nits

Author: anupras-mohapatra-arm

content/learning-paths/servers-and-cloud-computing/performix-agentic-dynamic-insights-codex/open-in-performix.md

@@ -18,12 +18,12 @@ A chat response helps you triage a performance problem, but the underlying profi
 - **Disassembly** to inspect generated instructions and confirm claims about the compiled code.
 - **Compare** to evaluate equivalent before-and-after runs.
 
-Use the AI Insight to focus your investigation, not replace it. For example, if the insight says a loop appears scalar, inspect its disassembly and compiler flags before changing the implementation. If Code Hotspots only shows that the loop is hot, run Instruction Mix or another appropriate recipe before concluding that scalar execution is the cause.
+Use the AI insight to focus your investigation, not replace it. For example, if the insight says a loop appears scalar, inspect its disassembly and compiler flags before changing the implementation. If Code Hotspots only shows that the loop is hot, run Instruction Mix or another appropriate recipe before concluding that scalar execution is the cause.
 
 For a before-and-after comparison, keep the workload, target, input, thread count, CPU affinity, and collection settings consistent. A lower sample percentage doesn't prove that a function became faster if total runtime or the surrounding workload changed.
 
 ## What you've accomplished
 
-You've now configured the Performix MCP server, selected or created a Code Hotspots run, generated an AI Insight, and checked its recommendation against the profile. 
+You've now configured the Arm Performix MCP server, selected or created a Code Hotspots run, generated an AI Insight, and checked its recommendation against the profile. 
 
 You can continue with the [Arm Performix getting-started video](https://youtu.be/_eX8ZpNT0kc) to explore the graphical workflow.