The art of simplicity

In my previous post , I covered what Copilot Spaces are and how to set one up. Spaces live on github.com by default, but you don't have to leave your editor to use one. The GitHub MCP server exposes your Spaces as tools, so you can pull that curated context straight into VS Code. Prerequisites You'll need the remote GitHub MCP server configured for VS Code, and the copilot_spaces toolset explicitly enabled. It's not part of the default toolset. The easiest way is to install the GitHub extension in VS Code. This will also add the GitHub MCP server. But as I mentioned above, you will not find the copilot_spaces toolset out-of-the-box. You need to open up the MCP configuration and explicitly add it there. Add this to your .vscode/mcp.json (or your global MCP configuration): { "servers": { "github": { "type": "http", "url": "https://api.githubcopilot.com/mcp/", "headers"...

A lot of developers are using GitHub Copilot as their day-to-day agent harness. But most of them seem to be unaware that with a GitHub Copilot license comes more than a CLI and Visual Studio (Code) plugins. One thing you get access to are GitHub Copilot Spaces. GitHub Copilot what? Let me first explain what Spaces are and why they are useful. Why GitHub Copilot Spaces? If you've used Copilot Chat for a while, you recognize the pattern: you paste in the same background information for the third time this week. The same coding conventions, the same architecture decisions, the same "no, we don't use that library anymore" context. Close the conversation, and Copilot forgets all of it. Remark: Part of the answer can be found in building up your CONTEXT.md file and using the GitHub Memory feature but it’s not always the right solution. That's because source code lives in your repositories, requirements live in issues and pull requests, and team conventions o...

If you've built anything with the Microsoft Agent Framework (MAF), you'll notice that your tool calls can fill up the context window quite fast. A function that returns a list of 50 orders as JSON easily costs you a few hundred tokens on braces, quotes and repeated field names alone. That cost hits you twice: once on the way into the model as tool output, and again on every subsequent turn where that history gets replayed. That's where TOON (Token-Oriented Object Notation) comes in. Let's explore this. What TOON actually is TOON is a line-oriented, indentation-based encoding of the same data model JSON uses. It borrows YAML's indentation for nested objects and CSV's tabular layout for arrays of uniform objects. The trick is in that last part: if you have a list of objects that all share the same fields, TOON declares the field list once and then just streams the values, row by row, instead of repeating every key for every item. Take a small object like t...

There's a version of leadership that looks busy, looks needed, and looks like it's working but turns out to be the least effective version there is. This summer I read Bob Chapman's Everybody Matters , and one distinction from the book kept coming back to me weeks after I'd finished it: firefighting versus firelighting, as two fundamentally different modes of leadership.   Chapman's point is simple to state and harder to live by. A firefighting leader spends their energy reacting: jumping from one problem to the next, stepping in to rescue a team the moment something goes wrong. A firelighting leader does the opposite. They don't chase problems. They ignite something in people, so the problems get solved without the leader showing up with the extinguisher every time. In the book, the differences are listed like this: Firefighting: Reactive by nature . You wait for the smoke before you act. The leader becomes the bottleneck. Nothing moves unti...

Note: this is a follow-up to my post on monitoring GitHub Copilot Chat with the Aspire Dashboard. Same idea, different agent. Some developers on the team have switched (part-time) from Copilot Chat to Claude Code for their agentic work. Same question came up again: how much are we actually using it, what does it cost, and where does the time go in a session? Claude Code has its own OTel support, separate from Copilot's, so it needs its own setup — but it plugs into the exact same Aspire Dashboard we already had running. What Claude Code exports Claude Code emits three kinds of OTel signals: Metrics — session counts, token usage, cost, lines of code changed, active time Events — one event per prompt, per API call, per tool result, per permission decision Traces (beta) — a claude_code.interaction span per prompt, with claude_code.llm_request and claude_code.tool spans nested underneath Remark: traces are still beta. Metrics and events are the stable path ...

When talking to developers I get wildly different opinions about how "good" GitHub Copilot actually was for them. Some love it, some find it slow, some weren't sure it was doing anything useful at all(just kidding). The problem: nobody had any data. We were talking about token usage, latency and tool calls purely from gut feeling. GitHub Copilot can export all of that as OpenTelemetry traces, metrics and events. And the easiest way to look at it locally, without spinning up a cloud backend, is the Aspire Dashboard. Let's set that up. Why do we need this? Copilot isn't just autocomplete anymore. Every agent turn is a small orchestration: it calls a model, the model asks for tools, the tools run, the model answers. If you want to know where the time and the tokens go, you need to see that orchestration, not just the end result. That's exactly what the Open Telemetry integration gives you. Every agent interaction produces a span tree: invoke_agent copil...