diff --git a/docs/14.gprmax/1.tutorials/2.scaling-with-mpi.md b/docs/14.gprmax/1.tutorials/2.scaling-with-mpi.md
index 2936eb6..882d58c 100644
--- a/docs/14.gprmax/1.tutorials/2.scaling-with-mpi.md
+++ b/docs/14.gprmax/1.tutorials/2.scaling-with-mpi.md
@@ -74,7 +74,7 @@ Next, we will explore MPI-based parallel execution, which will significantly spe
 ## MPI Processing
 gprMax supports MPI, allowing each of the 54 runs to be executed **in parallel**. This requires a machine with enough vCPUs to support all the runs. Hence, we'll be running the case on a `c2d-highcpu-112`, which has 112 vCPUs, providing ample resources for all 54 simulations to run concurrently.
 
-> ⚠️ **Note on vCPUs and Hyperthreading**: In most cloud environments (e.g., Google Cloud), a vCPU represents a single thread rather than a full physical core. By default, Google Cloud VMs provide 2 vCPUs per physical core, so a `c2d-standard-112` machine with 112 vCPUs typically has 56 physical cores with hyperthreading enabled.
+> ⚠️ **Note on vCPUs and Hyperthreading**: In most cloud environments (e.g., Google Cloud), a vCPU represents a single thread rather than a full physical core. By default, Google Cloud VMs provide 2 vCPUs per physical core, so a `c2d-highcpu-112` machine with 112 vCPUs typically has 56 physical cores with hyperthreading enabled.
 
 Wrap the Python command with `mpirun` as follows:
 
diff --git a/docs/31.swmm/1.tutorials/1.quick-start.md b/docs/31.swmm/1.tutorials/1.quick-start.md
index 5a9db1d..0fa29e9 100644
--- a/docs/31.swmm/1.tutorials/1.quick-start.md
+++ b/docs/31.swmm/1.tutorials/1.quick-start.md
@@ -52,7 +52,7 @@ task.download_outputs()
 task.print_summary()
 ```
 
-In this basic example, we're using a cloud machine (`c2d-highcpu-2`) equipped with 2 virtual CPUs.Since SWMM runs in a single thread, increasing the number of vCPUs won’t speed up a single simulation. You may consider switching machine families via the `machine_type` parameter, as newer CPU generations can improve single-core performance. You can explore the full range of available machines [here](https://console.inductiva.ai/machine-groups/instance-types).
+In this basic example, we're using a cloud machine (`c2d-highcpu-2`) equipped with 2 virtual CPUs. Since SWMM runs in a single thread, increasing the number of vCPUs won’t speed up a single simulation. You may consider switching machine families via the `machine_type` parameter, as newer CPU generations can improve single-core performance. Explore the full range of available machines [here](https://console.inductiva.ai/machine-groups/instance-types).
 
 > **Note**: Setting `spot=True` enables the use of [spot machines](/guides/machines/spot-machines), which are available at substantial discounts.
 > However, your simulation may be interrupted if the cloud provider reclaims the machine.