adding a summary and FAQs

Author: anupras-mohapatra-arm

content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/4-optimize.md

@@ -81,7 +81,7 @@ The only remaining hotspot is `Mandelbrot::draw` itself at ~98.6% of samples, wh
 
 ## What you've accomplished
 
-You've now applied AI-suggested optimizations to the Mandelbrot application.
+You've now applied AI-suggested optimizations — such as replacing `std::complex<double>` with plain `double` arithmetic, enabling `-03` for compiler optimizations, and eliminating sqrt in the escape check — to the Mandelbrot application.
 
 The cumulative result, measured by profile sample counts, was a reduction from approximately 48,750 baseline samples to approximately 3,997 — a ~12x speedup — through three rounds of code changes, each validated by a re-profile before moving to the next.
 

content/learning-paths/servers-and-cloud-computing/performix-mcp-agent/_index.md

@@ -19,9 +19,57 @@ prerequisites:
     - Access to Arm Performix configured with the remote Arm target. See the [Arm Performix install guide](/install-guides/performix/) for setup instructions
     - Basic understanding of C++
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-07-07T16:22:08Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: a81d31a804debf71196a478bdf388c9e58b6dab67a9e881b916f3d0169d9555d
+  summary_generated_at: '2026-07-07T16:22:08Z'
+  summary_source_hash: a81d31a804debf71196a478bdf388c9e58b6dab67a9e881b916f3d0169d9555d
+  faq_generated_at: '2026-07-07T16:22:08Z'
+  faq_source_hash: a81d31a804debf71196a478bdf388c9e58b6dab67a9e881b916f3d0169d9555d
+  summary: >-
+    You'll combine the Arm MCP Server's `apx_recipe_run` tool with an AI agent to drive the complete Code Hotspots workflow to profile on Arm Neoverse. First, you'll build an intentionally unoptimized Mandelbrot
+    C++ application on a remote Arm Linux target, then use a GitHub Copilot prompt file to run
+    the Performix Code Hotspots recipe. The agent confirms target details, executes collection,
+    and returns a flame graph with structured hotspot data to pinpoint the hottest functions.
+    Guided by the agent, you'll apply concrete code changes, such as math simplifications and enabling
+    a higher optimization level, directly on the server over SSH. Then, you'll re-run the recipe to compare
+    results. The end-to-end flow keeps profiling, interpretation, and edits within a single AI-assisted
+    loop.
+  faqs:
+  - question: Which prompt file should I use to run the Code Hotspots recipe?
+    answer: >-
+      Use the `arm-hotspots-optimization` prompt file from the Arm MCP Server repository with GitHub
+      Copilot. It directs the agent to confirm the remote target, run the recipe, and return structured
+      profiling results.
+  - question: What result should I expect after the profiling run completes?
+    answer: >-
+      Expect a flame graph and a hotspot summary that highlights the hottest functions in the
+      Mandelbrot application. Use these outputs to guide which code changes to apply first.
+  - question: Should I compile the Mandelbrot example with optimizations before profiling?
+    answer: >-
+      No. The single-threaded, unoptimized build is intentional so the hotspot analysis produces
+      a clear signal. The agent later proposes enabling -O3 as part of the optimization pass.
+  - question: How do I know the agent is targeting the correct machine?
+    answer: >-
+      The agent explicitly confirms your remote target details before running the Code Hotspots
+      recipe. Review this confirmation and proceed only if it matches your intended Arm target.
+  - question: What should I check if the Code Hotspots run fails to start?
+    answer: >-
+      Confirm that Arm Performix is configured with your remote Arm target and that the MCP Server
+      can reach it over SSH. Also verify that the Mandelbrot application builds on the target
+      system.
+# END generated_summary_faq
+
 author: Pareena Verma
 
-generate_summary_faq: true
+generate_summary_faq: false
 rerun_summary: false
 rerun_faqs: false