
Writing about SQL, machine learning, generative AI, and Python — applied to three decades of supply chain work. Practical notes from the intersection of data, AI, and logistics.
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An Agent, a Process and a Canvas
My previous post gave a high level view of some of the features of the M-Foundry harness. This post makes the first group of them more real by actually using them: we’ll create an agent, choose what runs it— a local model or a frontier one — put it to work on a schedule and on a webhook, and end with its results arranged on a canvas that behaves like a dynamically updated living dashboard. One worked example runs through the whole post, and everything shown is a working feature running on my own instance today.
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