corrections

tutorial/content/labs/instrumentation/manual/logs/index.md

@@ -11,15 +11,11 @@ weight: 4
 * Solution directory: `labs/manual-instrumentation-logs/solution`
 * Source code: `labs/manual-instrumentation-logs/initial/src`
 * How to run the application either:
-  * Run the task for the application: `RRun manual-instrumentation-logs initial application` (runs the Python application)
+  * Run the task for the application: `Run manual-instrumentation-logs initial application` (runs the Python application)
   * Run the application with Terminal commands `python3 src/app.py` (runs the Python application)
 
 ---
 
-### Where to find the code
-You can find the code inside `manual-instrumentation-logs/initial`.
-You can run the application with the task `Run manual-instrumentation-logs initial application` or with `python3 manual-instrumentation-logs/initial/src/app.py`
-
 ### How did we get here
 
 Logs are one of the main pillars of observability and part of the MELT acronym (Logs, Metrics, Traces and Events) and is therefore incredibly important to determine the behavior and health of a system. Logs have the longest legacy among all the signals. For decades simple instructions were used to print debug output to files or stdout like `Sytem.out.println("Hello, World!")` in Java, `print('Hello, World!')` in Python or the `println!("Hello, World!")` Macro in Rust. Thereby Logs can represent any data you want. It could be structured data like `JSON`

tutorial/content/labs/instrumentation/manual/metrics/index.md

@@ -17,15 +17,11 @@ The metric data model in OpenTelemetry defines the structure of the data that is
 * Solution directory: `labs/manual-instrumentation-metrics/solution`
 * Source code: `labs/manual-instrumentation-metrics/initial/src`
 * How to run the application either:
-  * Run the task for the application: `RRun manual-instrumentation-metrics initial application` (runs the Python application)
+  * Run the task for the application: `Run manual-instrumentation-metrics initial application` (runs the Python application)
   * Run the application with Terminal commands `python3 src/app.py` (runs the Python application)
 
 ---
 
-### Where to find the code
-You can find the code inside `manual-instrumentation-metrics/initial`.
-You can run the application with the task `Run manual-instrumentation-metrics initial application` or with `python3 manual-instrumentation-metrics/initial/src/app.py`
-
 ### Let's start
 
 Regardless of your setup, open two separate terminals with a shell in the container.
@@ -636,7 +632,7 @@ If we pass `DropAggregation`, the SDK will ignore all measurements from the matc
 You have now seen some basic examples of how Views let us match instruments and customize the metrics stream.
 Feel free to add these code snippets to `create_views` and observe the changes in the output.
 
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@@ -668,7 +664,7 @@ Feel free to add these code snippets to `create_views` and observe the changes i
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 ### push and pull-based exporter
 
 So far, we have seen how the ConsoleMetricsExporter can be a useful tool when debugging output generated by the SDK.