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feat(content): draft github copilot lessons articles
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content/1.posts/77.github-copilot-lessons-nuxt-blog.md

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@@ -17,9 +17,9 @@ Last December, I upgraded my Nuxt blog with the help of GitHub Copilot and I lea
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## Plan your changes
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Whether it's a technology upgrade, a complex feature to implement or a complete redesign, it's important to ask your AI assistant to plan the complex changes before implementing them. It does not matter if you use the [built-in Plan agent](https://code.visualstudio.com/docs/copilot/agents/planning), a custom agent or just a simple prompt, asking your AI assistant to plan the changes will help you identify the AI mistakes, see what could be improved, and challenge the assistant.
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Whether it's a technology upgrade, a complex feature to implement or a complete redesign, it's important to ask your AI assistant to plan the complex changes before implementing them. It does not matter if you use the [built-in Plan agent](https://code.visualstudio.com/docs/copilot/agents/planning), a custom agent or just a simple prompt, asking your AI assistant to plan the changes will help you identify the AI mistakes, see what could be improved, and challenge the assistant. Then, you can choose to let the assistant implement the plan, or implement it yourself with the help of the plan.
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For my blog upgrade, GitHub Copilot gave me a checklist with the differents seps of the process and it was very useful
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For my blog upgrade, GitHub Copilot gave me a checklist with the differents steps of the migration and it was very useful not to forget anything important and do things in the right order.
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## Specifications matter
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## Use the appropriate MCP Servers
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Today, everyone is talking about context engineering. And one way to improve the context of your AI assistant is to use the appropriate MCP servers.
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MCP servers can provide you the up-to-date documentation or code samples of the technologies you are using.
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My website is a Nuxt application using the Nuxt UI component library, so I have configured the Nuxt and Nuxt UI MCP servers for GitHub Copilot to have access to the latest documentation.
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```json [./vscode/mcp.json]
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{
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"servers": {
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"nuxt-ui": {
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"type": "http",
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"url": "https://ui.nuxt.com/mcp"
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},
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"nuxt": {
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"type": "http",
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"url": "https://nuxt.com/mcp"
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},
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"io.github.ChromeDevTools/chrome-devtools-mcp": {
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"type": "stdio",
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"command": "npx",
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"args": [
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"chrome-devtools-mcp@0.12.1"
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],
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"gallery": "https://api.mcp.github.com",
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"version": "0.12.1"
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}
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},
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"inputs": []
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}
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```
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As you can see, I also added the Chrome DevTools MCP server to be able to let GitHub Copilot interact with the website in local development from the browser.
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## Use Git Worktrees
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With git worktrees, you can checkout multiple branches at the same time in different directories. That's something I was using before to review pull requests on a worktree to avoid swithing branches on the directory I was working on. In the context of my migration it was useful to have the previous version of the website available in a worktree while working in another directory.
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However, when git worktrees really shine is when you want to parallelize work across multiple branches using multiple AI agents. Most of the AI assistants have built-in support for delegating tasks to agents in a specific worktree, so that every agent can work on its own branch without interfering with the others.
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![Image description](/posts/images/77.github-copilot-lessons-nuxt-blog_1.png){.rounded-lg .mx-auto}
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Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
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That's something I really recommend using especially to avoid context switching. When you are focused on a specific task, and you spot something to fix that is not related to your current task, you can just delegate it to a background agent that would work in a different worktree without interrupting your flow or messing with your file.
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```typescript [example.ts]
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// Sample code block
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const example = "Hello World";
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console.log(example);
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```
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## Try different LLMs
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## Another Section
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Models keep improving and different companies are producing competitive new models. Don't hesitate to try new models, to use different types of models (faster, more accurate, etc.) depending on the context or the task. If you are not satisfied with what has implemented you favorite LLM on a task, try to do the same task again with a different model to get better results.
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Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
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An interesting idea could be to use git worktrees to implement the same task with different models on different branches at the same time.
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### Subsection
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## WYPIWYG with an integrated browser
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::callout{icon="i-heroicons-light-bulb"}
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This is a callout for tips or important notes.
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::
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