Update learner working-dir path to ~/ai-workflow-course after rename (#73)

Co-authored-by: claude <claude@jpaul.io>
Co-committed-by: claude <claude@jpaul.io>

modules/20-mcp-servers-giving-the-ai-hands/README.md

@@ -255,7 +255,7 @@ is the one that lands the concept.
 >   global `pip install` is refused on purpose. The clean fix is a virtual environment per project:
 >
 >   ```bash
->   cd ~/workflow-course/tasks-app
+>   cd ~/ai-workflow-course/tasks-app
 >   python3 -m venv .venv                       # one-time
 >   source .venv/bin/activate                   # Windows: .venv\Scripts\activate
 >   python3 -m pip install "mcp[cli]"
@@ -266,7 +266,7 @@ is the one that lands the concept.
 > - **The install interpreter must match the config's launch command.** Your MCP client starts the
 >   server by running the `"command"` in its config — *not* your activated shell — so activating a
 >   venv does nothing to help the client find the SDK. You must point `"command"` at the venv's
->   **absolute** python path (e.g. `~/workflow-course/tasks-app/.venv/bin/python`, or
+>   **absolute** python path (e.g. `~/ai-workflow-course/tasks-app/.venv/bin/python`, or
 >   `...\.venv\Scripts\python.exe` on Windows). If they don't match, the server dies on `import mcp`
 >   and your tool just says "not connected" with no obvious reason — the exact failure this lab is
 >   about avoiding.
@@ -274,7 +274,7 @@ is the one that lands the concept.
 > Before wiring anything, verify with the *same* interpreter the config will launch:
 >
 > ```bash
-> ~/workflow-course/tasks-app/.venv/bin/python -c "import mcp; print('mcp ok')"
+> ~/ai-workflow-course/tasks-app/.venv/bin/python -c "import mcp; print('mcp ok')"
 > ```
 
 ### Part A — Connect an existing server (optional warm-up, ~10 min)
@@ -348,8 +348,8 @@ That's the entire client/server loop, end to end, with zero code you wrote. Now
 
    ```json
    "tasks": {
-     "command": "/ABSOLUTE/PATH/TO/workflow-course/tasks-app/.venv/bin/python",
-     "args": ["/ABSOLUTE/PATH/TO/workflow-course/tasks-app/tasks_mcp_server.py"]
+     "command": "/ABSOLUTE/PATH/TO/ai-workflow-course/tasks-app/.venv/bin/python",
+     "args": ["/ABSOLUTE/PATH/TO/ai-workflow-course/tasks-app/tasks_mcp_server.py"]
    }
    ```
 

modules/20-mcp-servers-giving-the-ai-hands/lab/mcp-config-example.json

@@ -2,8 +2,8 @@
   "_comment": "Common shape of an MCP server entry for a local (stdio) server. Many agentic tools accept this 'mcpServers' map; yours may use a different key or location (check its docs). IMPORTANT: 'command' must be the ABSOLUTE path to the python interpreter that has the MCP SDK installed (e.g. your venv's python) -- a bare 'python' makes the client launch whatever is on its PATH, which usually does NOT have the SDK, and the server then reports 'not connected'. On Windows the venv python is ...\\.venv\\Scripts\\python.exe. Set 'args' to the ABSOLUTE path to tasks_mcp_server.py in your tasks-app.",
   "mcpServers": {
     "tasks": {
-      "command": "/ABSOLUTE/PATH/TO/workflow-course/tasks-app/.venv/bin/python",
-      "args": ["/ABSOLUTE/PATH/TO/workflow-course/tasks-app/tasks_mcp_server.py"]
+      "command": "/ABSOLUTE/PATH/TO/ai-workflow-course/tasks-app/.venv/bin/python",
+      "args": ["/ABSOLUTE/PATH/TO/ai-workflow-course/tasks-app/tasks_mcp_server.py"]
     }
   }
 }