Most AI content stops at the prototype. The posts in this collection are about what comes after — building AI tools that are reliable, affordable and safe enough to put in front of real users. A lot of this work centres on the Model Context Protocol (MCP), which I think is one of the most important shifts in how we connect models to real data and tools.

You will find end-to-end builds of MCP servers, deep dives on Amazon Bedrock and RAG, and hard-won lessons on cutting token costs, controlling model output, and keeping AI systems secure. I write from a builder’s seat: every guide here comes from something I actually shipped, including where it went wrong and what I would do differently next time.

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