updates

Author: madeline-underwood

content/learning-paths/cross-platform/adler32/more-11.md

@@ -8,11 +8,11 @@ layout: learningpathall
 
 ## What else can I do with GitHub Copilot on this project?
 
-You can investigate more topics using GitHub Copilot.
+GitHub Copilot can help you explore additional performance and optimization ideas:
 
-- Direct GitHub Copilot to try different compiler flags and use Agent mode to iterate through the options to find the best solution. 
-- Add support for the Clang compiler to the Makefile and compare the results to GCC. Depending on the application code, changing the compiler can result in improved performance.
-- Use GitHub Copilot to generate different data sizes and random data patterns to further investigate correct functionality and performance.
-- Try different algorithm implementations that use compiler autovectorization instead of NEON intrinsics or break down the Adler32 checksum into smaller blocks of data. It may be possible to get even better performance without NEON using the compiler and a better structure for the C code.
+- Test different compiler flags using Agent mode to automate iteration and identify the best combinations.
+- Add Clang support to your Makefile and compare performance against GCC — performance can differ significantly depending on your code structure.
+- Generate a wider range of data sizes and random patterns to stress-test functionality and measure performance under varied conditions.
+- Explore alternative algorithm structures that rely on compiler autovectorization instead of NEON intrinsics — you might discover better performance simply by restructuring the C code.
 
-While AI tools do not create performance code for every programming problem, they can be a big help to get you started in a new area of programming such as performance optimization using NEON intrinsics.
+AI tools won’t always generate high-performance code out of the box, but they can rapidly accelerate your experimentation and learning — especially in new areas of programming like NEON-based performance optimization.

content/learning-paths/cross-platform/adler32/summary-10.md

@@ -10,7 +10,9 @@ layout: learningpathall
 
 You can use GitHub Copilot to generate a project summary in a README file. 
 
-Copy the prompt below to your GitHub Copilot Agent chat and review the created README file.
+Use the prompt below to collaborate with GitHub Copilot Agent to generate your README. 
+
+Review and refine the results to align them with your project's goals.
 
 ```console
 Review the files in my project.
@@ -20,7 +22,7 @@ Add a note that the performance results recorded on the Neoverse N1 processor.
 Use a table to compare the original version and the NEON version and show the performance improvement factor.
 ```
 
-Below is the created README.md file. The formatting doesn't match the Learning Path template exactly, but you can copy the the README file to a new repository in GitHub for improved results. 
+Below is the created README.md file. The formatting doesn't match the Learning Path template exactly, but you can copy the README file to a new repository in GitHub for improved results. 
 
 ## Adler-32 Checksum Implementation Comparison
 
@@ -93,6 +95,15 @@ The table summarizes the speedup obtained by the NEON version.
 
 Using Agent mode in GitHub Copilot is a significant benefit when you are actively building and running software. Agent mode can create files and modify them to make needed improvements. 
 
-The entire project was done without modifying any of the generated files. While you may not need to do this on a real project, the concept of writing NEON intrinsics to improve performance was demonstrated. You can also use GitHub Copilot to fix issues in NEON code that are difficult to debug for developers who are not experts. 
+### Tips for Using GitHub Copilot Effectively
+
+This project was completed using GitHub Copilot Agent without modifying the generated files. While that might not be practical in every case, the demonstration shows how NEON intrinsics can significantly boost performance. 
+
+GitHub Copilot is especially useful for:
+* Generating vectorized versions of scalar code.
+* Writing and adapting NEON intrinsics.
+* Identifying and fixing bugs in complex low-level code, even for developers who aren’t SIMD experts.
 
 Make sure to try different LLMs with Copilot as the results will vary greatly depending on the model.
+
+