Update repo name

Author: annietllnd

content/learning-paths/embedded-and-microcontrollers/device-connect-strands/background.md

@@ -24,9 +24,9 @@ A device is any process that registers itself on the mesh and exposes callable f
 
 **Agent layer**
 
-Two interfaces sit at this layer. The `device-connect-agent-tools` package exposes `discover_devices()` and `invoke_device()` as plain Python functions you can call directly from a script or REPL, with no LLM involved. The `robot_mesh` tool from `redacted` wraps the same capabilities as a Strands agent tool, which means an LLM can also call them during a reasoning loop. Both share the same underlying Device Connect transport, so anything you can do with one you can do with the other.
+Two interfaces sit at this layer. The `device-connect-agent-tools` package exposes `discover_devices()` and `invoke_device()` as plain Python functions you can call directly from a script or REPL, with no LLM involved. The `robot_mesh` tool from `robots` wraps the same capabilities as a Strands agent tool, which means an LLM can also call them during a reasoning loop. Both share the same underlying Device Connect transport, so anything you can do with one you can do with the other.
 
-TODO: replace redacted
+TODO: replace robots
 
 The diagram below shows how these layers communicate at runtime:
 
@@ -66,7 +66,7 @@ Once you have the flow working end to end, replacing `'mock'` with a real policy
 
 By working through the remaining sections you will:
 
-- Clone two repositories and install the Device Connect SDK, agent tools, and Strands robot runtime from source into a single virtual environment.
+- Clone the sample repository and install the Device Connect SDK, agent tools, and Strands robot runtime from source into a single virtual environment.
 - Start a simulated robot that registers itself on the local device mesh.
 - Discover and invoke the robot using `device-connect-agent-tools` directly.
 - Discover and command the robot through the `robot_mesh` Strands tool, including an emergency stop.

content/learning-paths/embedded-and-microcontrollers/device-connect-strands/run-example.md

@@ -32,7 +32,7 @@ You will need two terminal windows open at the same time: one to keep the simula
 Make sure you are in the repository directory and that your virtual environment is activated:
 
 ```bash
-cd ~/strands-device-connect/redacted
+cd ~/strands-device-connect/robots
 source .venv/bin/activate
 ```
 

content/learning-paths/embedded-and-microcontrollers/device-connect-strands/run-infra-example.md

@@ -82,7 +82,7 @@ Note the address returned — for the rest of this section it is referred to as
 
 ## Step 3 — Prepare the Raspberry Pi
 
-On the Raspberry Pi, follow the same repository and environment setup from the setup section of this Learning Path: install Python 3.12, clone the `redacted` repository, create the virtual environment, and install the packages with the same editable install commands.
+On the Raspberry Pi, follow the same repository and environment setup from the setup section of this Learning Path: install Python 3.12, clone the `robots` repository, create the virtual environment, and install the packages with the same editable install commands.
 
 Once the environment is ready, export the three variables that tell the SDK to route traffic through the Device Connect router on your host rather than using local network discovery:
 

content/learning-paths/embedded-and-microcontrollers/device-connect-strands/setup.md

@@ -15,27 +15,27 @@ python3.12 --version
 git --version
 ```
 
-These instructions are tested on Python 3.12. Earlier versions of Python 3 may work but are not validated against the `redacted-branch` branch used in this Learning Path.
+These instructions are tested on Python 3.12. Earlier versions of Python 3 may work but are not validated against the `feat/device-connect-integration-draft` branch used in this Learning Path.
 
 ## Clone the repository
 
-The code run in this Learning Path sits in the `redacted` repository. It contains the robot runtime and the `robot_mesh` Strands tool.
+The code run in this Learning Path sits in the `robots` repository. It contains the robot runtime and the `robot_mesh` Strands tool.
 
 ```bash
 mkdir ~/strands-device-connect
 cd strands-device-connect
-git clone https://github.com/redacted
+git clone https://github.com/atsyplikhin/robots.git
 ```
 
 ## Check out the integration branch
 
-The Device Connect integration code for `redacted` lives on the `redacted-branch` branch. This branch adds the `RobotDeviceDriver` adapter and the updated `robot_mesh` tool that routes calls through the Device Connect SDK rather than the raw Zenoh mesh.
+The Device Connect integration code for `robots` lives on the `feat/device-connect-integration-draft` branch. This branch adds the `RobotDeviceDriver` adapter and the updated `robot_mesh` tool that routes calls through the Device Connect SDK rather than the raw Zenoh mesh.
 
-TODO: remove feature branch? replace redacted and redacted-branch
+TODO: remove feature branch? replace robots and feat/device-connect-integration-draft
 
 ```bash
-cd ~/strands-device-connect/redacted
-git checkout redacted-branch
+cd ~/strands-device-connect/robots
+git checkout feat/device-connect-integration-draft
 cd ..
 ```
 
@@ -65,7 +65,7 @@ This means discovery works as long as the device process and the agent process a
 
 At this point you have:
 
-- Two repositories cloned with `redacted` on the `feat/device-connect-integration` branch.
+- `robots` cloned with the `feat/device-connect-integration` branch.
 - A single Python 3.12 virtual environment with the Device Connect SDK, agent tools, and robot simulation runtime all installed.
 
 The next section walks you through starting a simulated robot and invoking it from both the agent tools and the `robot_mesh` Strands tool.