive, and who knows what the shape of the output is going to be. With Jev it's fast, cheap and accurate, and you always know exactly what you're getting back. That's just one way to use it, there are a bunch of others. If you want to try it out, TypeSafe has it and it's on OpenRouter too: https://typesafe.ai https://openrouter.ai/typesafe

Been working with software systems for more than 10 years, mostly around backend, cloud, AI, automation, and product development. One thing I’ve learned, knowing a programming language alone usually isn’t enough. The harder part is understanding the actual business. How to automate work that’s still manual. Connect systems that don’t work well together. Build AI into real workflows. Keep the backend stable when usage grows. Deal with data scattered across different tools. These are the kinds of problems I’ve spent a lot of time working on here in Japan. But I’m curious about the US side. For people running companies in the US, what technical skills are becoming difficult to find? And what skills do you think companies will need more of over the next few years?

I am an entrepreneur and a tech professional. Been working with software systems for quite a while, and one pattern comes up a lot. The first version works fine. Then customers grow, operations get bigger, and little technical problems start showing up everywhere. Too many manual steps. APIs that don’t talk properly. Slow backend. Data sitting in different places. AI added, but not really connected to the actual workflow. Usually not one huge problem. More like five small ones quietly costing time every day. For those running a company now, what’s becoming harder on the technical side as you grow?