AI

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.

Part 3 in the “Agent Skills” series: Part 1 – What an agent skill is | Part 2 – Create your own skill | Version française

In the first article and the second one of this series, we saw what a skill is and how to create one. A skill gives instructions to the agent, but it doesn’t give it access to new data or new systems. For that, you need tools, and that’s the role of MCP.

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.

For one of my .NET projects, I use Supabase to store my data. Supabase is an open-source alternative to Firebase. It is based on PostgreSQL and provides a REST API and websockets to interact with the database.

To connect my .NET project to Supabase, I use Supabase Sharp and you can also find the documentation on the official Supabase website.

The ease of use of the library is really interesting and it also allows me to use the Supabase website directly to manage my data without having to write code to do it in my own applications.