How I built arbitres.ca with Claude and Copilot

Part 2 in the “Behind arbitres.ca” series: Part 1 – Scheduling umpires without Excel | Version française

In the first article, I introduced arbitres.ca and its stack. Here I move on to the method. At the end of July, the repository had 280 commits, 68 specs and 9 skills, built up over about two and a half months of evenings.

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arbitres.ca: scheduling baseball umpires without Excel

Part 1 in the “Behind arbitres.ca” series: Part 2 – How I built it with AI | Version française

Someone close to me umpires in minor league baseball. That’s how I saw the way associations handle their umpire assignments, often with an Excel file and a bunch of text messages. I also wanted a project big enough to really test Blazor and Supabase, and the way I work with AI.

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Orchestrating Ollama and Blazor with .NET Aspire

Part 5 in the “Local AI with Ollama and .NET” series: Part 1 – Ollama and .NET | Part 2 – Local RAG | Part 3 – AI Agents | Part 3.5 – MCP Server | Part 4 – Microsoft Agent Framework | Version française

Through the rest of the series, Ollama ran by hand: installed locally on the machine, the ollama pull done manually, and the .NET app pointing at Ollama’s local address hardcoded. It works, but it’s several manual steps to repeat on every machine. Aspire takes care of this configuration. I wanted to see what it changes.

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Create a Blazor App with ML.NET

Learn how to integrate ML.NET into a Blazor Server app to build a sentiment analysis tool using binary classification. [Read More]
.NET  C#  Blazor  ML.NET  AI